app.py

import os
import re
import tempfile
import requests
import gradio as gr
print(f"Gradio version: {gr.__version__}")

from PyPDF2 import PdfReader
import logging
import webbrowser
from huggingface_hub import InferenceClient
from typing import Dict, List, Optional, Tuple
import time
from groq import Groq  # Import the Groq client

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

# Constants
CONTEXT_SIZES = {
   "4K": 4096,  
   "8K": 8192,  
   "32K": 32768,
   "64K": 65536,
   "128K": 131072
}

MODEL_CONTEXT_SIZES = {
   "Clipboard only": 4096,
   "OpenAI ChatGPT": {
       "gpt-3.5-turbo": 4096,
       "gpt-4": 8192, 
       "gpt-4-32k": 32768
   },
   "HuggingFace Inference": {
       "microsoft/phi-3-mini-4k-instruct": 4096,
       "HuggingFaceH4/zephyr-7b-beta": 8192,
       "deepseek-ai/DeepSeek-Coder-V2-Instruct": 8192,
       "meta-llama/Llama-3-8b-Instruct": 8192,
       "mistralai/Mistral-7B-Instruct-v0.3": 32768,
       "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO": 32768
   },
   "Groq API": {
       "gemma-7b-it": 8192,
       "llama-3.1-70b": 32768,
       "mixtral-8x7b-32768": 32768,
       "llama-3.1-8b": 8192
   }
}

class ModelRegistry:
   def __init__(self):
       # HuggingFace Models
       self.hf_models = {
           "Phi-3 Mini 4K": "microsoft/phi-3-mini-4k-instruct",
           "Phi-3 Mini 128k": "microsoft/Phi-3-mini-128k-instruct", 
           "Zephyr 7B Beta": "HuggingFaceH4/zephyr-7b-beta",
           "DeepSeek Coder V2": "deepseek-ai/DeepSeek-Coder-V2-Instruct",
           "Meta Llama 3.1 8B": "meta-llama/Llama-3-8b-Instruct",
           "Meta Llama 3.1 70B": "meta-llama/Meta-Llama-3.1-70B-Instruct",
           "Mixtral 7B": "mistralai/Mistral-7B-Instruct-v0.3", 
           "Nous-Hermes": "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO",
           "Cohere Command R+": "CohereForAI/c4ai-command-r-plus",
           "Aya 23-35B": "CohereForAI/aya-23-35B",
           "Custom Model": ""
       }
       
       # Default Groq Models
       self.default_groq_models = {
           "gemma-7b-it": "gemma-7b-it",
           "llama-3.1-70b-8192": "llama-3.1-70b-8192",
           "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",
           "llama-3.1-70b-8192-tool-use-preview": "llama3-groq-70b-8192-tool-use-preview"
       }
       
       self.groq_models = self._fetch_groq_models()

   def _fetch_groq_models(self) -> Dict[str, str]:
       """Fetch available Groq models with proper error handling"""
       try:
           groq_api_key = os.getenv('GROQ_API_KEY')
           if not groq_api_key:
               logging.warning("No GROQ_API_KEY found in environment")
               return self.default_groq_models

           headers = {
               "Authorization": f"Bearer {groq_api_key}",
               "Content-Type": "application/json"
           }
           
           response = requests.get(
               "https://api.groq.com/openai/v1/models", 
               headers=headers,
               timeout=10
           )
           
           if response.status_code == 200:
               models = response.json().get("data", [])
               model_dict = {model["id"]: model["id"] for model in models}
               
               # Merge with defaults to ensure all models are available
               return {**self.default_groq_models, **model_dict}
           else:
               logging.error(f"Failed to fetch Groq models: {response.status_code}")
               return self.default_groq_models
               
       except requests.exceptions.Timeout:
           logging.error("Timeout while fetching Groq models")
           return self.default_groq_models
       except Exception as e:
           logging.error(f"Error fetching Groq models: {e}")
           return self.default_groq_models

   def _get_default_groq_models(self) -> Dict[str, str]:
       """Return default Groq models"""
       return self.default_groq_models

   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_model(*args, **kwargs):  # Correct the outputs here
        try:
            with gr.Progress() as progress:
                progress(0, "Preparing to send to model...")
                summary, download_file = send_to_model_impl(*args, **kwargs)  # Get both outputs
                progress(1, "Complete!")
                return summary, download_file # Return both outputs
        except Exception as e:
            return f"Error: {str(e)}", None  # Return error message and None for the file

def send_to_model_impl(prompt, model_selection, hf_model_choice, hf_custom_model, hf_api_key,
                    groq_model_choice, groq_api_key, openai_api_key, openai_model_choice):
    try:
        if model_selection == "Clipboard only":
            return "Use copy/paste for processing", None

        elif model_selection == "HuggingFace Inference":
            if not hf_api_key:
                return "Error: HuggingFace API key required", None
            if not hf_model_choice:
                return "Error: Select a HuggingFace model", None
            model_id = hf_custom_model if hf_model_choice == "Custom Model" else model_registry.hf_models[hf_model_choice]
            try:
                summary = send_to_hf_inference(prompt, model_id, hf_api_key)
            except Exception as e:
                return f"Error with HuggingFace Inference: {e}", None


        elif model_selection == "Groq API":
            if not groq_api_key:
                return "Error: Groq API key required", None
            if not groq_model_choice:
                return "Error: Select a Groq model", None
            try:
                summary = send_to_groq(prompt, groq_model_choice, groq_api_key)
            except Exception as e:
                return f"Error with Groq API: {e}", None

        elif model_selection == "OpenAI ChatGPT":
            if not openai_api_key:
                return "Error: OpenAI API key required", None
            try:
                summary = send_to_openai(prompt, openai_api_key, model=openai_model_choice)
            except Exception as e:
                return f"Error with OpenAI API: {e}", None

        else:
            return "Error: Invalid model selection", None

        with tempfile.NamedTemporaryFile(delete=False, mode='w', suffix='.txt') as f:
            f.write(summary)
            download_file = f.name

        return summary, download_file

    except Exception as e:  # Outer exception handler
        error_msg = f"An unexpected error occurred: {str(e)}"
        logging.error(error_msg)
        return error_msg, None
    
def send_to_hf_inference(prompt: str, model_name: str, api_key: str) -> str:
   try:
       client = InferenceClient(token=api_key)
       response = client.text_generation(
           prompt,
           model=model_name,
           max_new_tokens=500,
           temperature=0.7,
           top_p=0.95,
           repetition_penalty=1.1
       )
       return str(response)
   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:
   try:
       client = Groq(api_key=api_key)
       response = client.chat.completions.create(
           model=model_name,
           messages=[{
               "role": "user", 
               "content": prompt
           }],
           temperature=0.7,
           max_tokens=500,
           top_p=0.95
       )
       return response.choices[0].message.content
   except Exception as e:
       logging.error(f"Error with Groq API: {e}")
       return f"Error with Groq API: {e}"

def send_to_openai(prompt: str, api_key: str, model: str = "gpt-3.5-turbo") -> str:
   try:
       import openai
       openai.api_key = api_key
       
       response = openai.ChatCompletion.create(
           model=model,
           messages=[
               {"role": "system", "content": "You are a helpful assistant that provides detailed responses with examples and references where appropriate."},
               {"role": "user", "content": prompt}
           ],
           temperature=0.7,
           max_tokens=500,
           top_p=0.95
       )
       return response.choices[0].message.content
   except Exception as e:
       logging.error(f"Error with OpenAI API: {e}")
       return f"Error with OpenAI API: {e}"

def copy_text_js(element_id: str) -> str:
    return f"""function() {{
        let textarea = document.getElementById('{element_id}');
        if (!textarea) return 'Element not found';
        textarea.select();
        try {{
            document.execCommand('copy');
            return 'Copied to clipboard!';
        }} catch(err) {{
            return 'Failed to copy: ' + err;
        }}
    }}"""

def open_chatgpt() -> str:
    """Open ChatGPT in new browser tab"""
    return """window.open('https://chat.openai.com/', '_blank');"""

def process_pdf(pdf, fmt, ctx_size):
    """Process PDF and return text and snippets"""
    try:
        if not pdf:
            return "Please upload a PDF file.", "", [], None
        
        # Extract text
        text = extract_text_from_pdf(pdf.name)
        if text.startswith("Error"):
            return text, "", [], None
        
        # Format content
        formatted_text = format_content(text, fmt)
        
        # Split into snippets
        snippets = split_into_snippets(formatted_text, ctx_size)
        
        # Save full text for download
        with tempfile.NamedTemporaryFile(delete=False, mode='w', suffix='.txt') as text_file:
            text_file.write(formatted_text)
            
        snippet_choices = [f"Snippet {i+1} of {len(snippets)}" for i in range(len(snippets))]
        
        return (
            "PDF processed successfully!", 
            formatted_text,
            snippets,
            snippet_choices,
            [text_file.name]
        )
        
    except Exception as e:
        logging.error(f"Error processing PDF: {e}")
        return f"Error processing PDF: {str(e)}", "", [], None

def generate_prompt(text, template, snippet_idx=None):
    """Generate prompt from text or selected snippet"""
    try:
        if not text:
            return "No text available.", "", None
            
        default_prompt = "Summarize the following text:"
        prompt_template = template if template else default_prompt
        
        if isinstance(text, list):
            # If text is list of snippets
            if snippet_idx is not None:
                if 0 <= snippet_idx < len(text):
                    content = text[snippet_idx]
                else:
                    return "Invalid snippet index.", "", None
            else:
                content = "\n\n".join(text)
        else:
            content = text
            
        prompt = f"{prompt_template}\n---\n{content}\n---"
        
        # Save prompt for download
        with tempfile.NamedTemporaryFile(delete=False, mode='w', suffix='.txt') as prompt_file:
            prompt_file.write(prompt)
            
        return "Prompt generated!", prompt, [prompt_file.name]
        
    except Exception as e:
        logging.error(f"Error generating prompt: {e}")
        return f"Error generating prompt: {str(e)}", "", None

# Main Interface
with gr.Blocks(css="""
    .gradio-container {max-width: 90%; margin: 0 auto;}
    @media (max-width: 768px) {.gradio-container {max-width: 98%; padding: 10px;} .gr-row {flex-direction: column;} .gr-col {width: 100%; margin-bottom: 10px;}}
""") as demo:
    # State variables
    pdf_content = gr.State("")
    snippets = gr.State([])
    
    # Header
    gr.Markdown("# 📄 Smart PDF Summarizer")
    gr.Markdown("Upload a PDF document and get AI-powered summaries using various AI models.")
    
    with gr.Tabs() as tabs:
        # Tab 1: PDF Processing
        with gr.Tab("1️⃣ PDF Processing"):
            with gr.Row():
                with gr.Column(scale=1):
                    pdf_input = gr.File(
                        label="📁 Upload PDF",
                        file_types=[".pdf"]
                    )
                    
                    format_type = gr.Radio(
                        choices=["txt", "md", "html"],
                        value="txt",
                        label="📝 Output Format"
                    )
                    
                    context_size = gr.Slider(
                        minimum=1000,
                        maximum=200000,
                        step=1000,
                        value=4096,
                        label="Context Size"
                    )
                    
                    gr.Markdown("### Context Size")
                    with gr.Row():
                        for size_name, size_value in CONTEXT_SIZES.items():
                            gr.Button(
                                size_name,
                                size="sm",
                                scale=1
                            ).click(
                                lambda v=size_value: gr.update(value=v),
                                None,
                                context_size
                            )
                    
                    process_button = gr.Button("🔍 Process PDF", variant="primary")
                    
                with gr.Column(scale=1):
                    progress_status = gr.Textbox(
                        label="Status",
                        interactive=False,
                        show_label=True,
                        visible=True  # Ensure error messages are always visible
                    )
                    processed_text = gr.Textbox(
                        label="Processed Text",
                        lines=10,
                        max_lines=50,
                        show_copy_button=True
                    )
                    download_full_text = gr.File(label="📥 Download Full Text")

        # Tab 2: Snippet Selection
        with gr.Tab("2️⃣ Snippet Selection"):
            with gr.Row():
                with gr.Column(scale=1):
                    snippet_selector = gr.Dropdown(
                        label="Select Snippet",
                        choices=[],
                        interactive=True
                    )
                    
                    custom_prompt = gr.Textbox(
                        label="✍️ Custom Prompt Template",
                        placeholder="Enter your custom prompt here...",
                        lines=2
                    )
                    
                    generate_prompt_btn = gr.Button("Generate Prompt", variant="primary")
                    
                with gr.Column(scale=1):
                    generated_prompt = gr.Textbox(
                    label="📋 Generated Prompt",
                    lines=10,
                    max_lines=50,
                    show_copy_button=True,
                    elem_id="generated_prompt"  # Add this
                )
                    
                    with gr.Row():
                        download_prompt = gr.File(label="📥 Download Prompt")
                        download_snippet = gr.File(label="📥 Download Selected Snippet")

        # Tab 3: Model Processing
        with gr.Tab("3️⃣ Model Processing"):
         with gr.Row():
           with gr.Column(scale=1):
               model_choice = gr.Radio(
                   choices=list(MODEL_CONTEXT_SIZES.keys()),
                   value="Clipboard only",
                   label="🤖 Provider Selection"
               )
               
               with gr.Column(visible=False) as openai_options:
                   openai_model = gr.Dropdown(
                       choices=list(MODEL_CONTEXT_SIZES["OpenAI ChatGPT"].keys()),
                       value="gpt-3.5-turbo",
                       label="OpenAI Model"
                   )
                   openai_api_key = gr.Textbox(
                       label="🔑 OpenAI API Key",
                       type="password"
                   )
               
               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 4K"
                    )
                    hf_custom_model = gr.Textbox(  # This needs to be defined before being used
                        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()),  # Use model_registry.groq_models
                        value=list(model_registry.groq_models.keys())[0] if model_registry.groq_models else None, # Set a default value if available
                        label="Groq Model"
                    )
                    groq_api_key = gr.Textbox(
                        label="🔑 Groq API Key",
                        type="password"
                    )
                    groq_refresh_btn = gr.Button("🔄 Refresh Groq Models")  # Add refresh button
                    
               send_to_model_btn = gr.Button("🚀 Send to Model", variant="primary")
               open_chatgpt_button = gr.Button("🌐 Open ChatGPT")
                    
               with gr.Column(scale=1):
                    summary_output = gr.Textbox(
                        label="📝 Summary",
                        lines=15,
                        max_lines=50,
                        show_copy_button=True,
                        elem_id="summary_output"  # Add this
                    )
                    
                    with gr.Row():
                        download_summary = gr.File(label="📥 Download Summary")
            
    # Hidden components for file handling
    download_files = gr.Files(label="📥 Downloads", visible=False)

    # Event Handlers
    def update_context_size(size: int) -> None:
        """Update context size slider with validation"""
        if not isinstance(size, (int, float)):
            size = 4096  # Default size
        return gr.update(value=int(size))
    
    def get_model_context_size(choice: str, groq_model: str = None) -> int:
        """Get context size for model with better defaults"""
        if choice == "Groq API" and groq_model:
            return MODEL_CONTEXT_SIZES["Groq API"].get(groq_model, 4096)
        elif choice == "OpenAI ChatGPT":
            return 4096
        elif choice == "HuggingFace Inference":
            return 4096
        return 32000  # Safe default
    
    def update_snippet_choices(snippets_list: List[str]) -> List[str]:
        """Create formatted snippet choices"""
        return [f"Snippet {i+1} of {len(snippets_list)}" for i in range(len(snippets_list))]

    def get_snippet_index(choice: str) -> int:
        """Extract snippet index from choice string"""
        if not choice:
            return 0
        try:
            return int(choice.split()[1]) - 1
        except:
            return 0

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

    def refresh_groq_models_list():
        try:
            with gr.Progress() as progress:
                progress(0, "Refreshing Groq models...")
                updated_models = model_registry.refresh_groq_models()
                progress(1, "Complete!")
                return gr.update(choices=list(updated_models.keys()))
        except Exception as e:
            logging.error(f"Error refreshing models: {e}")
            return gr.update()

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

    def handle_groq_model_change(model_name):
        """Handle Groq model selection change"""
        return update_context_size("Groq API", model_name)

    def handle_model_selection(choice):
        """Handle model selection and update UI"""
        ctx_size = MODEL_CONTEXT_SIZES.get(choice, {})
        if isinstance(ctx_size, dict):
            first_model = list(ctx_size.keys())[0]
            ctx_size = ctx_size[first_model]
            
            # Prepare dropdown choices based on provider
            if choice == "OpenAI ChatGPT":
                model_choices = list(MODEL_CONTEXT_SIZES["OpenAI ChatGPT"].keys())
                return [
                    gr.update(visible=False),  # hf_options
                    gr.update(visible=False),  # groq_options
                    gr.update(visible=True),   # openai_options
                    gr.update(value=ctx_size), # context_size
                    gr.Dropdown(choices=model_choices, value=first_model)  # openai_model
                ]
            elif choice == "HuggingFace Inference":
                model_choices = list(model_registry.hf_models.keys())
                return [
                    gr.update(visible=True),   # hf_options
                    gr.update(visible=False),  # groq_options
                    gr.update(visible=False),  # openai_options
                    gr.update(value=ctx_size), # context_size
                    gr.Dropdown(choices=model_choices, value="Phi-3 Mini 4K")  # openai_model (not used)
                ]
            elif choice == "Groq API":
                model_choices = list(model_registry.groq_models.keys())
                return [
                    gr.update(visible=False),  # hf_options
                    gr.update(visible=True),   # groq_options
                    gr.update(visible=False),  # openai_options
                    gr.update(value=ctx_size), # context_size
                    gr.Dropdown(choices=model_choices, value=model_choices[0] if model_choices else None)  # openai_model (not used)
                ]
        
        # Default return for "Clipboard only" or other options
        return [
            gr.update(visible=False),  # hf_options
            gr.update(visible=False),  # groq_options
            gr.update(visible=False),  # openai_options
            gr.update(value=4096),    # context_size
            gr.Dropdown(choices=[])    # openai_model (not used)
        ]
    
    # PDF Processing Handlers
    def handle_pdf_process(pdf, fmt, ctx_size):
        """Process PDF and update UI state"""
        if not pdf:
            return (
                "Please upload a PDF file.",  # progress_status
                "",                          # processed_text
                "",                          # pdf_content
                [],                         # snippets
                gr.update(choices=[], value=None),  # snippet_selector
                None                        # download_files
            )
            
        try:
            # Extract and format text
            text = extract_text_from_pdf(pdf.name)
            if text.startswith("Error"):
                return (
                    text,
                    "",
                    "",
                    [],
                    gr.update(choices=[], value=None),
                    None
                )
                
            formatted_text = format_content(text, fmt)
            snippets_list = split_into_snippets(formatted_text, ctx_size)
            
            # Create downloadable full text
            with tempfile.NamedTemporaryFile(delete=False, mode='w', suffix='.txt') as f:
                f.write(formatted_text)
                download_file = f.name
                
            return (
                f"PDF processed successfully! Generated {len(snippets_list)} snippets.",
                formatted_text,
                formatted_text,
                snippets_list,
                gr.update(choices=update_snippet_choices(snippets_list), value="Snippet 1 of " + str(len(snippets_list))),
                download_file # Return the file for download_full_text
                #[download_file]
            )
            
        except Exception as e:
            error_msg = f"Error processing PDF: {str(e)}"
            logging.error(error_msg)
            return (
                error_msg,
                "",
                "",
                [],
                gr.update(choices=[], value=None),
                None
            )

    def handle_snippet_selection(choice, snippets_list): # Add download_snippet output
        """Handle snippet selection, update prompt, and provide snippet download."""
        if not snippets_list:
            return "No snippets available.", "", None  # Return None for download

        try:
            idx = get_snippet_index(choice)
            selected_snippet = snippets_list[idx]

            with tempfile.NamedTemporaryFile(delete=False, mode='w', suffix='.txt') as f:
                f.write(selected_snippet)
                snippet_download_file = f.name  # Store the file path

            return (
                f"Selected snippet {idx + 1}",
                selected_snippet,
                snippet_download_file # Return file for download
            )

        except Exception as e:
            error_msg = f"Error selecting snippet: {str(e)}"
            logging.error(error_msg)
            return (
                error_msg,
                "",
                None
            )
        
    # Copy button handlers
    def handle_prompt_generation(snippet_text, template, snippet_choice, snippets_list):
        try:
            if not snippets_list:
                return "No text available.", "", None
                
            idx = get_snippet_index(snippet_choice)
            base_prompt = template if template else "Summarize the following text:"
            content = snippets_list[idx]
            
            prompt = f"{base_prompt}\n---\n{content}\n---"
            
            # Save prompt for download
            with tempfile.NamedTemporaryFile(delete=False, mode='w', suffix='.txt') as f:
                f.write(prompt)
                download_file = f.name
                
            return "Prompt generated!", prompt, download_file # Return the file for download_prompt

        except Exception as e:
            logging.error(f"Error generating prompt: {e}")
            return f"Error: {str(e)}", "", None

    def handle_copy_action(text):
        """Handle copy to clipboard action"""
        return {
            progress_status: gr.update(value="Text copied to clipboard!", visible=True)
        }

    # Connect all event handlers
    # Core event handlers
    process_button.click(
        handle_pdf_process,
        inputs=[pdf_input, format_type, context_size],
        outputs=[progress_status, processed_text, pdf_content, snippets, snippet_selector, download_full_text]
    )

    generate_prompt_btn.click(
        handle_prompt_generation,
        inputs=[generated_prompt, custom_prompt, snippet_selector, snippets],
        outputs=[progress_status, generated_prompt, download_prompt]
    )

    # Snippet handling
    snippet_selector.change(
        handle_snippet_selection,
        inputs=[snippet_selector, snippets],
        outputs=[progress_status, generated_prompt, download_snippet] # Connect download_snippet
    )

    # Model selection
    model_choice.change(
        handle_model_selection,
        inputs=[model_choice],
        outputs=[
            hf_options,
            groq_options,
            openai_options,
            context_size,
            openai_model
        ]
    )

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

    groq_model.change(
        handle_groq_model_change,
        inputs=[groq_model],
        outputs=[context_size]
    )

    def download_file(content: str, prefix: str) -> List[str]:
        if not content:
            return []
        try:
            filename = f"{prefix}_{int(time.time())}.txt"  # Add timestamp
            with tempfile.NamedTemporaryFile(delete=False, mode='w', suffix='.txt', prefix=filename) as f:
                f.write(content)
                return [f.name]
        except Exception as e:
            logging.error(f"Error creating download file: {e}")
            return []

    # ChatGPT handler
    open_chatgpt_button.click(
        fn=lambda: "window.open('https://chat.openai.com/', '_blank'); return 'Opened ChatGPT in new tab';",
        inputs=None,
        outputs=progress_status,
        js=True
    )

    # Model processing
    send_to_model_btn.click(
        send_to_model,
        inputs=[
            generated_prompt, model_choice, hf_model, hf_custom_model, hf_api_key,
            groq_model, groq_api_key, openai_api_key, openai_model # Add openai_model as input
        ],
        outputs=[summary_output, download_summary] # Correct outputs
    )

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

    # 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 in case you want to proceed directly (or continue with 5):
       - 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, optionally, 'Open ChatGPT' for manual processing
    7. Download generated files as needed
    """)

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