App_Function_Libraries/Summarization/Local_Summarization_Lib.py

# Local_Summarization_Lib.py
#########################################
# Local Summarization Library
# This library is used to perform summarization with a 'local' inference engine.
#
####
#
####################
# Function List
# FIXME - UPDATE Function Arguments
# 1. summarize_with_local_llm(text, custom_prompt_arg)
# 2. summarize_with_llama(api_url, text, token, custom_prompt)
# 3. summarize_with_kobold(api_url, text, kobold_api_token, custom_prompt)
# 4. summarize_with_oobabooga(api_url, text, ooba_api_token, custom_prompt)
# 5. summarize_with_vllm(vllm_api_url, vllm_api_key_function_arg, llm_model, text, vllm_custom_prompt_function_arg)
# 6. summarize_with_tabbyapi(tabby_api_key, tabby_api_IP, text, tabby_model, custom_prompt)
# 7. save_summary_to_file(summary, file_path)
#
###############################
# Import necessary libraries
import json
import logging
import os
import time
from typing import Union

import requests
# Import 3rd-party Libraries
# Import Local
from App_Function_Libraries.Utils.Utils import load_and_log_configs, extract_text_from_segments
#
#######################################################################################################################
# Function Definitions
#

logger = logging.getLogger()


summarizer_prompt = """
                    <s>You are a bulleted notes specialist. [INST]```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.[/INST]
                        **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]
                    """

# FIXME - temp is not used
def summarize_with_local_llm(input_data, custom_prompt_arg, temp, system_message=None):
    try:
        if isinstance(input_data, str) and os.path.isfile(input_data):
            logging.debug("Local LLM: Loading json data 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

        logging.debug(f"Local LLM: Loaded data: {data}")
        logging.debug(f"Local LLM: 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("Local LLM: 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")

        if system_message is None:
            system_message = "You are a helpful AI assistant."

        headers = {
            'Content-Type': 'application/json'
        }

        logging.debug("Local LLM: Preparing data + prompt for submittal")
        local_llm_prompt = f"{text} \n\n\n\n{custom_prompt_arg}"
        data = {
            "messages": [
                {
                    "role": "system",
                    "content": system_message
                },
                {
                    "role": "user",
                    "content": local_llm_prompt
                }
            ],
            "max_tokens": 28000,  # Adjust tokens as needed
        }
        logging.debug("Local LLM: Posting request")
        response = requests.post('http://127.0.0.1:8080/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("Local LLM: Summarization successful")
                print("Local LLM: Summarization successful.")
                return summary
            else:
                logging.warning("Local LLM: Summary not found in the response data")
                return "Local LLM: Summary not available"
        else:
            logging.debug("Local LLM: Summarization failed")
            print("Local LLM: Failed to process summary:", response.text)
            return "Local LLM: Failed to process summary"
    except Exception as e:
        logging.debug("Local LLM: Error in processing: %s", str(e))
        print("Error occurred while processing summary with Local LLM:", str(e))
        return "Local LLM: Error occurred while processing summary"


def summarize_with_llama(input_data, custom_prompt, api_key=None, temp=None, system_message=None, api_url="http://127.0.0.1:8080/completion",):
    try:
        logging.debug("Llama.cpp: Loading and validating configurations")
        loaded_config_data = load_and_log_configs()
        if loaded_config_data is None:
            logging.error("Failed to load configuration data")
            llama_api_key = None
        else:
            # Prioritize the API key passed as a parameter
            if api_key and api_key.strip():
                llama_api_key = api_key
                logging.info("Llama.cpp: Using API key provided as parameter")
            else:
                # If no parameter is provided, use the key from the config
                llama_api_key = loaded_config_data['api_keys'].get('llama')
                if llama_api_key:
                    logging.info("Llama.cpp: Using API key from config file")
                else:
                    logging.warning("Llama.cpp: No API key found in config file")

        # Load transcript
        logging.debug("llama.cpp: Loading JSON data")
        if isinstance(input_data, str) and os.path.isfile(input_data):
            logging.debug("Llama.cpp: Loading json data for summarization")
            with open(input_data, 'r') as file:
                data = json.load(file)
        else:
            logging.debug("Llama.cpp: Using provided string data for summarization")
            data = input_data

        logging.debug(f"Llama Summarize: Loaded data: {data}")
        logging.debug(f"Llama Summarize: 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("Llama Summarize: 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("Llama Summarize: Invalid input data format")

        headers = {
            'accept': 'application/json',
            'content-type': 'application/json',
        }
        if len(api_key) > 5:
            headers['Authorization'] = f'Bearer {api_key}'

        if system_message is None:
            system_message = "You are a helpful AI assistant."
        logging.debug(f":Llama Summarize: System Prompt being sent is {system_message}")
        if system_message is None:
            system_message = "You are a helpful AI assistant."

        if custom_prompt is None:
            llama_prompt = f"{summarizer_prompt}\n\n\n\n{text}"
        else:
            llama_prompt = f"{custom_prompt}\n\n\n\n{text}"

        data = {
            "messages": [
                {"role": "system", "content": system_message},
                {"role": "user", "content": llama_prompt}
            ],
            "max_tokens": 4096,
            "temperature": temp
        }

        logging.debug("llama: Submitting request to API endpoint")
        print("llama: Submitting request to API endpoint")
        response = requests.post(api_url, headers=headers, json=data)
        response_data = response.json()
        logging.debug("API Response Data: %s", response_data)

        if response.status_code == 200:
            # if 'X' in response_data:
            logging.debug(response_data)
            summary = response_data['content'].strip()
            logging.debug("llama: Summarization successful")
            print("Summarization successful.")
            return summary
        else:
            logging.error(f"Llama: API request failed with status code {response.status_code}: {response.text}")
            return f"Llama: API request failed: {response.text}"

    except Exception as e:
        logging.error("Llama: Error in processing: %s", str(e))
        return f"Llama: Error occurred while processing summary with llama: {str(e)}"


# https://lite.koboldai.net/koboldcpp_api#/api%2Fv1/post_api_v1_generate
def summarize_with_kobold(input_data, api_key, custom_prompt_input,  system_message=None, temp=None, kobold_api_ip="http://127.0.0.1:5001/api/v1/generate"):
    logging.debug("Kobold: Summarization process starting...")
    try:
        logging.debug("Kobold: Loading and validating configurations")
        loaded_config_data = load_and_log_configs()
        if loaded_config_data is None:
            logging.error("Failed to load configuration data")
            kobold_api_key = None
        else:
            # Prioritize the API key passed as a parameter
            if api_key and api_key.strip():
                kobold_api_key = api_key
                logging.info("Kobold: Using API key provided as parameter")
            else:
                # If no parameter is provided, use the key from the config
                kobold_api_key = loaded_config_data['api_keys'].get('kobold')
                if kobold_api_key:
                    logging.info("Kobold: Using API key from config file")
                else:
                    logging.warning("Kobold: No API key found in config file")

        logging.debug(f"Kobold: Using API Key: {kobold_api_key[:5]}...{kobold_api_key[-5:]}")

        if isinstance(input_data, str) and os.path.isfile(input_data):
            logging.debug("Kobold.cpp: Loading json data for summarization")
            with open(input_data, 'r') as file:
                data = json.load(file)
        else:
            logging.debug("Kobold.cpp: Using provided string data for summarization")
            data = input_data

        logging.debug(f"Kobold.cpp: Loaded data: {data}")
        logging.debug(f"Kobold.cpp: 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("Kobold.cpp: 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("Kobold.cpp: Invalid input data format")

        headers = {
            'accept': 'application/json',
            'content-type': 'application/json',
        }
        if custom_prompt_input is None:
            kobold_prompt = f"{summarizer_prompt}\n\n\n\n{text}"
        else:
            kobold_prompt = f"{custom_prompt_input}\n\n\n\n{text}"

        logging.debug("Kobold summarization: Prompt being sent is {kobold_prompt}")

        # FIXME
        # Values literally c/p from the api docs....
        data = {
            "max_context_length": 8096,
            "max_length": 4096,
            "prompt": kobold_prompt,
            "temperature": 0.7,
            #"top_p": 0.9,
            #"top_k": 100
            #"rep_penalty": 1.0,
        }

        logging.debug("Kobold Summarization: Submitting request to API endpoint")
        print("Kobold Summarization: Submitting request to API endpoint")
        kobold_api_ip = loaded_config_data['local_api_ip']['kobold']
        try:
            response = requests.post(kobold_api_ip, headers=headers, json=data)
            logging.debug("Kobold Summarization: API Response Status Code: %d", response.status_code)

            if response.status_code == 200:
                try:
                    response_data = response.json()
                    logging.debug("kobold: API Response Data: %s", response_data)

                    if response_data and 'results' in response_data and len(response_data['results']) > 0:
                        summary = response_data['results'][0]['text'].strip()
                        logging.debug("kobold: Summarization successful")
                        return summary
                    else:
                        logging.error("Expected data not found in API response.")
                        return "Expected data not found in API response."
                except ValueError as e:
                    logging.error("kobold: Error parsing JSON response: %s", str(e))
                    return f"Error parsing JSON response: {str(e)}"
            else:
                logging.error(f"kobold: API request failed with status code {response.status_code}: {response.text}")
                return f"kobold: API request failed: {response.text}"
        except Exception as e:
            logging.error("kobold: Error in processing: %s", str(e))
            return f"kobold: Error occurred while processing summary with kobold: {str(e)}"
    except Exception as e:
        logging.error("kobold: Error in processing: %s", str(e))
        return f"kobold: Error occurred while processing summary with kobold: {str(e)}"


# https://github.com/oobabooga/text-generation-webui/wiki/12-%E2%80%90-OpenAI-API
def summarize_with_oobabooga(input_data, api_key, custom_prompt, system_message=None, temp=None, api_url="http://127.0.0.1:5000/v1/chat/completions"):
    logging.debug("Oobabooga: Summarization process starting...")
    try:
        logging.debug("Oobabooga: Loading and validating configurations")
        loaded_config_data = load_and_log_configs()
        if loaded_config_data is None:
            logging.error("Failed to load configuration data")
            ooba_api_key = None
        else:
            # Prioritize the API key passed as a parameter
            if api_key and api_key.strip():
                ooba_api_key = api_key
                logging.info("Oobabooga: Using API key provided as parameter")
            else:
                # If no parameter is provided, use the key from the config
                ooba_api_key = loaded_config_data['api_keys'].get('ooba')
                if ooba_api_key:
                    logging.info("Anthropic: Using API key from config file")
                else:
                    logging.warning("Anthropic: No API key found in config file")

        logging.debug(f"Oobabooga: Using API Key: {ooba_api_key[:5]}...{ooba_api_key[-5:]}")

        if isinstance(input_data, str) and os.path.isfile(input_data):
            logging.debug("Oobabooga: Loading json data for summarization")
            with open(input_data, 'r') as file:
                data = json.load(file)
        else:
            logging.debug("Oobabooga: Using provided string data for summarization")
            data = input_data

        logging.debug(f"Oobabooga: Loaded data: {data}")
        logging.debug(f"Oobabooga: 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("Oobabooga: 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")

        headers = {
            'accept': 'application/json',
            'content-type': 'application/json',
        }

        if custom_prompt is None:
            custom_prompt = f"{summarizer_prompt}\n\n\n\n{text}"
        else:
            custom_prompt = f"{custom_prompt}\n\n\n\n{text}"

        logging.debug("Ooba Summarize: Prompt being sent is {kobold_prompt}")

        ooba_prompt = f"{text}" + f"\n\n\n\n{custom_prompt}"
        logging.debug("ooba: Prompt being sent is {ooba_prompt}")

        if system_message is None:
            system_message = "You are a helpful AI assistant."

        data = {
            "mode": "chat",
            "character": "Example",
            "messages": [{"role": "user", "content": ooba_prompt}],
            "system_message": system_message,
        }

        logging.debug("ooba: Submitting request to API endpoint")
        print("ooba: Submitting request to API endpoint")
        response = requests.post(api_url, headers=headers, json=data, verify=False)
        logging.debug("ooba: API Response Data: %s", response)

        if response.status_code == 200:
            response_data = response.json()
            summary = response.json()['choices'][0]['message']['content']
            logging.debug("ooba: Summarization successful")
            print("Summarization successful.")
            return summary
        else:
            logging.error(f"oobabooga: API request failed with status code {response.status_code}: {response.text}")
            return f"ooba: API request failed with status code {response.status_code}: {response.text}"

    except Exception as e:
        logging.error("ooba: Error in processing: %s", str(e))
        return f"ooba: Error occurred while processing summary with oobabooga: {str(e)}"


def summarize_with_tabbyapi(input_data, custom_prompt_input, system_message=None, api_key=None, temp=None, api_IP="http://127.0.0.1:5000/v1/chat/completions"):
    logging.debug("TabbyAPI: Summarization process starting...")
    try:
        logging.debug("TabbyAPI: Loading and validating configurations")
        loaded_config_data = load_and_log_configs()
        if loaded_config_data is None:
            logging.error("Failed to load configuration data")
            tabby_api_key = None
        else:
            # Prioritize the API key passed as a parameter
            if api_key and api_key.strip():
                tabby_api_key = api_key
                logging.info("TabbyAPI: Using API key provided as parameter")
            else:
                # If no parameter is provided, use the key from the config
                tabby_api_key = loaded_config_data['api_keys'].get('tabby')
                if tabby_api_key:
                    logging.info("TabbyAPI: Using API key from config file")
                else:
                    logging.warning("TabbyAPI: No API key found in config file")

        tabby_api_ip = loaded_config_data['local_api_ip']['tabby']
        tabby_model = loaded_config_data['models']['tabby']
        if temp is None:
            temp = 0.7

        logging.debug(f"TabbyAPI: Using API Key: {tabby_api_key[:5]}...{tabby_api_key[-5:]}")

        if isinstance(input_data, str) and os.path.isfile(input_data):
            logging.debug("tabby: Loading json data for summarization")
            with open(input_data, 'r') as file:
                data = json.load(file)
        else:
            logging.debug("tabby: Using provided string data for summarization")
            data = input_data

        logging.debug(f"tabby: Loaded data: {data}")
        logging.debug(f"tabby: 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("tabby: 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")
        if system_message is None:
            system_message = "You are a helpful AI assistant."

        if custom_prompt_input is None:
            custom_prompt_input = f"{summarizer_prompt}\n\n\n\n{text}"
        else:
            custom_prompt_input = f"{custom_prompt_input}\n\n\n\n{text}"

        headers = {
            'Authorization': f'Bearer {api_key}',
            'Content-Type': 'application/json'
        }
        data2 = {
            'max_tokens': 4096,
            "min_tokens": 0,
            'temperature': temp,
            #'top_p': 1.0,
            #'top_k': 0,
            #'frequency_penalty': 0,
            #'presence_penalty': 0.0,
            #"repetition_penalty": 1.0,
            'model': tabby_model,
            'user': custom_prompt_input,
            'messages': input_data
        }

        response = requests.post(tabby_api_ip, headers=headers, json=data2)

        if response.status_code == 200:
            response_json = response.json()

            # Validate the response structure
            if all(key in response_json for key in ['id', 'choices', 'created', 'model', 'object', 'usage']):
                logging.info("TabbyAPI: Received a valid 200 response")
                summary = response_json['choices'][0].get('message', {}).get('content', '')
                return summary
            else:
                logging.error("TabbyAPI: Received a 200 response, but the structure is invalid")
                return "Error: Received an invalid response structure from TabbyAPI."

        elif response.status_code == 422:
            logging.error(f"TabbyAPI: Received a 422 error. Details: {response.json()}")
            return "Error: Invalid request sent to TabbyAPI."

        else:
            response.raise_for_status()  # This will raise an exception for other status codes

    except requests.exceptions.RequestException as e:
        logging.error(f"Error summarizing with TabbyAPI: {e}")
        return f"Error summarizing with TabbyAPI: {str(e)}"
    except json.JSONDecodeError:
        logging.error("TabbyAPI: Received an invalid JSON response")
        return "Error: Received an invalid JSON response from TabbyAPI."
    except Exception as e:
        logging.error(f"Unexpected error in summarize_with_tabbyapi: {e}")
        return f"Unexpected error in summarization process: {str(e)}"

def summarize_with_vllm(
        input_data: Union[str, dict, list],
        custom_prompt_input: str,
        api_key: str = None,
        model: str = None,
        system_prompt: str = None,
        temp: float = 0.7,
        vllm_api_url: str = "http://127.0.0.1:8000/v1/chat/completions"
) -> str:
    logging.debug("vLLM: Summarization process starting...")
    try:
        logging.debug("vLLM: Loading and validating configurations")
        loaded_config_data = load_and_log_configs()
        if loaded_config_data is None:
            logging.error("Failed to load configuration data")
            vllm_api_key = None
        else:
            # Prioritize the API key passed as a parameter
            if api_key and api_key.strip():
                vllm_api_key = api_key
                logging.info("vLLM: Using API key provided as parameter")
            else:
                # If no parameter is provided, use the key from the config
                vllm_api_key = loaded_config_data['api_keys'].get('vllm')
                if vllm_api_key:
                    logging.info("vLLM: Using API key from config file")
                else:
                    logging.warning("vLLM: No API key found in config file")

        logging.debug(f"vLLM: Using API Key: {vllm_api_key[:5]}...{vllm_api_key[-5:]}")
        # Process input data
        if isinstance(input_data, str) and os.path.isfile(input_data):
            logging.debug("vLLM: Loading json data for summarization")
            with open(input_data, 'r') as file:
                data = json.load(file)
        else:
            logging.debug("vLLM: Using provided data for summarization")
            data = input_data

        logging.debug(f"vLLM: Type of data: {type(data)}")

        # Extract text for summarization
        if isinstance(data, dict) and 'summary' in data:
            logging.debug("vLLM: Summary already exists in the loaded data")
            return data['summary']
        elif isinstance(data, list):
            text = extract_text_from_segments(data)
        elif isinstance(data, str):
            text = data
        elif isinstance(data, dict):
            text = json.dumps(data)
        else:
            raise ValueError("Invalid input data format")

        logging.debug(f"vLLM: Extracted text (showing first 500 chars): {text[:500]}...")

        if system_prompt is None:
            system_prompt = "You are a helpful AI assistant."

        if custom_prompt_input is None:
            custom_prompt_input = f"{summarizer_prompt}\n\n\n\n{text}"
        else:
            custom_prompt_input = f"{custom_prompt_input}\n\n\n\n{text}"

        model = model or loaded_config_data['models']['vllm']
        if system_prompt is None:
            system_prompt = "You are a helpful AI assistant."

        # Prepare the API request
        headers = {
            "Content-Type": "application/json"
        }

        payload = {
            "model": model,
            "messages": [
                {"role": "system", "content": system_prompt},
                {"role": "user", "content": f"{custom_prompt_input}\n\n{text}"}
            ]
        }

        # Make the API call
        logging.debug(f"vLLM: Sending request to {vllm_api_url}")
        response = requests.post(vllm_api_url, headers=headers, json=payload)

        # Check for successful response
        response.raise_for_status()

        # Extract and return the summary
        response_data = response.json()
        if 'choices' in response_data and len(response_data['choices']) > 0:
            summary = response_data['choices'][0]['message']['content']
            logging.debug("vLLM: Summarization successful")
            logging.debug(f"vLLM: Summary (first 500 chars): {summary[:500]}...")
            return summary
        else:
            raise ValueError("Unexpected response format from vLLM API")

    except requests.RequestException as e:
        logging.error(f"vLLM: API request failed: {str(e)}")
        return f"Error: vLLM API request failed - {str(e)}"
    except json.JSONDecodeError as e:
        logging.error(f"vLLM: Failed to parse API response: {str(e)}")
        return f"Error: Failed to parse vLLM API response - {str(e)}"
    except Exception as e:
        logging.error(f"vLLM: Unexpected error during summarization: {str(e)}")
        return f"Error: Unexpected error during vLLM summarization - {str(e)}"


def summarize_with_ollama(
    input_data,
    custom_prompt,
    api_url="http://127.0.0.1:11434/v1/chat/completions",
    api_key=None,
    temp=None,
    system_message=None,
    model=None,
    max_retries=5,
    retry_delay=20
):
    try:
        logging.debug("Ollama: Loading and validating configurations")
        loaded_config_data = load_and_log_configs()
        if loaded_config_data is None:
            logging.error("Failed to load configuration data")
            ollama_api_key = None
        else:
            # Prioritize the API key passed as a parameter
            if api_key and api_key.strip():
                ollama_api_key = api_key
                logging.info("Ollama: Using API key provided as parameter")
            else:
                # If no parameter is provided, use the key from the config
                ollama_api_key = loaded_config_data['api_keys'].get('ollama')
                if ollama_api_key:
                    logging.info("Ollama: Using API key from config file")
                else:
                    logging.warning("Ollama: No API key found in config file")

            # Set model from parameter or config
            if model is None:
                model = loaded_config_data['models'].get('ollama')
                if model is None:
                    logging.error("Ollama: Model not found in config file")
                    return "Ollama: Model not found in config file"

            # Set api_url from parameter or config
            if api_url is None:
                api_url = loaded_config_data['local_api_ip'].get('ollama')
                if api_url is None:
                    logging.error("Ollama: API URL not found in config file")
                    return "Ollama: API URL not found in config file"

        # Load transcript
        logging.debug("Ollama: Loading JSON data")
        if isinstance(input_data, str) and os.path.isfile(input_data):
            logging.debug("Ollama: Loading json data for summarization")
            with open(input_data, 'r') as file:
                data = json.load(file)
        else:
            logging.debug("Ollama: Using provided string data for summarization")
            data = input_data

        logging.debug(f"Ollama: Loaded data: {data}")
        logging.debug(f"Ollama: 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("Ollama: 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("Ollama: Invalid input data format")

        headers = {
            'accept': 'application/json',
            'content-type': 'application/json',
        }
        if ollama_api_key and len(ollama_api_key) > 5:
            headers['Authorization'] = f'Bearer {ollama_api_key}'

        ollama_prompt = f"{custom_prompt}\n\n{text}"
        if system_message is None:
            system_message = "You are a helpful AI assistant."
        logging.debug(f"Ollama: Prompt being sent is: {ollama_prompt}")

        data_payload = {
            "model": model,
            "messages": [
                {
                    "role": "system",
                    "content": system_message
                },
                {
                    "role": "user",
                    "content": ollama_prompt
                }
            ],
            'temperature': temp
        }

        for attempt in range(1, max_retries + 1):
            logging.debug("Ollama: Submitting request to API endpoint")
            print("Ollama: Submitting request to API endpoint")
            try:
                response = requests.post(api_url, headers=headers, json=data_payload, timeout=30)
                response.raise_for_status()  # Raises HTTPError for bad responses
                response_data = response.json()
            except requests.exceptions.Timeout:
                logging.error("Ollama: Request timed out.")
                return "Ollama: Request timed out."
            except requests.exceptions.HTTPError as http_err:
                logging.error(f"Ollama: HTTP error occurred: {http_err}")
                return f"Ollama: HTTP error occurred: {http_err}"
            except requests.exceptions.RequestException as req_err:
                logging.error(f"Ollama: Request exception: {req_err}")
                return f"Ollama: Request exception: {req_err}"
            except json.JSONDecodeError:
                logging.error("Ollama: Failed to decode JSON response")
                return "Ollama: Failed to decode JSON response."
            except Exception as e:
                logging.error(f"Ollama: An unexpected error occurred: {str(e)}")
                return f"Ollama: An unexpected error occurred: {str(e)}"

            logging.debug(f"API Response Data: {response_data}")

            if response.status_code == 200:
                # Inspect available keys
                available_keys = list(response_data.keys())
                logging.debug(f"Ollama: Available keys in response: {available_keys}")

                # Attempt to retrieve 'response'
                summary = None
                if 'response' in response_data and response_data['response']:
                    summary = response_data['response'].strip()
                elif 'choices' in response_data and len(response_data['choices']) > 0:
                    choice = response_data['choices'][0]
                    if 'message' in choice and 'content' in choice['message']:
                        summary = choice['message']['content'].strip()

                if summary:
                    logging.debug("Ollama: Chat request successful")
                    print("\n\nChat request successful.")
                    return summary
                elif response_data.get('done_reason') == 'load':
                    logging.warning(f"Ollama: Model is loading. Attempt {attempt} of {max_retries}. Retrying in {retry_delay} seconds...")
                    time.sleep(retry_delay)
                else:
                    logging.error("Ollama: API response does not contain 'response' or 'choices'.")
                    return "Ollama: API response does not contain 'response' or 'choices'."
            else:
                logging.error(f"Ollama: API request failed with status code {response.status_code}: {response.text}")
                return f"Ollama: API request failed: {response.text}"

        logging.error("Ollama: Maximum retry attempts reached. Model is still loading.")
        return "Ollama: Maximum retry attempts reached. Model is still loading."

    except Exception as e:
        logging.error("\n\nOllama: Error in processing: %s", str(e))
        return f"Ollama: Error occurred while processing summary with Ollama: {str(e)}"


# FIXME - update to be a summarize request
def summarize_with_custom_openai(api_key, input_data, custom_prompt_arg, temp=None, system_message=None):
    loaded_config_data = load_and_log_configs()
    custom_openai_api_key = api_key
    try:
        # API key validation
        if not custom_openai_api_key:
            logging.info("Custom OpenAI API: API key not provided as parameter")
            logging.info("Custom OpenAI API: Attempting to use API key from config file")
            custom_openai_api_key = loaded_config_data['api_keys']['custom_openai_api_key']

        if not custom_openai_api_key:
            logging.error("Custom OpenAI API: API key not found or is empty")
            return "Custom OpenAI API: API Key Not Provided/Found in Config file or is empty"

        logging.debug(f"Custom OpenAI API: Using API Key: {custom_openai_api_key[:5]}...{custom_openai_api_key[-5:]}")

        # Input data handling
        logging.debug(f"Custom OpenAI API: Raw input data type: {type(input_data)}")
        logging.debug(f"Custom OpenAI API: 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("Custom OpenAI API: Parsing provided JSON string data for summarization")
                try:
                    data = json.loads(input_data)
                except json.JSONDecodeError as e:
                    logging.error(f"Custom OpenAI API: Error parsing JSON string: {str(e)}")
                    return f"Custom OpenAI API: Error parsing JSON input: {str(e)}"
            elif os.path.isfile(input_data):
                logging.debug("Custom OpenAI API: Loading JSON data from file for summarization")
                with open(input_data, 'r') as file:
                    data = json.load(file)
            else:
                logging.debug("Custom OpenAI API: Using provided string data for summarization")
                data = input_data
        else:
            data = input_data

        logging.debug(f"Custom OpenAI API: Processed data type: {type(data)}")
        logging.debug(f"Custom OpenAI API: Processed data (first 500 chars): {str(data)[:500]}...")

        # Text extraction
        if isinstance(data, dict):
            if 'summary' in data:
                logging.debug("Custom OpenAI API: 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"Custom OpenAI API: Invalid input data format: {type(data)}")

        logging.debug(f"Custom OpenAI API: Extracted text (first 500 chars): {text[:500]}...")
        logging.debug(f"v: Custom prompt: {custom_prompt_arg}")

        if input_data is None:
            input_data = f"{summarizer_prompt}\n\n\n\n{text}"
        else:
            input_data = f"{input_data}\n\n\n\n{text}"

        openai_model = loaded_config_data['models']['openai'] or "gpt-4o"
        logging.debug(f"Custom OpenAI API: Using model: {openai_model}")

        headers = {
            'Authorization': f'Bearer {custom_openai_api_key}',
            'Content-Type': 'application/json'
        }

        logging.debug(
            f"OpenAI API Key: {custom_openai_api_key[:5]}...{custom_openai_api_key[-5:] if custom_openai_api_key else None}")
        logging.debug("Custom OpenAI API: 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
        }

        custom_openai_url = loaded_config_data['Local_api_ip']['custom_openai_api_ip']

        logging.debug("Custom OpenAI API: Posting request")
        response = requests.post(custom_openai_url, headers=headers, json=data)
        logging.debug(f"Custom OpenAI API full API response data: {response}")
        if response.status_code == 200:
            response_data = response.json()
            logging.debug(response_data)
            if 'choices' in response_data and len(response_data['choices']) > 0:
                chat_response = response_data['choices'][0]['message']['content'].strip()
                logging.debug("Custom OpenAI API: Chat Sent successfully")
                logging.debug(f"Custom OpenAI API: Chat response: {chat_response}")
                return chat_response
            else:
                logging.warning("Custom OpenAI API: Chat response not found in the response data")
                return "Custom OpenAI API: Chat not available"
        else:
            logging.error(f"Custom OpenAI API: Chat request failed with status code {response.status_code}")
            logging.error(f"Custom OpenAI API: Error response: {response.text}")
            return f"OpenAI: Failed to process chat response. Status code: {response.status_code}"
    except json.JSONDecodeError as e:
        logging.error(f"Custom OpenAI API: Error decoding JSON: {str(e)}", exc_info=True)
        return f"Custom OpenAI API: Error decoding JSON input: {str(e)}"
    except requests.RequestException as e:
        logging.error(f"Custom OpenAI API: Error making API request: {str(e)}", exc_info=True)
        return f"Custom OpenAI API: Error making API request: {str(e)}"
    except Exception as e:
        logging.error(f"Custom OpenAI API: Unexpected error: {str(e)}", exc_info=True)
        return f"Custom OpenAI API: Unexpected error occurred: {str(e)}"


def save_summary_to_file(summary, file_path):
    logging.debug("Now saving summary to file...")
    base_name = os.path.splitext(os.path.basename(file_path))[0]
    summary_file_path = os.path.join(os.path.dirname(file_path), base_name + '_summary.txt')
    os.makedirs(os.path.dirname(summary_file_path), exist_ok=True)
    logging.debug("Opening summary file for writing, *segments.json with *_summary.txt")
    with open(summary_file_path, 'w') as file:
        file.write(summary)
    logging.info(f"Summary saved to file: {summary_file_path}")

#
#
#######################################################################################################################