app.py

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 dotenv import load_dotenv
from sentence_transformers import SentenceTransformer, util
import logging
from openai_tts_tool import generate_audio_and_text  # Importing from openai_tts_tool

# Set up logging configuration
logging.basicConfig(level=logging.INFO, format='%(asctime)s | %(levelname)s | %(message)s')

# Load environment variables from .env file
load_dotenv()

# Initialize global variables
vector_index = None
query_log = []
sentence_model = SentenceTransformer('all-MiniLM-L6-v2')

langs = os.popen('tesseract --list-langs').read().split('\n')[1:-1]

# Preprocessing function
def preprocess_image(image_path):
    img = cv2.imread(image_path)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    gray = cv2.equalizeHist(gray)
    gray = cv2.GaussianBlur(gray, (5, 5), 0)
    processed_image = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
                                            cv2.THRESH_BINARY, 11, 2)
    temp_filename = "processed_image.png"
    cv2.imwrite(temp_filename, processed_image)
    return temp_filename

# Function to extract text from images
def extract_text_from_image(image_path, lang='eng'):
    processed_image_path = preprocess_image(image_path)
    text = pytesseract.image_to_string(Image.open(processed_image_path), lang=lang)
    return text

# Function to extract text from PDFs
def extract_text_from_pdf(pdf_path, lang='eng'):
    text = ""
    try:
        with open(pdf_path, 'rb') as file:
            pdf_reader = PyPDF2.PdfReader(file)
            for page_num in range(len(pdf_reader.pages)):
                page = pdf_reader.pages[page_num]
                page_text = page.extract_text()
                if page_text.strip():
                    text += page_text
                else:
                    images = convert_from_path(pdf_path, first_page=page_num + 1, last_page=page_num + 1)
                    for image in images:
                        image.save('temp_image.png', 'PNG')
                        text += extract_text_from_image('temp_image.png', lang=lang)
                        text += f"\n[OCR applied on page {page_num + 1}]\n"
    except Exception as e:
        return f"Error processing PDF: {str(e)}"
    return text

# General function to handle different file types
def extract_text(file_path, lang='eng'):
    file_ext = file_path.lower().split('.')[-1]
    if file_ext in ['pdf']:
        return extract_text_from_pdf(file_path, lang)
    elif file_ext in ['png', 'jpg', 'jpeg']:
        return extract_text_from_image(file_path, lang)
    else:
        return f"Unsupported file type: {file_ext}"

# Process uploaded documents and index them
def process_upload(api_key, files, lang):
    global vector_index

    if not api_key:
        return "Please provide a valid OpenAI API Key.", None

    if not files:
        return "No files uploaded.", None

    documents = []
    error_messages = []
    image_heavy_docs = []

    for file_path in files:
        try:
            text = extract_text(file_path, lang)
            if "This document consists of" in text and "page(s) of images" in text:
                image_heavy_docs.append(os.path.basename(file_path))
            documents.append(Document(text=text))
        except Exception as e:
            error_message = f"Error processing file {file_path}: {str(e)}"
            logging.error(error_message)
            error_messages.append(error_message)

    if documents:
        try:
            embed_model = OpenAIEmbedding(model="text-embedding-3-large", api_key=api_key)
            vector_index = VectorStoreIndex.from_documents(documents, embed_model=embed_model)
            
            success_message = f"Successfully indexed {len(documents)} files."
            if image_heavy_docs:
                success_message += f"\nNote: The following documents consist mainly of images and may require manual review: {', '.join(image_heavy_docs)}"
            if error_messages:
                success_message += f"\nErrors: {'; '.join(error_messages)}"
            
            return success_message, vector_index
        except Exception as e:
            return f"Error creating index: {str(e)}", None
    else:
        return f"No valid documents were indexed. Errors: {'; '.join(error_messages)}", None

# Function to calculate similarity
def calculate_similarity(response, ground_truth):
    response_embedding = sentence_model.encode(response, convert_to_tensor=True)
    truth_embedding = sentence_model.encode(ground_truth, convert_to_tensor=True)

    response_embedding = response_embedding / np.linalg.norm(response_embedding)
    truth_embedding = truth_embedding / np.linalg.norm(truth_embedding)

    similarity = np.dot(response_embedding, truth_embedding)
    similarity_percentage = (similarity + 1) / 2 * 100

    return similarity_percentage

# Function to query documents
def query_app(query, model_name, use_similarity_check, openai_api_key):
    global vector_index, query_log

    if vector_index is None:
        logging.error("No documents indexed yet. Please upload documents first.")
        return "No documents indexed yet. Please upload documents first.", None

    if not openai_api_key:
        logging.error("No OpenAI API Key provided.")
        return "Please provide a valid OpenAI API Key.", None

    try:
        llm = OpenAI(model=model_name, api_key=openai_api_key)
    except Exception as e:
        logging.error(f"Error initializing the OpenAI model: {e}")
        return f"Error initializing the OpenAI model: {e}", None

    response_synthesizer = get_response_synthesizer(llm=llm)
    query_engine = vector_index.as_query_engine(llm=llm, response_synthesizer=response_synthesizer)

    try:
        response = query_engine.query(query)
    except Exception as e:
        logging.error(f"Error during query processing: {e}")
        return f"Error during query processing: {e}", None

    generated_response = response.response
    query_log.append({
        "query_id": str(len(query_log) + 1),
        "query": query,
        "gt_answer": "Placeholder ground truth answer",
        "response": generated_response,
        "retrieved_context": [{"text": doc.text} for doc in response.source_nodes]
    })

    metrics = {}

    if use_similarity_check:
        try:
            logging.info("Similarity check is enabled. Calculating similarity.")
            similarity = calculate_similarity(generated_response, "Placeholder ground truth answer")
            metrics['similarity'] = similarity
            logging.info(f"Similarity calculated: {similarity}")
        except Exception as e:
            logging.error(f"Error during similarity calculation: {e}")
            metrics['error'] = f"Error during similarity calculation: {e}"

    return generated_response, metrics if use_similarity_check else None

# Function to generate audio and text (integrating from openai_tts_tool.py)
def process_tts(api_key, input_text, model_name, voice_type, voice_speed, language, output_option, summary_length, additional_prompt):
    try:
        return generate_audio_and_text(api_key, input_text, model_name, voice_type, voice_speed, language, output_option, summary_length, additional_prompt)
    except Exception as e:
        logging.error(f"Error during TTS generation: {e}")
        return f"Error during TTS generation: {e}", None

# Main function with Gradio interface
def main():
    with gr.Blocks(title="Document Processing and TTS App") as demo:
        gr.Markdown("# 📄 Document Processing, Text & Audio Generation App")

        # Upload documents and chat functionality
        with gr.Tab("📤 Upload Documents"):
            api_key_input = gr.Textbox(label="Enter OpenAI API Key", placeholder="Paste your OpenAI API Key here")
            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)
            upload_button.click(fn=process_upload, inputs=[api_key_input, file_upload, lang_dropdown], outputs=[upload_status])

        # Chat with document
        with gr.Tab("❓ Ask a Question"):
            query_input = gr.Textbox(label="Enter your question")
            model_dropdown = gr.Dropdown(choices=["gpt-4o", "gpt-4o-mini"], label="Select Model", value="gpt-4o")
            similarity_checkbox = gr.Checkbox(label="Use Similarity Check", value=False)
            query_button = gr.Button("Ask")
            answer_output = gr.Textbox(label="Answer", interactive=False)
            metrics_output = gr.JSON(label="Metrics")
            query_button.click(fn=query_app, inputs=[query_input, model_dropdown, similarity_checkbox, api_key_input], outputs=[answer_output, metrics_output])

        # Text-to-Speech generation
        with gr.Tab("🗣️ Generate Audio and Text"):
            text_input = gr.Textbox(label="Enter text for generation")
            voice_type = gr.Dropdown(choices=["alloy", "echo", "fable", "onyx"], label="Voice Type", value="alloy")
            voice_speed = gr.Dropdown(choices=["normal", "slow", "fast"], label="Voice Speed", value="normal")
            language = gr.Dropdown(choices=["en", "ar", "de", "hi"], label="Language", value="en")
            output_option = gr.Radio(choices=["audio", "summary_text", "both"], label="Output Option", value="both")
            summary_length = gr.Number(label="Summary Length", value=100)
            additional_prompt = gr.Textbox(label="Additional Prompt (Optional)")
            generate_button = gr.Button("Generate")
            audio_output = gr.Audio(label="Generated Audio", interactive=False)
            summary_output = gr.Textbox(label="Generated Summary Text", interactive=False)

            generate_button.click(fn=process_tts, inputs=[api_key_input, text_input, model_dropdown, voice_type, voice_speed, language, output_option, summary_length, additional_prompt], outputs=[audio_output, summary_output])

    demo.launch()

if __name__ == "__main__":
    main()