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# Summarization_General_Lib.py
#########################################
# General Summarization Library
# This library is used to perform summarization.
#
####
####################
# Function List
#
# 1. extract_text_from_segments(segments: List[Dict]) -> str
# 2. summarize_with_openai(api_key, file_path, custom_prompt_arg)
# 3. summarize_with_anthropic(api_key, file_path, model, custom_prompt_arg, max_retries=3, retry_delay=5)
# 4. summarize_with_cohere(api_key, file_path, model, custom_prompt_arg)
# 5. summarize_with_groq(api_key, file_path, model, custom_prompt_arg)
#
#
####################
# Import necessary libraries
import json
import logging
import os
import time
from typing import Optional
import requests
from requests import RequestException
from App_Function_Libraries.Audio.Audio_Transcription_Lib import convert_to_wav, speech_to_text
from App_Function_Libraries.Chunk_Lib import semantic_chunking, rolling_summarize, recursive_summarize_chunks, \
improved_chunking_process
from App_Function_Libraries.Audio.Diarization_Lib import combine_transcription_and_diarization
from App_Function_Libraries.Summarization.Local_Summarization_Lib import summarize_with_llama, summarize_with_kobold, \
summarize_with_oobabooga, summarize_with_tabbyapi, summarize_with_vllm, summarize_with_local_llm, \
summarize_with_ollama, summarize_with_custom_openai
from App_Function_Libraries.DB.DB_Manager import add_media_to_database
# Import Local
from App_Function_Libraries.Utils.Utils import load_and_log_configs, load_comprehensive_config, sanitize_filename, \
clean_youtube_url, create_download_directory, is_valid_url
from App_Function_Libraries.Video_DL_Ingestion_Lib import download_video, extract_video_info
#
#######################################################################################################################
# Function Definitions
#
config = load_comprehensive_config()
openai_api_key = config.get('API', 'openai_api_key', fallback=None)
def summarize(
input_data: str,
custom_prompt_arg: Optional[str],
api_name: str,
api_key: Optional[str],
temp: Optional[float],
system_message: Optional[str]
) -> str:
try:
logging.debug(f"api_name type: {type(api_name)}, value: {api_name}")
if api_name.lower() == "openai":
return summarize_with_openai(api_key, input_data, custom_prompt_arg, temp, system_message)
elif api_name.lower() == "anthropic":
return summarize_with_anthropic(api_key, input_data, custom_prompt_arg, temp, system_message)
elif api_name.lower() == "cohere":
return summarize_with_cohere(api_key, input_data, custom_prompt_arg, temp, system_message)
elif api_name.lower() == "groq":
return summarize_with_groq(api_key, input_data, custom_prompt_arg, temp, system_message)
elif api_name.lower() == "huggingface":
return summarize_with_huggingface(api_key, input_data, custom_prompt_arg, temp)
elif api_name.lower() == "openrouter":
return summarize_with_openrouter(api_key, input_data, custom_prompt_arg, temp, system_message)
elif api_name.lower() == "deepseek":
return summarize_with_deepseek(api_key, input_data, custom_prompt_arg, temp, system_message)
elif api_name.lower() == "mistral":
return summarize_with_mistral(api_key, input_data, custom_prompt_arg, temp, system_message)
elif api_name.lower() == "llama.cpp":
return summarize_with_llama(input_data, custom_prompt_arg, temp, system_message)
elif api_name.lower() == "kobold":
return summarize_with_kobold(input_data, api_key, custom_prompt_arg, temp, system_message)
elif api_name.lower() == "ooba":
return summarize_with_oobabooga(input_data, api_key, custom_prompt_arg, temp, system_message)
elif api_name.lower() == "tabbyapi":
return summarize_with_tabbyapi(input_data, custom_prompt_arg, temp, system_message)
elif api_name.lower() == "vllm":
return summarize_with_vllm(input_data, custom_prompt_arg, None, system_message)
elif api_name.lower() == "local-llm":
return summarize_with_local_llm(input_data, custom_prompt_arg, temp, system_message)
elif api_name.lower() == "huggingface":
return summarize_with_huggingface(api_key, input_data, custom_prompt_arg, temp, )#system_message)
else:
return f"Error: Invalid API Name {api_name}"
except Exception as e:
logging.error(f"Error in summarize function: {str(e)}", exc_info=True)
return f"Error: {str(e)}"
def extract_text_from_segments(segments):
logging.debug(f"Segments received: {segments}")
logging.debug(f"Type of segments: {type(segments)}")
text = ""
if isinstance(segments, list):
for segment in segments:
logging.debug(f"Current segment: {segment}")
logging.debug(f"Type of segment: {type(segment)}")
if 'Text' in segment:
text += segment['Text'] + " "
else:
logging.warning(f"Skipping segment due to missing 'Text' key: {segment}")
else:
logging.warning(f"Unexpected type of 'segments': {type(segments)}")
return text.strip()
def summarize_with_openai(api_key, input_data, custom_prompt_arg, temp=None, system_message=None):
loaded_config_data = load_and_log_configs()
try:
# API key validation
if not api_key or api_key.strip() == "":
logging.info("OpenAI: #1 API key not provided as parameter")
logging.info("OpenAI: Attempting to use API key from config file")
api_key = loaded_config_data['api_keys']['openai']
if not api_key or api_key.strip() == "":
logging.error("OpenAI: #2 API key not found or is empty")
return "OpenAI: API Key Not Provided/Found in Config file or is empty"
openai_api_key = api_key
logging.debug(f"OpenAI: Using API Key: {api_key[:5]}...{api_key[-5:]}")
# Input data handling
logging.debug(f"OpenAI: Raw input data type: {type(input_data)}")
logging.debug(f"OpenAI: Raw input data (first 500 chars): {str(input_data)[:500]}...")
if isinstance(input_data, str):
if input_data.strip().startswith('{'):
# It's likely a JSON string
logging.debug("OpenAI: Parsing provided JSON string data for summarization")
try:
data = json.loads(input_data)
except json.JSONDecodeError as e:
logging.error(f"OpenAI: Error parsing JSON string: {str(e)}")
return f"OpenAI: Error parsing JSON input: {str(e)}"
elif os.path.isfile(input_data):
logging.debug("OpenAI: Loading JSON data from file for summarization")
with open(input_data, 'r') as file:
data = json.load(file)
else:
logging.debug("OpenAI: Using provided string data for summarization")
data = input_data
else:
data = input_data
logging.debug(f"OpenAI: Processed data type: {type(data)}")
logging.debug(f"OpenAI: Processed data (first 500 chars): {str(data)[:500]}...")
# Text extraction
if isinstance(data, dict):
if 'summary' in data:
logging.debug("OpenAI: Summary already exists in the loaded data")
return data['summary']
elif 'segments' in data:
text = extract_text_from_segments(data['segments'])
else:
text = json.dumps(data) # Convert dict to string if no specific format
elif isinstance(data, list):
text = extract_text_from_segments(data)
elif isinstance(data, str):
text = data
else:
raise ValueError(f"OpenAI: Invalid input data format: {type(data)}")
logging.debug(f"OpenAI: Extracted text (first 500 chars): {text[:500]}...")
logging.debug(f"OpenAI: Custom prompt: {custom_prompt_arg}")
openai_model = loaded_config_data['models']['openai'] or "gpt-4o"
logging.debug(f"OpenAI: Using model: {openai_model}")
headers = {
'Authorization': f'Bearer {openai_api_key}',
'Content-Type': 'application/json'
}
logging.debug(
f"OpenAI API Key: {openai_api_key[:5]}...{openai_api_key[-5:] if openai_api_key else None}")
logging.debug("openai: Preparing data + prompt for submittal")
openai_prompt = f"{text} \n\n\n\n{custom_prompt_arg}"
if temp is None:
temp = 0.7
if system_message is None:
system_message = "You are a helpful AI assistant who does whatever the user requests."
temp = float(temp)
data = {
"model": openai_model,
"messages": [
{"role": "system", "content": system_message},
{"role": "user", "content": openai_prompt}
],
"max_tokens": 4096,
"temperature": temp
}
logging.debug("OpenAI: Posting request")
response = requests.post('https://api.openai.com/v1/chat/completions', headers=headers, json=data)
if response.status_code == 200:
response_data = response.json()
if 'choices' in response_data and len(response_data['choices']) > 0:
summary = response_data['choices'][0]['message']['content'].strip()
logging.debug("OpenAI: Summarization successful")
logging.debug(f"OpenAI: Summary (first 500 chars): {summary[:500]}...")
return summary
else:
logging.warning("OpenAI: Summary not found in the response data")
return "OpenAI: Summary not available"
else:
logging.error(f"OpenAI: Summarization failed with status code {response.status_code}")
logging.error(f"OpenAI: Error response: {response.text}")
return f"OpenAI: Failed to process summary. Status code: {response.status_code}"
except json.JSONDecodeError as e:
logging.error(f"OpenAI: Error decoding JSON: {str(e)}", exc_info=True)
return f"OpenAI: Error decoding JSON input: {str(e)}"
except requests.RequestException as e:
logging.error(f"OpenAI: Error making API request: {str(e)}", exc_info=True)
return f"OpenAI: Error making API request: {str(e)}"
except Exception as e:
logging.error(f"OpenAI: Unexpected error: {str(e)}", exc_info=True)
return f"OpenAI: Unexpected error occurred: {str(e)}"
def summarize_with_anthropic(api_key, input_data, custom_prompt_arg, temp=None, system_message=None, max_retries=3, retry_delay=5):
logging.debug("Anthropic: Summarization process starting...")
try:
logging.debug("Anthropic: Loading and validating configurations")
loaded_config_data = load_and_log_configs()
if loaded_config_data is None:
logging.error("Failed to load configuration data")
anthropic_api_key = None
else:
# Prioritize the API key passed as a parameter
if api_key and api_key.strip():
anthropic_api_key = api_key
logging.info("Anthropic: Using API key provided as parameter")
else:
# If no parameter is provided, use the key from the config
anthropic_api_key = loaded_config_data['api_keys'].get('anthropic')
if anthropic_api_key:
logging.info("Anthropic: Using API key from config file")
else:
logging.warning("Anthropic: No API key found in config file")
# Final check to ensure we have a valid API key
if not anthropic_api_key or not anthropic_api_key.strip():
logging.error("Anthropic: No valid API key available")
# You might want to raise an exception here or handle this case as appropriate for your application
#FIXME
# For example: raise ValueError("No valid Anthropic API key available")
logging.debug(f"Anthropic: Using API Key: {anthropic_api_key[:5]}...{anthropic_api_key[-5:]}")
if isinstance(input_data, str) and os.path.isfile(input_data):
logging.debug("AnthropicAI: Loading json data for summarization")
with open(input_data, 'r') as file:
data = json.load(file)
else:
logging.debug("AnthropicAI: Using provided string data for summarization")
data = input_data
# DEBUG - Debug logging to identify sent data
logging.debug(f"AnthropicAI: Loaded data: {data[:500]}...(snipped to first 500 chars)")
logging.debug(f"AnthropicAI: Type of data: {type(data)}")
if isinstance(data, dict) and 'summary' in data:
# If the loaded data is a dictionary and already contains a summary, return it
logging.debug("Anthropic: Summary already exists in the loaded data")
return data['summary']
# If the loaded data is a list of segment dictionaries or a string, proceed with summarization
if isinstance(data, list):
segments = data
text = extract_text_from_segments(segments)
elif isinstance(data, str):
text = data
else:
raise ValueError("Anthropic: Invalid input data format")
if temp is None:
temp = 0.1
temp = float(temp)
if system_message is None:
system_message = "You are a helpful AI assistant who does whatever the user requests."
headers = {
'x-api-key': anthropic_api_key,
'anthropic-version': '2023-06-01',
'Content-Type': 'application/json'
}
anthropic_prompt = custom_prompt_arg
logging.debug(f"Anthropic: Prompt is {anthropic_prompt}")
user_message = {
"role": "user",
"content": f"{text} \n\n\n\n{anthropic_prompt}"
}
model = loaded_config_data['models']['anthropic']
data = {
"model": model,
"max_tokens": 4096, # max _possible_ tokens to return
"messages": [user_message],
"stop_sequences": ["\n\nHuman:"],
"temperature": temp,
"top_k": 0,
"top_p": 1.0,
"metadata": {
"user_id": "example_user_id",
},
"stream": False,
"system": system_message
}
for attempt in range(max_retries):
try:
logging.debug("anthropic: Posting request to API")
response = requests.post('https://api.anthropic.com/v1/messages', headers=headers, json=data)
# Check if the status code indicates success
if response.status_code == 200:
logging.debug("anthropic: Post submittal successful")
response_data = response.json()
try:
summary = response_data['content'][0]['text'].strip()
logging.debug("anthropic: Summarization successful")
print("Summary processed successfully.")
return summary
except (IndexError, KeyError) as e:
logging.debug("anthropic: Unexpected data in response")
print("Unexpected response format from Anthropic API:", response.text)
return None
elif response.status_code == 500: # Handle internal server error specifically
logging.debug("anthropic: Internal server error")
print("Internal server error from API. Retrying may be necessary.")
time.sleep(retry_delay)
else:
logging.debug(
f"anthropic: Failed to summarize, status code {response.status_code}: {response.text}")
print(f"Failed to process summary, status code {response.status_code}: {response.text}")
return None
except RequestException as e:
logging.error(f"anthropic: Network error during attempt {attempt + 1}/{max_retries}: {str(e)}")
if attempt < max_retries - 1:
time.sleep(retry_delay)
else:
return f"anthropic: Network error: {str(e)}"
except FileNotFoundError as e:
logging.error(f"anthropic: File not found: {input_data}")
return f"anthropic: File not found: {input_data}"
except json.JSONDecodeError as e:
logging.error(f"anthropic: Invalid JSON format in file: {input_data}")
return f"anthropic: Invalid JSON format in file: {input_data}"
except Exception as e:
logging.error(f"anthropic: Error in processing: {str(e)}")
return f"anthropic: Error occurred while processing summary with Anthropic: {str(e)}"
# Summarize with Cohere
def summarize_with_cohere(api_key, input_data, custom_prompt_arg, temp=None, system_message=None):
logging.debug("Cohere: Summarization process starting...")
try:
logging.debug("Cohere: Loading and validating configurations")
loaded_config_data = load_and_log_configs()
if loaded_config_data is None:
logging.error("Failed to load configuration data")
cohere_api_key = None
else:
# Prioritize the API key passed as a parameter
if api_key and api_key.strip():
cohere_api_key = api_key
logging.info("Cohere: Using API key provided as parameter")
else:
# If no parameter is provided, use the key from the config
cohere_api_key = loaded_config_data['api_keys'].get('cohere')
if cohere_api_key:
logging.info("Cohere: Using API key from config file")
else:
logging.warning("Cohere: No API key found in config file")
# Final check to ensure we have a valid API key
if not cohere_api_key or not cohere_api_key.strip():
logging.error("Cohere: No valid API key available")
# You might want to raise an exception here or handle this case as appropriate for your application
# FIXME
# For example: raise ValueError("No valid Anthropic API key available")
if custom_prompt_arg is None:
custom_prompt_arg = ""
if system_message is None:
system_message = ""
logging.debug(f"Cohere: Using API Key: {cohere_api_key[:5]}...{cohere_api_key[-5:]}")
if isinstance(input_data, str) and os.path.isfile(input_data):
logging.debug("Cohere: Loading json data for summarization")
with open(input_data, 'r') as file:
data = json.load(file)
else:
logging.debug("Cohere: Using provided string data for summarization")
data = input_data
# DEBUG - Debug logging to identify sent data
logging.debug(f"Cohere: Loaded data: {data[:500]}...(snipped to first 500 chars)")
logging.debug(f"Cohere: Type of data: {type(data)}")
if isinstance(data, dict) and 'summary' in data:
# If the loaded data is a dictionary and already contains a summary, return it
logging.debug("Cohere: Summary already exists in the loaded data")
return data['summary']
# If the loaded data is a list of segment dictionaries or a string, proceed with summarization
if isinstance(data, list):
segments = data
text = extract_text_from_segments(segments)
elif isinstance(data, str):
text = data
else:
raise ValueError("Invalid input data format")
cohere_model = loaded_config_data['models']['cohere']
if temp is None:
temp = 0.3
temp = float(temp)
if system_message is None:
system_message = "You are a helpful AI assistant who does whatever the user requests."
headers = {
'accept': 'application/json',
'content-type': 'application/json',
'Authorization': f'Bearer {cohere_api_key}'
}
cohere_prompt = f"{text} \n\n\n\n{custom_prompt_arg}"
logging.debug(f"cohere: Prompt being sent is {cohere_prompt}")
data = {
"preamble": system_message,
"message": cohere_prompt,
"model": cohere_model,
# "connectors": [{"id": "web-search"}],
"temperature": temp
}
logging.debug("cohere: Submitting request to API endpoint")
response = requests.post('https://api.cohere.ai/v1/chat', headers=headers, json=data)
response_data = response.json()
logging.debug("API Response Data: %s", response_data)
if response.status_code == 200:
if 'text' in response_data:
summary = response_data['text'].strip()
logging.debug("cohere: Summarization successful")
print("Summary processed successfully.")
return summary
else:
logging.error("Expected data not found in API response.")
return "Expected data not found in API response."
else:
logging.error(f"cohere: API request failed with status code {response.status_code}: {response.text}")
print(f"Failed to process summary, status code {response.status_code}: {response.text}")
return f"cohere: API request failed: {response.text}"
except Exception as e:
logging.error("cohere: Error in processing: %s", str(e))
return f"cohere: Error occurred while processing summary with Cohere: {str(e)}"
# https://console.groq.com/docs/quickstart
def summarize_with_groq(api_key, input_data, custom_prompt_arg, temp=None, system_message=None):
logging.debug("Groq: Summarization process starting...")
try:
logging.debug("Groq: Loading and validating configurations")
loaded_config_data = load_and_log_configs()
if loaded_config_data is None:
logging.error("Failed to load configuration data")
groq_api_key = None
else:
# Prioritize the API key passed as a parameter
if api_key and api_key.strip():
groq_api_key = api_key
logging.info("Groq: Using API key provided as parameter")
else:
# If no parameter is provided, use the key from the config
groq_api_key = loaded_config_data['api_keys'].get('groq')
if groq_api_key:
logging.info("Groq: Using API key from config file")
else:
logging.warning("Groq: No API key found in config file")
# Final check to ensure we have a valid API key
if not groq_api_key or not groq_api_key.strip():
logging.error("Anthropic: No valid API key available")
# You might want to raise an exception here or handle this case as appropriate for your application
# FIXME
# For example: raise ValueError("No valid Anthropic API key available")
logging.debug(f"Groq: Using API Key: {groq_api_key[:5]}...{groq_api_key[-5:]}")
# Transcript data handling & Validation
if isinstance(input_data, str) and os.path.isfile(input_data):
logging.debug("Groq: Loading json data for summarization")
with open(input_data, 'r') as file:
data = json.load(file)
else:
logging.debug("Groq: Using provided string data for summarization")
data = input_data
# DEBUG - Debug logging to identify sent data
logging.debug(f"Groq: Loaded data: {data[:500]}...(snipped to first 500 chars)")
logging.debug(f"Groq: Type of data: {type(data)}")
if isinstance(data, dict) and 'summary' in data:
# If the loaded data is a dictionary and already contains a summary, return it
logging.debug("Groq: Summary already exists in the loaded data")
return data['summary']
# If the loaded data is a list of segment dictionaries or a string, proceed with summarization
if isinstance(data, list):
segments = data
text = extract_text_from_segments(segments)
elif isinstance(data, str):
text = data
else:
raise ValueError("Groq: Invalid input data format")
# Set the model to be used
groq_model = loaded_config_data['models']['groq']
if temp is None:
temp = 0.2
temp = float(temp)
if system_message is None:
system_message = "You are a helpful AI assistant who does whatever the user requests."
headers = {
'Authorization': f'Bearer {groq_api_key}',
'Content-Type': 'application/json'
}
groq_prompt = f"{text} \n\n\n\n{custom_prompt_arg}"
logging.debug("groq: Prompt being sent is {groq_prompt}")
data = {
"messages": [
{
"role": "system",
"content": system_message,
},
{
"role": "user",
"content": groq_prompt,
}
],
"model": groq_model,
"temperature": temp
}
logging.debug("groq: Submitting request to API endpoint")
print("groq: Submitting request to API endpoint")
response = requests.post('https://api.groq.com/openai/v1/chat/completions', headers=headers, json=data)
response_data = response.json()
logging.debug("API Response Data: %s", response_data)
if response.status_code == 200:
if 'choices' in response_data and len(response_data['choices']) > 0:
summary = response_data['choices'][0]['message']['content'].strip()
logging.debug("groq: Summarization successful")
print("Summarization successful.")
return summary
else:
logging.error("Expected data not found in API response.")
return "Expected data not found in API response."
else:
logging.error(f"groq: API request failed with status code {response.status_code}: {response.text}")
return f"groq: API request failed: {response.text}"
except Exception as e:
logging.error("groq: Error in processing: %s", str(e))
return f"groq: Error occurred while processing summary with groq: {str(e)}"
def summarize_with_openrouter(api_key, input_data, custom_prompt_arg, temp=None, system_message=None):
import requests
import json
global openrouter_model, openrouter_api_key
try:
logging.debug("OpenRouter: Loading and validating configurations")
loaded_config_data = load_and_log_configs()
if loaded_config_data is None:
logging.error("Failed to load configuration data")
openrouter_api_key = None
else:
# Prioritize the API key passed as a parameter
if api_key and api_key.strip():
openrouter_api_key = api_key
logging.info("OpenRouter: Using API key provided as parameter")
else:
# If no parameter is provided, use the key from the config
openrouter_api_key = loaded_config_data['api_keys'].get('openrouter')
if openrouter_api_key:
logging.info("OpenRouter: Using API key from config file")
else:
logging.warning("OpenRouter: No API key found in config file")
# Model Selection validation
logging.debug("OpenRouter: Validating model selection")
loaded_config_data = load_and_log_configs()
openrouter_model = loaded_config_data['models']['openrouter']
logging.debug(f"OpenRouter: Using model from config file: {openrouter_model}")
# Final check to ensure we have a valid API key
if not openrouter_api_key or not openrouter_api_key.strip():
logging.error("OpenRouter: No valid API key available")
raise ValueError("No valid Anthropic API key available")
except Exception as e:
logging.error("OpenRouter: Error in processing: %s", str(e))
return f"OpenRouter: Error occurred while processing config file with OpenRouter: {str(e)}"
logging.debug(f"OpenRouter: Using API Key: {openrouter_api_key[:5]}...{openrouter_api_key[-5:]}")
logging.debug(f"OpenRouter: Using Model: {openrouter_model}")
if isinstance(input_data, str) and os.path.isfile(input_data):
logging.debug("OpenRouter: Loading json data for summarization")
with open(input_data, 'r') as file:
data = json.load(file)
else:
logging.debug("OpenRouter: Using provided string data for summarization")
data = input_data
# DEBUG - Debug logging to identify sent data
logging.debug(f"OpenRouter: Loaded data: {data[:500]}...(snipped to first 500 chars)")
logging.debug(f"OpenRouter: Type of data: {type(data)}")
if isinstance(data, dict) and 'summary' in data:
# If the loaded data is a dictionary and already contains a summary, return it
logging.debug("OpenRouter: Summary already exists in the loaded data")
return data['summary']
# If the loaded data is a list of segment dictionaries or a string, proceed with summarization
if isinstance(data, list):
segments = data
text = extract_text_from_segments(segments)
elif isinstance(data, str):
text = data
else:
raise ValueError("OpenRouter: Invalid input data format")
openrouter_prompt = f"{input_data} \n\n\n\n{custom_prompt_arg}"
if temp is None:
temp = 0.1
temp = float(temp)
if system_message is None:
system_message = "You are a helpful AI assistant who does whatever the user requests."
try:
logging.debug("OpenRouter: Submitting request to API endpoint")
print("OpenRouter: Submitting request to API endpoint")
response = requests.post(
url="https://openrouter.ai/api/v1/chat/completions",
headers={
"Authorization": f"Bearer {openrouter_api_key}",
},
data=json.dumps({
"model": openrouter_model,
"messages": [
{"role": "system", "content": system_message},
{"role": "user", "content": openrouter_prompt}
],
"temperature": temp
})
)
response_data = response.json()
logging.debug("API Response Data: %s", response_data)
if response.status_code == 200:
if 'choices' in response_data and len(response_data['choices']) > 0:
summary = response_data['choices'][0]['message']['content'].strip()
logging.debug("openrouter: Summarization successful")
print("openrouter: Summarization successful.")
return summary
else:
logging.error("openrouter: Expected data not found in API response.")
return "openrouter: Expected data not found in API response."
else:
logging.error(f"openrouter: API request failed with status code {response.status_code}: {response.text}")
return f"openrouter: API request failed: {response.text}"
except Exception as e:
logging.error("openrouter: Error in processing: %s", str(e))
return f"openrouter: Error occurred while processing summary with openrouter: {str(e)}"
def summarize_with_huggingface(api_key, input_data, custom_prompt_arg, temp=None):
loaded_config_data = load_and_log_configs()
logging.debug("HuggingFace: Summarization process starting...")
try:
logging.debug("HuggingFace: Loading and validating configurations")
if loaded_config_data is None:
logging.error("Failed to load configuration data")
huggingface_api_key = None
else:
# Prioritize the API key passed as a parameter
if api_key and api_key.strip():
huggingface_api_key = api_key
logging.info("HuggingFace: Using API key provided as parameter")
else:
# If no parameter is provided, use the key from the config
huggingface_api_key = loaded_config_data['api_keys'].get('huggingface')
logging.debug(f"HuggingFace: API key from config: {huggingface_api_key[:5]}...{huggingface_api_key[-5:]}")
if huggingface_api_key:
logging.info("HuggingFace: Using API key from config file")
else:
logging.warning("HuggingFace: No API key found in config file")
# Final check to ensure we have a valid API key
if not huggingface_api_key or not huggingface_api_key.strip():
logging.error("HuggingFace: No valid API key available")
# You might want to raise an exception here or handle this case as appropriate for your application
# FIXME
# For example: raise ValueError("No valid Anthropic API key available")
logging.debug(f"HuggingFace: Using API Key: {huggingface_api_key[:5]}...{huggingface_api_key[-5:]}")
if isinstance(input_data, str) and os.path.isfile(input_data):
logging.debug("HuggingFace: Loading json data for summarization")
with open(input_data, 'r') as file:
data = json.load(file)
else:
logging.debug("HuggingFace: Using provided string data for summarization")
data = input_data
# DEBUG - Debug logging to identify sent data
logging.debug(f"HuggingFace: Loaded data: {data[:500]}...(snipped to first 500 chars)")
logging.debug(f"HuggingFace: Type of data: {type(data)}")
if isinstance(data, dict) and 'summary' in data:
# If the loaded data is a dictionary and already contains a summary, return it
logging.debug("HuggingFace: Summary already exists in the loaded data")
return data['summary']
# If the loaded data is a list of segment dictionaries or a string, proceed with summarization
if isinstance(data, list):
segments = data
text = extract_text_from_segments(segments)
elif isinstance(data, str):
text = data
else:
raise ValueError("HuggingFace: Invalid input data format")
headers = {
"Authorization": f"Bearer {huggingface_api_key}"
}
huggingface_model = loaded_config_data['models']['huggingface']
API_URL = f"https://api-inference.huggingface.co/models/{huggingface_model}"
if temp is None:
temp = 0.1
temp = float(temp)
huggingface_prompt = f"{custom_prompt_arg}\n\n\n{text}"
logging.debug("huggingface: Prompt being sent is {huggingface_prompt}")
data = {
"inputs": huggingface_prompt,
"max_tokens": 4096,
"stream": False,
"temperature": temp
}
logging.debug("huggingface: Submitting request...")
response = requests.post(API_URL, headers=headers, json=data)
if response.status_code == 200:
print(response.json())
chat_response = response.json()[0]['generated_text'].strip()
logging.debug("huggingface: Summarization successful")
print("Chat request successful.")
return chat_response
else:
logging.error(f"huggingface: Summarization failed with status code {response.status_code}: {response.text}")
return f"Failed to process summary, status code {response.status_code}: {response.text}"
except Exception as e:
logging.error("huggingface: Error in processing: %s", str(e))
print(f"Error occurred while processing summary with huggingface: {str(e)}")
return None
def summarize_with_deepseek(api_key, input_data, custom_prompt_arg, temp=None, system_message=None):
logging.debug("DeepSeek: Summarization process starting...")
try:
logging.debug("DeepSeek: Loading and validating configurations")
loaded_config_data = load_and_log_configs()
if loaded_config_data is None:
logging.error("Failed to load configuration data")
deepseek_api_key = None
else:
# Prioritize the API key passed as a parameter
if api_key and api_key.strip():
deepseek_api_key = api_key
logging.info("DeepSeek: Using API key provided as parameter")
else:
# If no parameter is provided, use the key from the config
deepseek_api_key = loaded_config_data['api_keys'].get('deepseek')
if deepseek_api_key:
logging.info("DeepSeek: Using API key from config file")
else:
logging.warning("DeepSeek: No API key found in config file")
# Final check to ensure we have a valid API key
if not deepseek_api_key or not deepseek_api_key.strip():
logging.error("DeepSeek: No valid API key available")
# You might want to raise an exception here or handle this case as appropriate for your application
# FIXME
# For example: raise ValueError("No valid deepseek API key available")
logging.debug(f"DeepSeek: Using API Key: {deepseek_api_key[:5]}...{deepseek_api_key[-5:]}")
# Input data handling
if isinstance(input_data, str) and os.path.isfile(input_data):
logging.debug("DeepSeek: Loading json data for summarization")
with open(input_data, 'r') as file:
data = json.load(file)
else:
logging.debug("DeepSeek: Using provided string data for summarization")
data = input_data
# DEBUG - Debug logging to identify sent data
logging.debug(f"DeepSeek: Loaded data: {data[:500]}...(snipped to first 500 chars)")
logging.debug(f"DeepSeek: Type of data: {type(data)}")
if isinstance(data, dict) and 'summary' in data:
# If the loaded data is a dictionary and already contains a summary, return it
logging.debug("DeepSeek: Summary already exists in the loaded data")
return data['summary']
# Text extraction
if isinstance(data, list):
segments = data
text = extract_text_from_segments(segments)
elif isinstance(data, str):
text = data
else:
raise ValueError("DeepSeek: Invalid input data format")
deepseek_model = loaded_config_data['models']['deepseek'] or "deepseek-chat"
if temp is None:
temp = 0.1
temp = float(temp)
if system_message is None:
system_message = "You are a helpful AI assistant who does whatever the user requests."
headers = {
'Authorization': f'Bearer {api_key}',
'Content-Type': 'application/json'
}
logging.debug(
f"Deepseek API Key: {api_key[:5]}...{api_key[-5:] if api_key else None}")
logging.debug("openai: Preparing data + prompt for submittal")
deepseek_prompt = f"{text} \n\n\n\n{custom_prompt_arg}"
data = {
"model": deepseek_model,
"messages": [
{"role": "system", "content": system_message},
{"role": "user", "content": deepseek_prompt}
],
"stream": False,
"temperature": temp
}
logging.debug("DeepSeek: Posting request")
response = requests.post('https://api.deepseek.com/chat/completions', headers=headers, json=data)
if response.status_code == 200:
response_data = response.json()
if 'choices' in response_data and len(response_data['choices']) > 0:
summary = response_data['choices'][0]['message']['content'].strip()
logging.debug("DeepSeek: Summarization successful")
return summary
else:
logging.warning("DeepSeek: Summary not found in the response data")
return "DeepSeek: Summary not available"
else:
logging.error(f"DeepSeek: Summarization failed with status code {response.status_code}")
logging.error(f"DeepSeek: Error response: {response.text}")
return f"DeepSeek: Failed to process summary. Status code: {response.status_code}"
except Exception as e:
logging.error(f"DeepSeek: Error in processing: {str(e)}", exc_info=True)
return f"DeepSeek: Error occurred while processing summary: {str(e)}"
def summarize_with_mistral(api_key, input_data, custom_prompt_arg, temp=None, system_message=None):
logging.debug("Mistral: Summarization process starting...")
try:
logging.debug("Mistral: Loading and validating configurations")
loaded_config_data = load_and_log_configs()
if loaded_config_data is None:
logging.error("Failed to load configuration data")
mistral_api_key = None
else:
# Prioritize the API key passed as a parameter
if api_key and api_key.strip():
mistral_api_key = api_key
logging.info("Mistral: Using API key provided as parameter")
else:
# If no parameter is provided, use the key from the config
mistral_api_key = loaded_config_data['api_keys'].get('mistral')
if mistral_api_key:
logging.info("Mistral: Using API key from config file")
else:
logging.warning("Mistral: No API key found in config file")
# Final check to ensure we have a valid API key
if not mistral_api_key or not mistral_api_key.strip():
logging.error("Mistral: No valid API key available")
# You might want to raise an exception here or handle this case as appropriate for your application
# FIXME
# For example: raise ValueError("No valid deepseek API key available")
logging.debug(f"Mistral: Using API Key: {mistral_api_key[:5]}...{mistral_api_key[-5:]}")
# Input data handling
if isinstance(input_data, str) and os.path.isfile(input_data):
logging.debug("Mistral: Loading json data for summarization")
with open(input_data, 'r') as file:
data = json.load(file)
else:
logging.debug("Mistral: Using provided string data for summarization")
data = input_data
# DEBUG - Debug logging to identify sent data
logging.debug(f"Mistral: Loaded data: {data[:500]}...(snipped to first 500 chars)")
logging.debug(f"Mistral: Type of data: {type(data)}")
if isinstance(data, dict) and 'summary' in data:
# If the loaded data is a dictionary and already contains a summary, return it
logging.debug("Mistral: Summary already exists in the loaded data")
return data['summary']
# Text extraction
if isinstance(data, list):
segments = data
text = extract_text_from_segments(segments)
elif isinstance(data, str):
text = data
else:
raise ValueError("Mistral: Invalid input data format")
mistral_model = loaded_config_data['models']['mistral'] or "mistral-large-latest"
if temp is None:
temp = 0.2
temp = float(temp)
if system_message is None:
system_message = "You are a helpful AI assistant who does whatever the user requests."
headers = {
'Authorization': f'Bearer {mistral_api_key}',
'Content-Type': 'application/json'
}
logging.debug(
f"Deepseek API Key: {mistral_api_key[:5]}...{mistral_api_key[-5:] if mistral_api_key else None}")
logging.debug("Mistral: Preparing data + prompt for submittal")
mistral_prompt = f"{custom_prompt_arg}\n\n\n\n{text} "
data = {
"model": mistral_model,
"messages": [
{"role": "system",
"content": system_message},
{"role": "user",
"content": mistral_prompt}
],
"temperature": temp,
"top_p": 1,
"max_tokens": 4096,
"stream": "false",
"safe_prompt": "false"
}
logging.debug("Mistral: Posting request")
response = requests.post('https://api.mistral.ai/v1/chat/completions', headers=headers, json=data)
if response.status_code == 200:
response_data = response.json()
if 'choices' in response_data and len(response_data['choices']) > 0:
summary = response_data['choices'][0]['message']['content'].strip()
logging.debug("Mistral: Summarization successful")
return summary
else:
logging.warning("Mistral: Summary not found in the response data")
return "Mistral: Summary not available"
else:
logging.error(f"Mistral: Summarization failed with status code {response.status_code}")
logging.error(f"Mistral: Error response: {response.text}")
return f"Mistral: Failed to process summary. Status code: {response.status_code}"
except Exception as e:
logging.error(f"Mistral: Error in processing: {str(e)}", exc_info=True)
return f"Mistral: Error occurred while processing summary: {str(e)}"
#
#
#######################################################################################################################
#
#
# Gradio File Processing
# Handle multiple videos as input
def process_video_urls(url_list, num_speakers, whisper_model, custom_prompt_input, offset, api_name, api_key, vad_filter,
download_video_flag, download_audio, rolling_summarization, detail_level, question_box,
keywords, chunk_text_by_words, max_words, chunk_text_by_sentences, max_sentences,
chunk_text_by_paragraphs, max_paragraphs, chunk_text_by_tokens, max_tokens, chunk_by_semantic,
semantic_chunk_size, semantic_chunk_overlap, recursive_summarization):
global current_progress
progress = [] # This must always be a list
status = [] # This must always be a list
if custom_prompt_input is None:
custom_prompt_input = """
You are a bulleted notes specialist. ```When creating comprehensive bulleted notes, you should follow these guidelines: Use multiple headings based on the referenced topics, not categories like quotes or terms. Headings should be surrounded by bold formatting and not be listed as bullet points themselves. Leave no space between headings and their corresponding list items underneath. Important terms within the content should be emphasized by setting them in bold font. Any text that ends with a colon should also be bolded. Before submitting your response, review the instructions, and make any corrections necessary to adhered to the specified format. Do not reference these instructions within the notes.``` \nBased on the content between backticks create comprehensive bulleted notes.
**Bulleted Note Creation Guidelines**
**Headings**:
- Based on referenced topics, not categories like quotes or terms
- Surrounded by **bold** formatting
- Not listed as bullet points
- No space between headings and list items underneath
**Emphasis**:
- **Important terms** set in bold font
- **Text ending in a colon**: also bolded
**Review**:
- Ensure adherence to specified format
- Do not reference these instructions in your response.</s>[INST] {{ .Prompt }} [/INST]"""
def update_progress(index, url, message):
progress.append(f"Processing {index + 1}/{len(url_list)}: {url}") # Append to list
status.append(message) # Append to list
return "\n".join(progress), "\n".join(status) # Return strings for display
for index, url in enumerate(url_list):
try:
logging.info(f"Starting to process video {index + 1}/{len(url_list)}: {url}")
transcription, summary, json_file_path, summary_file_path, _, _ = process_url(url=url,
num_speakers=num_speakers,
whisper_model=whisper_model,
custom_prompt_input=custom_prompt_input,
offset=offset,
api_name=api_name,
api_key=api_key,
vad_filter=vad_filter,
download_video_flag=download_video_flag,
download_audio=download_audio,
rolling_summarization=rolling_summarization,
detail_level=detail_level,
question_box=question_box,
keywords=keywords,
chunk_text_by_words=chunk_text_by_words,
max_words=max_words,
chunk_text_by_sentences=chunk_text_by_sentences,
max_sentences=max_sentences,
chunk_text_by_paragraphs=chunk_text_by_paragraphs,
max_paragraphs=max_paragraphs,
chunk_text_by_tokens=chunk_text_by_tokens,
max_tokens=max_tokens,
chunk_by_semantic=chunk_by_semantic,
semantic_chunk_size=semantic_chunk_size,
semantic_chunk_overlap=semantic_chunk_overlap,
recursive_summarization=recursive_summarization)
# Update progress and transcription properly
current_progress, current_status = update_progress(index, url, "Video processed and ingested into the database.")
logging.info(f"Successfully processed video {index + 1}/{len(url_list)}: {url}")
time.sleep(1)
except Exception as e:
logging.error(f"Error processing video {index + 1}/{len(url_list)}: {url}")
logging.error(f"Error details: {str(e)}")
current_progress, current_status = update_progress(index, url, f"Error: {str(e)}")
yield current_progress, current_status, None, None, None, None
success_message = "All videos have been transcribed, summarized, and ingested into the database successfully."
return current_progress, success_message, None, None, None, None
def perform_transcription(video_path, offset, whisper_model, vad_filter, diarize=False):
temp_files = []
logging.info(f"Processing media: {video_path}")
global segments_json_path
audio_file_path = convert_to_wav(video_path, offset)
logging.debug(f"Converted audio file: {audio_file_path}")
temp_files.append(audio_file_path)
logging.debug("Replacing audio file with segments.json file")
segments_json_path = audio_file_path.replace('.wav', '.segments.json')
temp_files.append(segments_json_path)
if diarize:
diarized_json_path = audio_file_path.replace('.wav', '.diarized.json')
# Check if diarized JSON already exists
if os.path.exists(diarized_json_path):
logging.info(f"Diarized file already exists: {diarized_json_path}")
try:
with open(diarized_json_path, 'r') as file:
diarized_segments = json.load(file)
if not diarized_segments:
logging.warning(f"Diarized JSON file is empty, re-generating: {diarized_json_path}")
raise ValueError("Empty diarized JSON file")
logging.debug(f"Loaded diarized segments from {diarized_json_path}")
return audio_file_path, diarized_segments
except (json.JSONDecodeError, ValueError) as e:
logging.error(f"Failed to read or parse the diarized JSON file: {e}")
os.remove(diarized_json_path)
# If diarized file doesn't exist or was corrupted, generate new diarized transcription
logging.info(f"Generating diarized transcription for {audio_file_path}")
diarized_segments = combine_transcription_and_diarization(audio_file_path)
# Save diarized segments
with open(diarized_json_path, 'w') as file:
json.dump(diarized_segments, file, indent=2)
return audio_file_path, diarized_segments
# Non-diarized transcription (existing functionality)
if os.path.exists(segments_json_path):
logging.info(f"Segments file already exists: {segments_json_path}")
try:
with open(segments_json_path, 'r') as file:
segments = json.load(file)
if not segments:
logging.warning(f"Segments JSON file is empty, re-generating: {segments_json_path}")
raise ValueError("Empty segments JSON file")
logging.debug(f"Loaded segments from {segments_json_path}")
except (json.JSONDecodeError, ValueError) as e:
logging.error(f"Failed to read or parse the segments JSON file: {e}")
os.remove(segments_json_path)
logging.info(f"Re-generating transcription for {audio_file_path}")
audio_file, segments = re_generate_transcription(audio_file_path, whisper_model, vad_filter)
if segments is None:
return None, None
else:
audio_file, segments = re_generate_transcription(audio_file_path, whisper_model, vad_filter)
return audio_file_path, segments
def re_generate_transcription(audio_file_path, whisper_model, vad_filter):
try:
segments = speech_to_text(audio_file_path, whisper_model=whisper_model, vad_filter=vad_filter)
# Save segments to JSON
with open(segments_json_path, 'w') as file:
json.dump(segments, file, indent=2)
logging.debug(f"Transcription segments saved to {segments_json_path}")
return audio_file_path, segments
except Exception as e:
logging.error(f"Error in re-generating transcription: {str(e)}")
return None, None
def save_transcription_and_summary(transcription_text, summary_text, download_path, info_dict):
try:
video_title = sanitize_filename(info_dict.get('title', 'Untitled'))
# Save transcription
transcription_file_path = os.path.join(download_path, f"{video_title}_transcription.txt")
with open(transcription_file_path, 'w', encoding='utf-8') as f:
f.write(transcription_text)
# Save summary if available
summary_file_path = None
if summary_text:
summary_file_path = os.path.join(download_path, f"{video_title}_summary.txt")
with open(summary_file_path, 'w', encoding='utf-8') as f:
f.write(summary_text)
return transcription_file_path, summary_file_path
except Exception as e:
logging.error(f"Error in save_transcription_and_summary: {str(e)}", exc_info=True)
return None, None
def summarize_chunk(api_name, text, custom_prompt_input, api_key, temp=None, system_message=None):
logging.debug("Entered 'summarize_chunk' function")
try:
result = summarize(text, custom_prompt_input, api_name, api_key, temp, system_message)
if result is None or result.startswith("Error:"):
logging.warning(f"Summarization with {api_name} failed: {result}")
return None
logging.info(f"Summarization with {api_name} successful")
return result
except Exception as e:
logging.error(f"Error in summarize_chunk with {api_name}: {str(e)}", exc_info=True)
return None
def extract_metadata_and_content(input_data):
metadata = {}
content = ""
if isinstance(input_data, str):
if os.path.exists(input_data):
with open(input_data, 'r', encoding='utf-8') as file:
data = json.load(file)
else:
try:
data = json.loads(input_data)
except json.JSONDecodeError:
return {}, input_data
elif isinstance(input_data, dict):
data = input_data
else:
return {}, str(input_data)
# Extract metadata
metadata['title'] = data.get('title', 'No title available')
metadata['author'] = data.get('author', 'Unknown author')
# Extract content
if 'transcription' in data:
content = extract_text_from_segments(data['transcription'])
elif 'segments' in data:
content = extract_text_from_segments(data['segments'])
elif 'content' in data:
content = data['content']
else:
content = json.dumps(data)
return metadata, content
def format_input_with_metadata(metadata, content):
formatted_input = f"Title: {metadata.get('title', 'No title available')}\n"
formatted_input += f"Author: {metadata.get('author', 'Unknown author')}\n\n"
formatted_input += content
return formatted_input
def perform_summarization(api_name, input_data, custom_prompt_input, api_key, recursive_summarization=False, temp=None, system_message=None):
loaded_config_data = load_and_log_configs()
logging.info("Starting summarization process...")
if system_message is None:
system_message = """
You are a bulleted notes specialist. ```When creating comprehensive bulleted notes, you should follow these guidelines: Use multiple headings based on the referenced topics, not categories like quotes or terms. Headings should be surrounded by bold formatting and not be listed as bullet points themselves. Leave no space between headings and their corresponding list items underneath. Important terms within the content should be emphasized by setting them in bold font. Any text that ends with a colon should also be bolded. Before submitting your response, review the instructions, and make any corrections necessary to adhered to the specified format. Do not reference these instructions within the notes.``` \nBased on the content between backticks create comprehensive bulleted notes.
**Bulleted Note Creation Guidelines**
**Headings**:
- Based on referenced topics, not categories like quotes or terms
- Surrounded by **bold** formatting
- Not listed as bullet points
- No space between headings and list items underneath
**Emphasis**:
- **Important terms** set in bold font
- **Text ending in a colon**: also bolded
**Review**:
- Ensure adherence to specified format
- Do not reference these instructions in your response.</s>[INST] {{ .Prompt }} [/INST]"""
try:
logging.debug(f"Input data type: {type(input_data)}")
logging.debug(f"Input data (first 500 chars): {str(input_data)[:500]}...")
# Extract metadata and content
metadata, content = extract_metadata_and_content(input_data)
logging.debug(f"Extracted metadata: {metadata}")
logging.debug(f"Extracted content (first 500 chars): {content[:500]}...")
# Prepare a structured input for summarization
structured_input = format_input_with_metadata(metadata, content)
# Perform summarization on the structured input
if recursive_summarization:
chunk_options = {
'method': 'words', # or 'sentences', 'paragraphs', 'tokens' based on your preference
'max_size': 1000, # adjust as needed
'overlap': 100, # adjust as needed
'adaptive': False,
'multi_level': False,
'language': 'english'
}
chunks = improved_chunking_process(structured_input, chunk_options)
logging.debug(f"Chunking process completed. Number of chunks: {len(chunks)}")
logging.debug("Now performing recursive summarization on each chunk...")
logging.debug("summary = recursive_summarize_chunks")
summary = recursive_summarize_chunks([chunk['text'] for chunk in chunks],
lambda x: summarize_chunk(api_name, x, custom_prompt_input, api_key),
custom_prompt_input, temp, system_message)
else:
logging.debug("summary = summarize_chunk")
summary = summarize_chunk(api_name, structured_input, custom_prompt_input, api_key, temp, system_message)
# add some actual validation logic
if summary is not None:
logging.info(f"Summary generated using {api_name} API")
if isinstance(input_data, str) and os.path.exists(input_data):
summary_file_path = input_data.replace('.json', '_summary.txt')
with open(summary_file_path, 'w', encoding='utf-8') as file:
file.write(summary)
else:
logging.warning(f"Failed to generate summary using {api_name} API")
logging.info("Summarization completed successfully.")
return summary
except requests.exceptions.ConnectionError:
logging.error("Connection error while summarizing")
except Exception as e:
logging.error(f"Error summarizing with {api_name}: {str(e)}", exc_info=True)
return f"An error occurred during summarization: {str(e)}"
return None
def extract_text_from_input(input_data):
if isinstance(input_data, str):
try:
# Try to parse as JSON
data = json.loads(input_data)
except json.JSONDecodeError:
# If not valid JSON, treat as plain text
return input_data
elif isinstance(input_data, dict):
data = input_data
else:
return str(input_data)
# Extract relevant fields from the JSON object
text_parts = []
if 'title' in data:
text_parts.append(f"Title: {data['title']}")
if 'description' in data:
text_parts.append(f"Description: {data['description']}")
if 'transcription' in data:
if isinstance(data['transcription'], list):
transcription_text = ' '.join([segment.get('Text', '') for segment in data['transcription']])
elif isinstance(data['transcription'], str):
transcription_text = data['transcription']
else:
transcription_text = str(data['transcription'])
text_parts.append(f"Transcription: {transcription_text}")
elif 'segments' in data:
segments_text = extract_text_from_segments(data['segments'])
text_parts.append(f"Segments: {segments_text}")
return '\n\n'.join(text_parts)
def process_url(
url,
num_speakers,
whisper_model,
custom_prompt_input,
offset,
api_name,
api_key,
vad_filter,
download_video_flag,
download_audio,
rolling_summarization,
detail_level,
# It's for the asking a question about a returned prompt - needs to be removed #FIXME
question_box,
keywords,
chunk_text_by_words,
max_words,
chunk_text_by_sentences,
max_sentences,
chunk_text_by_paragraphs,
max_paragraphs,
chunk_text_by_tokens,
max_tokens,
chunk_by_semantic,
semantic_chunk_size,
semantic_chunk_overlap,
local_file_path=None,
diarize=False,
recursive_summarization=False,
temp=None,
system_message=None):
# Handle the chunk summarization options
set_chunk_txt_by_words = chunk_text_by_words
set_max_txt_chunk_words = max_words
set_chunk_txt_by_sentences = chunk_text_by_sentences
set_max_txt_chunk_sentences = max_sentences
set_chunk_txt_by_paragraphs = chunk_text_by_paragraphs
set_max_txt_chunk_paragraphs = max_paragraphs
set_chunk_txt_by_tokens = chunk_text_by_tokens
set_max_txt_chunk_tokens = max_tokens
set_chunk_txt_by_semantic = chunk_by_semantic
set_semantic_chunk_size = semantic_chunk_size
set_semantic_chunk_overlap = semantic_chunk_overlap
progress = []
success_message = "All videos processed successfully. Transcriptions and summaries have been ingested into the database."
# Validate input
if not url and not local_file_path:
return "Process_URL: No URL provided.", "No URL provided.", None, None, None, None, None, None
if isinstance(url, str):
urls = url.strip().split('\n')
if len(urls) > 1:
return process_video_urls(urls, num_speakers, whisper_model, custom_prompt_input, offset, api_name, api_key, vad_filter,
download_video_flag, download_audio, rolling_summarization, detail_level, question_box,
keywords, chunk_text_by_words, max_words, chunk_text_by_sentences, max_sentences,
chunk_text_by_paragraphs, max_paragraphs, chunk_text_by_tokens, max_tokens, chunk_by_semantic, semantic_chunk_size, semantic_chunk_overlap, recursive_summarization)
else:
urls = [url]
if url and not is_valid_url(url):
return "Process_URL: Invalid URL format.", "Invalid URL format.", None, None, None, None, None, None
if url:
# Clean the URL to remove playlist parameters if any
url = clean_youtube_url(url)
logging.info(f"Process_URL: Processing URL: {url}")
if api_name:
print("Process_URL: API Name received:", api_name) # Debugging line
video_file_path = None
global info_dict
# If URL/Local video file is provided
try:
info_dict, title = extract_video_info(url)
download_path = create_download_directory(title)
current_whsiper_model = whisper_model
video_path = download_video(url, download_path, info_dict, download_video_flag, current_whsiper_model)
global segments
audio_file_path, segments = perform_transcription(video_path, offset, whisper_model, vad_filter)
if diarize:
transcription_text = combine_transcription_and_diarization(audio_file_path)
else:
audio_file, segments = perform_transcription(video_path, offset, whisper_model, vad_filter)
transcription_text = {'audio_file': audio_file, 'transcription': segments}
if audio_file_path is None or segments is None:
logging.error("Process_URL: Transcription failed or segments not available.")
return "Process_URL: Transcription failed.", "Transcription failed.", None, None, None, None
logging.debug(f"Process_URL: Transcription audio_file: {audio_file_path}")
logging.debug(f"Process_URL: Transcription segments: {segments}")
logging.debug(f"Process_URL: Transcription text: {transcription_text}")
# FIXME - Implement chunking calls here
# Implement chunking calls here
chunked_transcriptions = []
if chunk_text_by_words:
chunked_transcriptions = chunk_text_by_words(transcription_text['transcription'], max_words)
elif chunk_text_by_sentences:
chunked_transcriptions = chunk_text_by_sentences(transcription_text['transcription'], max_sentences)
elif chunk_text_by_paragraphs:
chunked_transcriptions = chunk_text_by_paragraphs(transcription_text['transcription'], max_paragraphs)
elif chunk_text_by_tokens:
chunked_transcriptions = chunk_text_by_tokens(transcription_text['transcription'], max_tokens)
elif chunk_by_semantic:
chunked_transcriptions = semantic_chunking(transcription_text['transcription'], semantic_chunk_size, 'tokens')
# If we did chunking, we now have the chunked transcripts in 'chunked_transcriptions'
elif rolling_summarization:
# FIXME - rolling summarization
# text = extract_text_from_segments(segments)
# summary_text = rolling_summarize_function(
# transcription_text,
# detail=detail_level,
# api_name=api_name,
# api_key=api_key,
# custom_prompt_input=custom_prompt_input,
# chunk_by_words=chunk_text_by_words,
# max_words=max_words,
# chunk_by_sentences=chunk_text_by_sentences,
# max_sentences=max_sentences,
# chunk_by_paragraphs=chunk_text_by_paragraphs,
# max_paragraphs=max_paragraphs,
# chunk_by_tokens=chunk_text_by_tokens,
# max_tokens=max_tokens
# )
pass
else:
pass
summarized_chunk_transcriptions = []
if chunk_text_by_words or chunk_text_by_sentences or chunk_text_by_paragraphs or chunk_text_by_tokens or chunk_by_semantic and api_name:
# Perform summarization based on chunks
for chunk in chunked_transcriptions:
summarized_chunks = []
if api_name == "anthropic":
summary = summarize_with_anthropic(api_key, chunk, custom_prompt_input)
elif api_name == "cohere":
summary = summarize_with_cohere(api_key, chunk, custom_prompt_input, temp, system_message)
elif api_name == "openai":
summary = summarize_with_openai(api_key, chunk, custom_prompt_input, temp, system_message)
elif api_name == "Groq":
summary = summarize_with_groq(api_key, chunk, custom_prompt_input, temp, system_message)
elif api_name == "DeepSeek":
summary = summarize_with_deepseek(api_key, chunk, custom_prompt_input, temp, system_message)
elif api_name == "OpenRouter":
summary = summarize_with_openrouter(api_key, chunk, custom_prompt_input, temp, system_message)
# Local LLM APIs
elif api_name == "Llama.cpp":
summary = summarize_with_llama(chunk, custom_prompt_input, api_key, temp, system_message)
elif api_name == "Kobold":
summary = summarize_with_kobold(chunk, None, custom_prompt_input, system_message, temp)
elif api_name == "Ooba":
summary = summarize_with_oobabooga(chunk, None, custom_prompt_input, system_message, temp)
elif api_name == "Tabbyapi":
summary = summarize_with_tabbyapi(chunk, custom_prompt_input, system_message, None, temp)
elif api_name == "VLLM":
summary = summarize_with_vllm(chunk, custom_prompt_input, None, None, system_message)
elif api_name == "Ollama":
summary = summarize_with_ollama(chunk, custom_prompt_input, api_key, temp, system_message, None)
elif api_name == "custom_openai_api":
summary = summarize_with_custom_openai(chunk, custom_prompt_input, api_key, temp=None, system_message=None)
summarized_chunk_transcriptions.append(summary)
# Combine chunked transcriptions into a single file
combined_transcription_text = '\n\n'.join(chunked_transcriptions)
combined_transcription_file_path = os.path.join(download_path, 'combined_transcription.txt')
with open(combined_transcription_file_path, 'w') as f:
f.write(combined_transcription_text)
# Combine summarized chunk transcriptions into a single file
combined_summary_text = '\n\n'.join(summarized_chunk_transcriptions)
combined_summary_file_path = os.path.join(download_path, 'combined_summary.txt')
with open(combined_summary_file_path, 'w') as f:
f.write(combined_summary_text)
# Handle rolling summarization
if rolling_summarization:
summary_text = rolling_summarize(
text=extract_text_from_segments(segments),
detail=detail_level,
model='gpt-4-turbo',
additional_instructions=custom_prompt_input,
summarize_recursively=recursive_summarization
)
elif api_name:
summary_text = perform_summarization(api_name, segments_json_path, custom_prompt_input, api_key,
recursive_summarization, temp=None)
else:
summary_text = 'Summary not available'
# Check to see if chunking was performed, and if so, return that instead
if chunk_text_by_words or chunk_text_by_sentences or chunk_text_by_paragraphs or chunk_text_by_tokens or chunk_by_semantic:
# Combine chunked transcriptions into a single file
# FIXME - validate this works....
json_file_path, summary_file_path = save_transcription_and_summary(combined_transcription_file_path, combined_summary_file_path, download_path, info_dict)
add_media_to_database(url, info_dict, segments, summary_text, keywords, custom_prompt_input, whisper_model)
return transcription_text, summary_text, json_file_path, summary_file_path, None, None
else:
json_file_path, summary_file_path = save_transcription_and_summary(transcription_text, summary_text, download_path, info_dict)
add_media_to_database(url, info_dict, segments, summary_text, keywords, custom_prompt_input, whisper_model)
return transcription_text, summary_text, json_file_path, summary_file_path, None, None
except Exception as e:
logging.error(f": {e}")
return str(e), 'process_url: Error processing the request.', None, None, None, None
#
#
############################################################################################################################################
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