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import os | |
import cv2 | |
import numpy as np | |
from PIL import Image | |
import pytesseract | |
import gradio as gr | |
from pdf2image import convert_from_path | |
import PyPDF2 | |
from llama_index.core import VectorStoreIndex, Document | |
from llama_index.embeddings.openai import OpenAIEmbedding | |
from llama_index.llms.openai import OpenAI | |
from llama_index.core import get_response_synthesizer | |
from sentence_transformers import SentenceTransformer, util | |
import logging | |
from openai_tts_tool import generate_audio_and_text | |
import tempfile | |
# [Previous imports and initialization code remains the same...] | |
def create_summary_file(summary_text): | |
"""Create a downloadable file from the summary text""" | |
if not summary_text: | |
return None | |
temp_dir = os.path.join(os.getcwd(), 'temp') | |
if not os.path.exists(temp_dir): | |
os.makedirs(temp_dir) | |
# Create a unique filename | |
summary_file = os.path.join(temp_dir, f"summary_{hash(summary_text)}.txt") | |
with open(summary_file, 'w', encoding='utf-8') as f: | |
f.write(summary_text) | |
return summary_file | |
def query_app(query, model_name, use_similarity_check, api_key): | |
global vector_index, query_log | |
if vector_index is None: | |
return "No documents indexed yet. Please upload documents first.", None | |
if not api_key: | |
return "Please provide a valid OpenAI API Key.", None | |
try: | |
llm = OpenAI(model=model_name, api_key=api_key) | |
response_synthesizer = get_response_synthesizer(llm=llm) | |
query_engine = vector_index.as_query_engine(llm=llm, response_synthesizer=response_synthesizer) | |
response = query_engine.query(query) | |
generated_response = response.response | |
# Return both the response and the same response (to update the text generation input) | |
return generated_response, generated_response | |
except Exception as e: | |
logging.error(f"Error during query processing: {e}") | |
return f"Error during query processing: {str(e)}", None | |
def create_gradio_interface(): | |
with gr.Blocks(title="Document Processing and TTS App") as demo: | |
gr.Markdown("# π Document Processing, Text & Audio Generation App") | |
# Store API key at the top level to share across tabs | |
api_key_input = gr.Textbox( | |
label="Enter OpenAI API Key", | |
placeholder="Paste your OpenAI API Key here", | |
type="password" | |
) | |
with gr.Tab("π€ Upload Documents"): | |
file_upload = gr.File(label="Upload Files", file_count="multiple", type="filepath") | |
lang_dropdown = gr.Dropdown(choices=langs, label="Select OCR Language", value='eng') | |
upload_button = gr.Button("Upload and Index") | |
upload_status = gr.Textbox(label="Status", interactive=False) | |
with gr.Tab("β Ask a Question"): | |
query_input = gr.Textbox(label="Enter your question") | |
model_dropdown = gr.Dropdown( | |
choices=["gpt-4-0125-preview", "gpt-3.5-turbo-0125"], | |
label="Select Model", | |
value="gpt-3.5-turbo-0125" | |
) | |
similarity_checkbox = gr.Checkbox(label="Use Similarity Check", value=False) | |
query_button = gr.Button("Ask") | |
answer_output = gr.Textbox(label="Answer", interactive=False) | |
with gr.Tab("π£οΈ Generate Audio and Text"): | |
text_input = gr.Textbox(label="Enter text for generation") | |
voice_type = gr.Dropdown( | |
choices=["alloy", "echo", "fable", "onyx", "nova", "shimmer"], | |
label="Voice Type", | |
value="alloy" | |
) | |
voice_speed = gr.Slider( | |
minimum=0.25, | |
maximum=4.0, | |
value=1.0, | |
label="Voice Speed" | |
) | |
language = gr.Dropdown( | |
choices=["en", "ar", "de", "hi", "es", "fr", "it", "ja", "ko", "pt"], | |
label="Language", | |
value="en" | |
) | |
output_option = gr.Radio( | |
choices=["audio", "summary_text", "both"], | |
label="Output Option", | |
value="both" | |
) | |
summary_length = gr.Slider( | |
minimum=50, | |
maximum=500, | |
value=100, | |
step=10, | |
label="Summary Length (words)" | |
) | |
additional_prompt = gr.Textbox(label="Additional Prompt (Optional)") | |
generate_button = gr.Button("Generate") | |
with gr.Row(): | |
audio_output = gr.Audio(label="Generated Audio") | |
summary_output = gr.File(label="Generated Summary Text") | |
# Wire up the components | |
upload_button.click( | |
fn=process_upload, | |
inputs=[api_key_input, file_upload, lang_dropdown], | |
outputs=[upload_status] | |
) | |
# Modified to update both answer output and text generation input | |
query_button.click( | |
fn=query_app, | |
inputs=[query_input, model_dropdown, similarity_checkbox, api_key_input], | |
outputs=[answer_output, text_input] # Now updates both outputs | |
) | |
# Modified to handle file output | |
def process_generation(*args): | |
audio_file, summary_text = generate_audio_and_text(*args) | |
summary_file = create_summary_file(summary_text) if summary_text else None | |
return audio_file, summary_file | |
generate_button.click( | |
fn=process_generation, | |
inputs=[ | |
api_key_input, text_input, model_dropdown, voice_type, | |
voice_speed, language, output_option, summary_length, | |
additional_prompt | |
], | |
outputs=[audio_output, summary_output] | |
) | |
return demo | |
if __name__ == "__main__": | |
demo = create_gradio_interface() | |
demo.launch() | |
else: | |
demo = create_gradio_interface() |