app.py

import fitz  # PyMuPDF
import gradio as gr
import requests
from bs4 import BeautifulSoup
import urllib.parse
import random
import os
from dotenv import load_dotenv

load_dotenv()  # Load environment variables from .env file

# Now replace the hard-coded token with the environment variable
HUGGINGFACE_API_TOKEN = os.getenv("HUGGINGFACE_TOKEN")

_useragent_list = [
    "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36",
    "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36",
    "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Edge/91.0.864.59 Safari/537.36",
    "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Edge/91.0.864.59 Safari/537.36",
    "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Safari/537.36",
    "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Safari/537.36",
]

# Function to extract visible text from HTML content of a webpage
def extract_text_from_webpage(html):
    print("Extracting text from webpage...")
    soup = BeautifulSoup(html, 'html.parser')
    for script in soup(["script", "style"]):
        script.extract()  # Remove scripts and styles
    text = soup.get_text()
    lines = (line.strip() for line in text.splitlines())
    chunks = (phrase.strip() for line in lines for phrase in line.split("  "))
    text = '\n'.join(chunk for chunk in chunks if chunk)
    print(f"Extracted text length: {len(text)}")
    return text

# Function to perform a Google search and retrieve results
def google_search(term, num_results=5, lang="en", timeout=5, safe="active", ssl_verify=None):
    """Performs a Google search and returns the results."""
    print(f"Searching for term: {term}")
    escaped_term = urllib.parse.quote_plus(term)
    start = 0
    all_results = []
    max_chars_per_page = 8000  # Limit the number of characters from each webpage to stay under the token limit
    
    with requests.Session() as session:
        while start < num_results:
            print(f"Fetching search results starting from: {start}")
            try:
                # Choose a random user agent
                user_agent = random.choice(_useragent_list)
                headers = {
                    'User-Agent': user_agent
                }
                print(f"Using User-Agent: {headers['User-Agent']}")
                
                resp = session.get(
                    url="https://www.google.com/search",
                    headers=headers,
                    params={
                        "q": term,
                        "num": num_results - start,
                        "hl": lang,
                        "start": start,
                        "safe": safe,
                    },
                    timeout=timeout,
                    verify=ssl_verify,
                )
                resp.raise_for_status()
            except requests.exceptions.RequestException as e:
                print(f"Error fetching search results: {e}")
                break
            
            soup = BeautifulSoup(resp.text, "html.parser")
            result_block = soup.find_all("div", attrs={"class": "g"})
            if not result_block:
                print("No more results found.")
                break
            for result in result_block:
                link = result.find("a", href=True)
                if link:
                    link = link["href"]
                    print(f"Found link: {link}")
                    try:
                        webpage = session.get(link, headers=headers, timeout=timeout)
                        webpage.raise_for_status()
                        visible_text = extract_text_from_webpage(webpage.text)
                        if len(visible_text) > max_chars_per_page:
                            visible_text = visible_text[:max_chars_per_page] + "..."
                        all_results.append({"link": link, "text": visible_text})
                    except requests.exceptions.RequestException as e:
                        print(f"Error fetching or processing {link}: {e}")
                        all_results.append({"link": link, "text": None})
                else:
                    print("No link found in result.")
                    all_results.append({"link": None, "text": None})
            start += len(result_block)
    print(f"Total results fetched: {len(all_results)}")
    return all_results

# Function to format the prompt for the Hugging Face API
def format_prompt(query, search_results, instructions):
    formatted_results = ""
    for result in search_results:
        link = result["link"]
        text = result["text"]
        if link:
            formatted_results += f"URL: {link}\nContent: {text}\n{'-'*80}\n"
        else:
            formatted_results += "No link found.\n" + '-'*80 + '\n'

    prompt = f"{instructions}User Query: {query}\n\nWeb Search Results:\n{formatted_results}\n\nAssistant:"
    return prompt

# Function to generate text using Hugging Face API
def generate_text(input_text, temperature=0.7, repetition_penalty=1.0, top_p=0.9):
    print("Generating text using Hugging Face API...")
    endpoint = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.3"
    headers = {
        "Authorization": f"Bearer {HUGGINGFACE_API_TOKEN}",  # Use the environment variable
        "Content-Type": "application/json"
    }
    data = {
        "inputs": input_text,
        "parameters": {
            "max_new_tokens": 8000,  # Adjust as needed
            "temperature": temperature,
            "repetition_penalty": repetition_penalty,
            "top_p": top_p
        }
    }

    try:
        response = requests.post(endpoint, headers=headers, json=data)
        response.raise_for_status()

        # Check if response is JSON
        try:
            json_data = response.json()
        except ValueError:
            print("Response is not JSON.")
            return None

        # Extract generated text from response JSON
        if isinstance(json_data, list):
            # Handle list response (if applicable for your use case)
            generated_text = json_data[0].get("generated_text") if json_data else None
        elif isinstance(json_data, dict):
            # Handle dictionary response
            generated_text = json_data.get("generated_text")
        else:
            print("Unexpected response format.")
            return None

        if generated_text is not None:
            print("Text generation complete using Hugging Face API.")
            print(f"Generated text: {generated_text}")  # Debugging line
            return generated_text
        else:
            print("Generated text not found in response.")
            return None

    except requests.exceptions.RequestException as e:
        print(f"Error generating text using Hugging Face API: {e}")
        return None

# Function to read and extract text from a PDF
def read_pdf(file_obj):
    with fitz.open(file_obj.name) as document:
        text = ""
        for page_num in range(document.page_count):
            page = document.load_page(page_num)
            text += page.get_text()
        return text

# Function to format the prompt with instructions for text generation
def format_prompt_with_instructions(text, instructions):
    prompt = f"{instructions}{text}\n\nAssistant:"
    return prompt

# Function to save text to a PDF
def save_text_to_pdf(text, output_path):
    print(f"Saving text to PDF at {output_path}...")
    doc = fitz.open()  # Create a new PDF document
    page = doc.new_page()  # Create a new page

    # Set the page margins
    margin = 50  # 50 points margin
    page_width = page.rect.width
    page_height = page.rect.height
    text_width = page_width - 2 * margin
    text_height = page_height - 2 * margin

    # Define font size and line spacing
    font_size = 9
    line_spacing = 1 * font_size
    fontname = "times-roman"  # Use a supported font name

    # Process the text to handle line breaks and paragraphs
    paragraphs = text.split("\n")  # Split text into paragraphs
    y_position = margin

    for paragraph in paragraphs:
        words = paragraph.split()
        current_line = ""

        for word in words:
            word = str(word)  # Ensure word is treated as string
            # Calculate the length of the current line plus the new word
            current_line_length = fitz.get_text_length(current_line + " " + word, fontsize=font_size, fontname=fontname)
            if current_line_length <= text_width:
                current_line += " " + word
            else:
                page.insert_text(fitz.Point(margin, y_position), current_line.strip(), fontsize=font_size, fontname=fontname)
                y_position += line_spacing
                if y_position + line_spacing > page_height - margin:
                    page = doc.new_page()  # Add a new page if text exceeds page height
                    y_position = margin
                current_line = word

        # Add the last line of the paragraph
        page.insert_text(fitz.Point(margin, y_position), current_line.strip(), fontsize=font_size, fontname=fontname)
        y_position += line_spacing

        # Add extra space for new paragraph
        y_position += line_spacing
        if y_position + line_spacing > page_height - margin:
            page = doc.new_page()  # Add a new page if text exceeds page height
            y_position = margin

    doc.save(output_path)  # Save the PDF to the specified path
    print("PDF saved successfully.")




# Integrated function to perform web scraping, formatting, and text generation
def scrape_and_display(query, num_results, instructions, web_search=True, temperature=0.7, repetition_penalty=1.0, top_p=0.9):
    print(f"Scraping and displaying results for query: {query} with num_results: {num_results}")
    if web_search:
        search_results = google_search(query, num_results)
        formatted_prompt = format_prompt(query, search_results, instructions)
        generated_summary = generate_text(formatted_prompt, temperature=temperature, repetition_penalty=repetition_penalty, top_p=top_p)
    else:
        formatted_prompt = format_prompt_with_instructions(query, instructions)
        generated_summary = generate_text(formatted_prompt, temperature=temperature, repetition_penalty=repetition_penalty, top_p=top_p)
    print("Scraping and display complete.")
    if generated_summary:
        # Extract and return text starting from "Assistant:"
        assistant_index = generated_summary.find("Assistant:")
        if assistant_index != -1:
            generated_summary = generated_summary[assistant_index:]
        else:
            generated_summary = "Assistant: No response generated."
    print(f"Generated summary: {generated_summary}")  # Debugging line
    return generated_summary

# Main Gradio interface function
def gradio_interface(query, use_pdf, pdf, num_results, instructions, temperature, repetition_penalty, top_p):
    if use_pdf and pdf is not None:
        pdf_text = read_pdf(pdf)
        generated_summary = scrape_and_display(pdf_text, num_results=0, instructions=instructions, web_search=False, temperature=temperature, repetition_penalty=repetition_penalty, top_p=top_p)
    else:
        generated_summary = scrape_and_display(query, num_results=num_results, instructions=instructions, web_search=True, temperature=temperature, repetition_penalty=repetition_penalty, top_p=top_p)
    
    # Save the generated summary to a PDF
    output_pdf_path = "output_summary.pdf"
    save_text_to_pdf(generated_summary, output_pdf_path)
    
    return generated_summary, output_pdf_path

# Deploy Gradio Interface
gr.Interface(
    fn=gradio_interface,
    inputs=[
        gr.Textbox(label="Query"),
        gr.Checkbox(label="Use PDF"),
        gr.File(label="Upload PDF"),
        gr.Slider(minimum=1, maximum=20, label="Number of Results"),  # Added Slider for num_results
        gr.Textbox(label="Instructions"),
        gr.Slider(minimum=0.1, maximum=1.0, label="Temperature"),
        gr.Slider(minimum=0.1, maximum=1.0, label="Repetition Penalty"),
        gr.Slider(minimum=0.1, maximum=1.0, label="Top p")
    ],
    outputs=["text", "file"],  # Updated to return text and a file
    title="Financial Analyst AI Assistant",
    description="Enter your query about a company's financials to get valuable insights. Optionally, upload a PDF for analysis.Please instruct me for curating your output template, also for web search you can modify my search results but its advisable to restrict the same at 10. You can also adjust my parameters like Temperature, Repetition Penalty and Top_P, its adivsable to set repetition penalty at 1 and other two parameters at 0.1.",
).launch(share=True)