app.py

import os
import re
import tempfile
import requests
import gradio as gr
from PyPDF2 import PdfReader
import logging
import webbrowser
from huggingface_hub import InferenceClient
from typing import Dict, List, Optional, Tuple
import time

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

# Constants
CONTEXT_SIZES = {
    "4K": 4000,
    "8K": 8000,
    "32K": 32000,
    "128K": 128000,
    "200K": 200000
}

class ModelRegistry:
    def __init__(self):
        self.hf_models = {
            "Phi-3 Mini 128k": "microsoft/Phi-3-mini-128k-instruct",
            "Custom Model": ""
        }
        self.groq_models = self._fetch_groq_models()

    def _fetch_groq_models(self) -> Dict[str, str]:
        """Fetch available Groq models"""
        try:
            headers = {
                "Authorization": f"Bearer {os.getenv('GROQ_API_KEY')}",
                "Content-Type": "application/json"
            }
            response = requests.get("https://api.groq.com/openai/v1/models", headers=headers)
            if response.status_code == 200:
                models = response.json().get("data", [])
                return {model["id"]: model["id"] for model in models}
            else:
                logging.error(f"Failed to fetch Groq models: {response.status_code}")
                return self._get_default_groq_models()
        except Exception as e:
            logging.error(f"Error fetching Groq models: {e}")
            return self._get_default_groq_models()

    def _get_default_groq_models(self) -> Dict[str, str]:
        """Return default Groq models when API is unavailable"""
        return {
            "llama-3.1-70b-versatile": "llama-3.1-70b-versatile",
            "mixtral-8x7b-32768": "mixtral-8x7b-32768",
            "llama-3.1-8b-instant": "llama-3.1-8b-instant"
        }

    def refresh_groq_models(self) -> Dict[str, str]:
        """Refresh the list of available Groq models"""
        self.groq_models = self._fetch_groq_models()
        return self.groq_models

# Initialize model registry
model_registry = ModelRegistry()

def extract_text_from_pdf(pdf_path: str) -> str:
    """Extract text content from PDF file."""
    try:
        reader = PdfReader(pdf_path)
        text = ""
        for page_num, page in enumerate(reader.pages, start=1):
            page_text = page.extract_text()
            if page_text:
                text += page_text + "\n"
            else:
                logging.warning(f"No text found on page {page_num}.")
        if not text.strip():
            return "Error: No extractable text found in the PDF."
        return text
    except Exception as e:
        logging.error(f"Error reading PDF file: {e}")
        return f"Error reading PDF file: {e}"

def format_content(text: str, format_type: str) -> str:
    """Format extracted text according to specified format."""
    if format_type == 'txt':
        return text
    elif format_type == 'md':
        paragraphs = text.split('\n\n')
        return '\n\n'.join(paragraphs)
    elif format_type == 'html':
        paragraphs = text.split('\n\n')
        return ''.join([f'<p>{para.strip()}</p>' for para in paragraphs if para.strip()])
    else:
        logging.error(f"Unsupported format: {format_type}")
        return f"Unsupported format: {format_type}"

def split_into_snippets(text: str, context_size: int) -> List[str]:
    """Split text into manageable snippets based on context size."""
    sentences = re.split(r'(?<=[.!?]) +', text)
    snippets = []
    current_snippet = ""

    for sentence in sentences:
        if len(current_snippet) + len(sentence) + 1 > context_size:
            if current_snippet:
                snippets.append(current_snippet.strip())
                current_snippet = sentence + " "
            else:
                snippets.append(sentence.strip())
                current_snippet = ""
        else:
            current_snippet += sentence + " "

    if current_snippet.strip():
        snippets.append(current_snippet.strip())

    return snippets

def build_prompts(snippets: List[str], prompt_instruction: str, custom_prompt: Optional[str], snippet_num: Optional[int] = None) -> str:
    """Build formatted prompts from text snippets."""
    if snippet_num is not None:
        if 1 <= snippet_num <= len(snippets):
            selected_snippets = [snippets[snippet_num - 1]]
        else:
            return f"Error: Invalid snippet number. Please choose between 1 and {len(snippets)}."
    else:
        selected_snippets = snippets

    prompts = []
    base_prompt = custom_prompt if custom_prompt else prompt_instruction
    
    for idx, snippet in enumerate(selected_snippets, start=1):
        if len(selected_snippets) > 1:
            prompt_header = f"{base_prompt} Part {idx} of {len(selected_snippets)}: ---\n"
        else:
            prompt_header = f"{base_prompt} ---\n"
        
        framed_prompt = f"{prompt_header}{snippet}\n---"
        prompts.append(framed_prompt)
    
    return "\n\n".join(prompts)

def send_to_hf_inference(prompt: str, model_name: str, api_key: str) -> str:
    """Send prompt to HuggingFace using Inference API"""
    try:
        client = InferenceClient(api_key=api_key)
        messages = [{"role": "user", "content": prompt}]
        completion = client.chat.completions.create(
            model=model_name,
            messages=messages,
            max_tokens=500
        )
        return completion.choices[0].message.content
    except Exception as e:
        logging.error(f"Error with HF inference: {e}")
        return f"Error with HF inference: {e}"

def send_to_groq(prompt: str, model_name: str, api_key: str) -> str:
    """Send prompt to Groq API"""
    try:
        headers = {
            "Authorization": f"Bearer {api_key}",
            "Content-Type": "application/json"
        }
        data = {
            "model": model_name,
            "messages": [{"role": "user", "content": prompt}]
        }
        response = requests.post(
            "https://api.groq.com/openai/v1/chat/completions",
            headers=headers,
            json=data
        )
        return response.json()["choices"][0]["message"]["content"]
    except Exception as e:
        logging.error(f"Error with Groq API: {e}")
        return f"Error with Groq API: {e}"

def copy_to_clipboard(text: str) -> str:
    """Copy text to clipboard"""
    return "Text copied to clipboard!"

def open_chatgpt() -> str:
    """Open ChatGPT in browser"""
    webbrowser.open('https://chat.openai.com/')
    return "Opening ChatGPT in browser..."

def process_pdf(pdf, fmt, ctx_size, snippet_num, prompt, model_selection, 
                hf_model_choice, hf_custom_model, hf_api_key,
                groq_model_choice, groq_api_key) -> Tuple[str, str, str, List[str]]:
    """Process PDF and generate summary"""
    try:
        if not pdf:
            return "Please upload a PDF file.", "", "", []
        
        # Extract text
        text = extract_text_from_pdf(pdf.name)
        if text.startswith("Error"):
            return text, "", "", []
        
        # Format content
        formatted_text = format_content(text, fmt)
        
        # Split into snippets
        snippets = split_into_snippets(formatted_text, ctx_size)
        
        # Build prompts
        default_prompt = "Summarize the following text:"
        full_prompt = build_prompts(snippets, default_prompt, prompt, snippet_num)
        
        if isinstance(full_prompt, str) and full_prompt.startswith("Error"):
            return full_prompt, "", "", []
        
        # Process with selected model
        if model_selection == "HuggingFace Inference":
            if not hf_api_key:
                return "HuggingFace API key required.", full_prompt, "", []
            
            model_id = hf_custom_model if hf_model_choice == "Custom Model" else model_registry.hf_models[hf_model_choice]
            summary = send_to_hf_inference(full_prompt, model_id, hf_api_key)
            
        elif model_selection == "Groq API":
            if not groq_api_key:
                return "Groq API key required.", full_prompt, "", []
                
            summary = send_to_groq(full_prompt, groq_model_choice, groq_api_key)
            
        else:  # OpenAI ChatGPT
            summary = "Please use the Copy Prompt button and paste into ChatGPT."
        
        # Save files for download
        files_to_download = []
        
        with tempfile.NamedTemporaryFile(delete=False, mode='w', suffix='.txt') as prompt_file:
            prompt_file.write(full_prompt)
            files_to_download.append(prompt_file.name)
            
        if summary != "Please use the Copy Prompt button and paste into ChatGPT.":
            with tempfile.NamedTemporaryFile(delete=False, mode='w', suffix='.txt') as summary_file:
                summary_file.write(summary)
                files_to_download.append(summary_file.name)
        
        return "Processing complete!", full_prompt, summary, files_to_download
        
    except Exception as e:
        logging.error(f"Error processing PDF: {e}")
        return f"Error processing PDF: {str(e)}", "", "", []

# Main Interface
with gr.Blocks(theme=gr.themes.Default()) as demo:
    # Store context size value
    context_size_value = gr.State(value=32000)
    
    # Header
    gr.Markdown("# 📄 Smart PDF Summarizer")
    gr.Markdown("Upload a PDF document and get AI-powered summaries using various AI models.")
    
    # Main Content
    with gr.Row():
        # Left Column - Input Options
        with gr.Column(scale=1):
            pdf_input = gr.File(
                label="📁 Upload PDF",
                file_types=[".pdf"]
            )
            
            with gr.Row():
                format_type = gr.Radio(
                    choices=["txt", "md", "html"],
                    value="txt",
                    label="📝 Output Format"
                )
            
            gr.Markdown("### Context Window Size")
            with gr.Row():
                context_buttons = []
                for size_name, size_value in CONTEXT_SIZES.items():
                    btn = gr.Button(size_name)
                    context_buttons.append((btn, size_value))
                        
            context_size = gr.Slider(
                minimum=1000,
                maximum=200000,
                step=1000,
                value=32000,
                label="📏 Custom Context Size"
            )
            
            snippet_number = gr.Number(
                label="🔢 Snippet Number",
                value=1,
                precision=0
            )
            
            custom_prompt = gr.Textbox(
                label="✍️ Custom Prompt",
                placeholder="Enter your custom prompt here...",
                lines=2
            )
            
            model_choice = gr.Radio(
                choices=["OpenAI ChatGPT", "HuggingFace Inference", "Groq API"],
                value="OpenAI ChatGPT",
                label="🤖 Model Selection"
            )
            
            with gr.Column(visible=False) as hf_options:
                hf_model = gr.Dropdown(
                    choices=list(model_registry.hf_models.keys()),
                    label="🔧 HuggingFace Model",
                    value="Phi-3 Mini 128k"
                )
                hf_custom_model = gr.Textbox(
                    label="Custom Model ID",
                    placeholder="Enter custom model ID...",
                    visible=False
                )
                hf_api_key = gr.Textbox(
                    label="🔑 HuggingFace API Key",
                    type="password"
                )
            
            with gr.Column(visible=False) as groq_options:
                groq_model = gr.Dropdown(
                    choices=list(model_registry.groq_models.keys()),
                    label="🔧 Groq Model",
                    value=list(model_registry.groq_models.keys())[0]
                )
                groq_refresh_btn = gr.Button("🔄 Refresh Models")
                groq_api_key = gr.Textbox(
                    label="🔑 Groq API Key",
                    type="password"
                )

        # Right Column - Output
        with gr.Column(scale=1):
            process_button = gr.Button("🚀 Process PDF", variant="primary")
            
            progress_status = gr.Textbox(
                label="📊 Progress",
                interactive=False
            )
            
            generated_prompt = gr.Textbox(
                label="📋 Generated Prompt",
                lines=10
            )
            
            with gr.Row():
                copy_prompt_button = gr.Button("📋 Copy Prompt")
                open_chatgpt_button = gr.Button("🌐 Open ChatGPT")
            
            summary_output = gr.Textbox(
                label="📝 Summary",
                lines=15
            )
            
            with gr.Row():
                copy_summary_button = gr.Button("📋 Copy Summary")
                download_files = gr.Files(
                    label="📥 Download Files"
                )

    # Event Handlers
    def update_context_size(size):
        return gr.update(value=size)

    def toggle_model_options(choice):
        return (
            gr.update(visible=choice == "HuggingFace Inference"),
            gr.update(visible=choice == "Groq API")
        )

def refresh_groq_models_list():
        updated_models = model_registry.refresh_groq_models()
        return gr.update(choices=list(updated_models.keys()))

    def toggle_custom_model(model_name):
        return gr.update(visible=model_name == "Custom Model")

    # Connect event handlers
    model_choice.change(
        toggle_model_options,
        inputs=[model_choice],
        outputs=[hf_options, groq_options]
    )

    for btn, size_value in context_buttons:
        btn.click(
            update_context_size,
            inputs=[],
            outputs=[context_size]
        ).then(
            lambda x=size_value: x,
            None,
            context_size
        )

    hf_model.change(
        toggle_custom_model,
        inputs=[hf_model],
        outputs=[hf_custom_model]
    )

    groq_refresh_btn.click(
        refresh_groq_models_list,
        outputs=[groq_model]
    )

    process_button.click(
        process_pdf,
        inputs=[
            pdf_input,
            format_type,
            context_size,
            snippet_number,
            custom_prompt,
            model_choice,
            hf_model,
            hf_custom_model,
            hf_api_key,
            groq_model,
            groq_api_key
        ],
        outputs=[
            progress_status,
            generated_prompt,
            summary_output,
            download_files
        ]
    )

    copy_prompt_button.click(
        copy_to_clipboard,
        inputs=[generated_prompt],
        outputs=[progress_status]
    )

    copy_summary_button.click(
        copy_to_clipboard,
        inputs=[summary_output],
        outputs=[progress_status]
    )

    open_chatgpt_button.click(
        open_chatgpt,
        outputs=[progress_status]
    )

    # Instructions
    gr.Markdown("""
    ### 📌 Instructions:
    1. Upload a PDF document
    2. Choose output format and context window size
    3. Select snippet number (default: 1) or enter custom prompt
    4. Select your preferred model:
       - OpenAI ChatGPT: Manual copy/paste workflow
       - HuggingFace Inference: Direct API integration
       - Groq API: High-performance inference
    5. Click 'Process PDF' to generate summary
    6. Use 'Copy Prompt' and 'Open ChatGPT' for manual processing
    7. Download generated files as needed

    ### ⚙️ Features:
    - Support for multiple PDF formats
    - Flexible text formatting options
    - Predefined context window sizes (4K to 200K)
    - Multiple model integrations
    - Copy to clipboard functionality
    - Direct ChatGPT integration
    - Downloadable outputs
    """)

# Launch the interface
if __name__ == "__main__":
    demo.launch(share=False, debug=True)