app.py

import os
import gradio as gr
import requests
import inspect
import time
import pandas as pd
from smolagents import DuckDuckGoSearchTool
import threading
from typing import Dict, List, Optional, Tuple, Union
import json
from huggingface_hub import InferenceClient
import base64
from PIL import Image
import io
import tempfile
import urllib.parse
from pathlib import Path

# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"

# --- Global Cache for Answers ---
cached_answers = {}
cached_questions = []
processing_status = {"is_processing": False, "progress": 0, "total": 0}

# --- File Download Utility ---
def download_attachment(url: str, temp_dir: str) -> Optional[str]:
    """
    Download an attachment from URL to a temporary directory.
    Returns the local file path if successful, None otherwise.
    """
    try:
        response = requests.get(url, timeout=30)
        response.raise_for_status()
        
        # Extract filename from URL or create one based on content type
        parsed_url = urllib.parse.urlparse(url)
        filename = os.path.basename(parsed_url.path)
        
        if not filename or '.' not in filename:
            # Try to determine extension from content type
            content_type = response.headers.get('content-type', '').lower()
            if 'image' in content_type:
                if 'jpeg' in content_type or 'jpg' in content_type:
                    filename = f"attachment_{int(time.time())}.jpg"
                elif 'png' in content_type:
                    filename = f"attachment_{int(time.time())}.png"
                else:
                    filename = f"attachment_{int(time.time())}.img"
            elif 'audio' in content_type:
                if 'mp3' in content_type:
                    filename = f"attachment_{int(time.time())}.mp3"
                elif 'wav' in content_type:
                    filename = f"attachment_{int(time.time())}.wav"
                else:
                    filename = f"attachment_{int(time.time())}.audio"
            elif 'python' in content_type or 'text' in content_type:
                filename = f"attachment_{int(time.time())}.py"
            else:
                filename = f"attachment_{int(time.time())}.file"
        
        file_path = os.path.join(temp_dir, filename)
        
        with open(file_path, 'wb') as f:
            f.write(response.content)
        
        print(f"Downloaded attachment: {url} -> {file_path}")
        return file_path
        
    except Exception as e:
        print(f"Failed to download attachment {url}: {e}")
        return None

# --- Code Processing Tool ---
class CodeAnalysisTool:
    def __init__(self, model_name: str = "meta-llama/Llama-3.1-8B-Instruct"):
        self.client = InferenceClient(model=model_name, provider="sambanova")
        
    def analyze_code(self, code_path: str) -> str:
        """
        Analyze Python code and return insights.
        """
        try:
            with open(code_path, 'r', encoding='utf-8') as f:
                code_content = f.read()
            
            # Limit code length for analysis
            if len(code_content) > 5000:
                code_content = code_content[:5000] + "\n... (truncated)"
            
            analysis_prompt = f"""Analyze this Python code and provide a concise summary of:
1. What the code does (main functionality)
2. Key functions/classes
3. Any notable patterns or issues
4. Input/output behavior if applicable

Code:
```python
{code_content}
```

Provide a brief, focused analysis:"""

            messages = [{"role": "user", "content": analysis_prompt}]
            response = self.client.chat_completion(
                messages=messages,
                max_tokens=500,
                temperature=0.3
            )
            
            return response.choices[0].message.content.strip()
            
        except Exception as e:
            return f"Code analysis failed: {e}"

# --- Image Processing Tool ---
class ImageAnalysisTool:
    def __init__(self, model_name: str = "microsoft/Florence-2-large"):
        self.client = InferenceClient(model=model_name)
        
    def analyze_image(self, image_path: str, prompt: str = "Describe this image in detail") -> str:
        """
        Analyze an image and return a description.
        """
        try:
            # Open and process the image
            with open(image_path, "rb") as f:
                image_bytes = f.read()
            
            # Use the vision model to analyze the image
            response = self.client.image_to_text(
                image=image_bytes,
                model="microsoft/Florence-2-large"
            )
            
            return response.get("generated_text", "Could not analyze image")
            
        except Exception as e:
            try:
                # Fallback: use a different vision model
                response = self.client.image_to_text(
                    image=image_bytes,
                    model="Salesforce/blip-image-captioning-large"
                )
                return response.get("generated_text", f"Image analysis error: {e}")
            except:
                return f"Image analysis failed: {e}"

    def extract_text_from_image(self, image_path: str) -> str:
        """
        Extract text from an image using OCR.
        """
        try:
            with open(image_path, "rb") as f:
                image_bytes = f.read()
            
            # Use an OCR model
            response = self.client.image_to_text(
                image=image_bytes,
                model="microsoft/trocr-base-printed"
            )
            
            return response.get("generated_text", "No text found in image")
            
        except Exception as e:
            return f"OCR failed: {e}"

# --- Audio Processing Tool ---
class AudioTranscriptionTool:
    def __init__(self, model_name: str = "openai/whisper-large-v3"):
        self.client = InferenceClient(model=model_name)
        
    def transcribe_audio(self, audio_path: str) -> str:
        """
        Transcribe audio file to text.
        """
        try:
            with open(audio_path, "rb") as f:
                audio_bytes = f.read()
            
            # Use Whisper for transcription
            response = self.client.automatic_speech_recognition(
                audio=audio_bytes
            )
            
            return response.get("text", "Could not transcribe audio")
            
        except Exception as e:
            try:
                # Fallback to a different ASR model
                response = self.client.automatic_speech_recognition(
                    audio=audio_bytes,
                    model="facebook/wav2vec2-large-960h-lv60-self"
                )
                return response.get("text", f"Audio transcription error: {e}")
            except:
                return f"Audio transcription failed: {e}"

# --- Enhanced Intelligent Agent with Media Processing ---
class IntelligentAgent:
    def __init__(self, debug: bool = True, model_name: str = "meta-llama/Llama-3.1-8B-Instruct"):
        self.search = DuckDuckGoSearchTool()
        self.client = InferenceClient(model=model_name, provider="sambanova")
        self.image_tool = ImageAnalysisTool()
        self.audio_tool = AudioTranscriptionTool()
        self.code_tool = CodeAnalysisTool(model_name)
        self.debug = debug
        if self.debug:
            print(f"IntelligentAgent initialized with model: {model_name}")

    def _chat_completion(self, prompt: str, max_tokens: int = 500, temperature: float = 0.3) -> str:
        """
        Use chat completion instead of text generation to avoid provider compatibility issues.
        """
        try:
            messages = [{"role": "user", "content": prompt}]
            
            # Try chat completion first
            try:
                response = self.client.chat_completion(
                    messages=messages,
                    max_tokens=max_tokens,
                    temperature=temperature
                )
                return response.choices[0].message.content.strip()
            except Exception as chat_error:
                if self.debug:
                    print(f"Chat completion failed: {chat_error}, trying text generation...")
                
                # Fallback to text generation
                response = self.client.conversational(
                    prompt,
                    max_new_tokens=max_tokens,
                    temperature=temperature,
                    do_sample=temperature > 0
                )
                return response.strip()
                
        except Exception as e:
            if self.debug:
                print(f"Both chat completion and text generation failed: {e}")
            raise e

    def _detect_and_download_attachments(self, question_data: dict) -> Tuple[List[str], List[str], List[str]]:
        """
        Detect and download attachments from question data.
        Returns (image_files, audio_files, code_files)
        """
        image_files = []
        audio_files = []
        code_files = []
        
        # Create temporary directory for downloads
        temp_dir = tempfile.mkdtemp(prefix="agent_attachments_")
        
        # Check for attachments in various fields
        attachments = []
        
        # Common fields where attachments might be found
        attachment_fields = ['attachments', 'files', 'media', 'resources']
        
        for field in attachment_fields:
            if field in question_data:
                field_data = question_data[field]
                if isinstance(field_data, list):
                    attachments.extend(field_data)
                elif isinstance(field_data, str):
                    attachments.append(field_data)
        
        # Also check if the question text contains URLs
        question_text = question_data.get('question', '')
        if 'http' in question_text:
            import re
            urls = re.findall(r'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+', question_text)
            attachments.extend(urls)
        
        # Download and categorize attachments
        for attachment in attachments:
            if isinstance(attachment, dict):
                url = attachment.get('url') or attachment.get('link') or attachment.get('file_url')
                file_type = attachment.get('type', '').lower()
            else:
                url = attachment
                file_type = ''
            
            if not url:
                continue
                
            # Download the file
            file_path = download_attachment(url, temp_dir)
            if not file_path:
                continue
            
            # Categorize based on extension or type
            file_ext = Path(file_path).suffix.lower()
            
            if file_type:
                if 'image' in file_type or file_ext in ['.jpg', '.jpeg', '.png', '.gif', '.bmp', '.webp']:
                    image_files.append(file_path)
                elif 'audio' in file_type or file_ext in ['.mp3', '.wav', '.m4a', '.ogg', '.flac']:
                    audio_files.append(file_path)
                elif 'python' in file_type or 'code' in file_type or file_ext in ['.py', '.txt']:
                    code_files.append(file_path)
            else:
                # Auto-detect based on extension
                if file_ext in ['.jpg', '.jpeg', '.png', '.gif', '.bmp', '.webp']:
                    image_files.append(file_path)
                elif file_ext in ['.mp3', '.wav', '.m4a', '.ogg', '.flac']:
                    audio_files.append(file_path)
                elif file_ext in ['.py', '.txt']:
                    code_files.append(file_path)
        
        if self.debug:
            print(f"Downloaded attachments: {len(image_files)} images, {len(audio_files)} audio, {len(code_files)} code files")
        
        return image_files, audio_files, code_files

    def _process_attachments(self, image_files: List[str] = None, audio_files: List[str] = None, code_files: List[str] = None) -> str:
        """
        Process all types of attachments and return their content as text.
        """
        attachment_content = []
        
        # Process code files
        if code_files:
            for code_file in code_files:
                if code_file and os.path.exists(code_file):
                    try:
                        # First, include the raw code content (truncated)
                        with open(code_file, 'r', encoding='utf-8') as f:
                            code_content = f.read()
                        
                        if len(code_content) > 1000:
                            code_preview = code_content[:1000] + "\n... (truncated)"
                        else:
                            code_preview = code_content
                        
                        attachment_content.append(f"Code File Content:\n```python\n{code_preview}\n```")
                        
                        # Then add analysis
                        code_analysis = self.code_tool.analyze_code(code_file)
                        attachment_content.append(f"Code Analysis: {code_analysis}")
                        
                    except Exception as e:
                        attachment_content.append(f"Error processing code file {code_file}: {e}")
        
        # Process images
        if image_files:
            for image_file in image_files:
                if image_file and os.path.exists(image_file):
                    try:
                        # Analyze the image
                        image_description = self.image_tool.analyze_image(image_file)
                        attachment_content.append(f"Image Analysis: {image_description}")
                        
                        # Try to extract text from image
                        extracted_text = self.image_tool.extract_text_from_image(image_file)
                        if extracted_text and "No text found" not in extracted_text:
                            attachment_content.append(f"Text from Image: {extracted_text}")
                            
                    except Exception as e:
                        attachment_content.append(f"Error processing image {image_file}: {e}")
        
        # Process audio files
        if audio_files:
            for audio_file in audio_files:
                if audio_file and os.path.exists(audio_file):
                    try:
                        # Transcribe the audio
                        transcription = self.audio_tool.transcribe_audio(audio_file)
                        attachment_content.append(f"Audio Transcription: {transcription}")
                        
                    except Exception as e:
                        attachment_content.append(f"Error processing audio {audio_file}: {e}")
        
        return "\n\n".join(attachment_content) if attachment_content else ""

    def _should_search(self, question: str, attachment_context: str = "") -> bool:
        """
        Use LLM to determine if search is needed for the question, considering attachment context.
        Returns True if search is recommended, False otherwise.
        """
        decision_prompt = f"""Analyze this question and decide if it requires real-time information, recent data, or specific facts that might not be in your training data.

SEARCH IS NEEDED for:
- Current events, news, recent developments
- Real-time data (weather, stock prices, sports scores)
- Specific factual information that changes frequently
- Recent product releases, company information
- Current status of people, organizations, or projects
- Location-specific current information

SEARCH IS NOT NEEDED for:
- General knowledge questions
- Mathematical calculations
- Programming concepts and syntax
- Historical facts (older than 1 year)
- Definitions of well-established concepts
- How-to instructions for common tasks
- Creative writing or opinion-based responses
- Questions that can be answered from attached files (code, images, audio)
- Code analysis, debugging, or explanation questions
- Questions about uploaded content

Question: "{question}"

{f"Attachment Context Available: {attachment_context[:500]}..." if attachment_context else "No attachment context available."}

Respond with only "SEARCH" or "NO_SEARCH" followed by a brief reason (max 20 words).

Example responses:
- "SEARCH - Current weather data needed"
- "NO_SEARCH - Mathematical concept, general knowledge sufficient"
- "NO_SEARCH - Can be answered from attached code/image content"
"""

        try:
            response = self._chat_completion(decision_prompt, max_tokens=50, temperature=0.1)
            
            decision = response.strip().upper()
            should_search = decision.startswith("SEARCH")
            time.sleep(5)
            
            if self.debug:
                print(f"Decision for '{question}': {decision}")
                
            return should_search
            
        except Exception as e:
            if self.debug:
                print(f"Error in search decision: {e}, defaulting to no search for attachment questions")
            # Default to no search if decision fails and there are attachments
            return len(attachment_context) == 0

    def _answer_with_llm(self, question: str, attachment_context: str = "") -> str:
        """
        Generate answer using LLM without search, considering attachment context.
        """
        context_section = f"\n\nAttachment Context:\n{attachment_context}" if attachment_context else ""
        
        answer_prompt = f"""You are a general AI assistant. I will ask you a question. 
        YOUR ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. 
        If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string. Do not add a dot after the numbers.

{context_section}

Question: {question}

Answer:"""

        try:
            response = self._chat_completion(answer_prompt, max_tokens=500, temperature=0.3)
            return response
            
        except Exception as e:
            return f"Sorry, I encountered an error generating the response: {e}"

    def _answer_with_search(self, question: str, attachment_context: str = "") -> str:
        """
        Generate answer using search results and LLM, considering attachment context.
        """
        try:
            # Perform search
            time.sleep(10)
            search_results = self.search(question)
            
            if self.debug:
                print(f"Search results type: {type(search_results)}")
            
            if not search_results:
                return "No search results found. Let me try to answer based on my knowledge:\n\n" + self._answer_with_llm(question, attachment_context)

            # Format search results - handle different result formats
            if isinstance(search_results, str):
                search_context = search_results
            else:
                # Handle list of results
                formatted_results = []
                for i, result in enumerate(search_results[:3]):  # Use top 3 results
                    if isinstance(result, dict):
                        title = result.get("title", "No title")
                        snippet = result.get("snippet", "").strip()
                        link = result.get("link", "")
                        formatted_results.append(f"Title: {title}\nContent: {snippet}\nSource: {link}")
                    elif isinstance(result, str):
                        formatted_results.append(result)
                    else:
                        formatted_results.append(str(result))
                
                search_context = "\n\n".join(formatted_results)

            # Generate answer using search context and attachment context
            context_section = f"\n\nAttachment Context:\n{attachment_context}" if attachment_context else ""
            
            answer_prompt = f"""You are a general AI assistant. I will ask you a question. 
            Based on the search results and the context section below, provide an answer to the question. 
            If the search results don't fully answer the question, you can supplement with your general knowledge. 
            Your ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. 
            Do not add dot if your answer is a number.
            If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise.
            If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. 
            If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string.
            Do nor report on your thoughts.

Question: {question}

Search Results:
{search_context}

{context_section}

Answer:"""

            try:
                response = self._chat_completion(answer_prompt, max_tokens=600, temperature=0.3)
                return response
                
            except Exception as e:
                if self.debug:
                    print(f"LLM generation error: {e}")
                # Fallback to simple search result formatting
                if search_results:
                    if isinstance(search_results, str):
                        return search_results
                    elif isinstance(search_results, list) and len(search_results) > 0:
                        first_result = search_results[0]
                        if isinstance(first_result, dict):
                            title = first_result.get("title", "Search Result")
                            snippet = first_result.get("snippet", "").strip()
                            link = first_result.get("link", "")
                            return f"**{title}**\n\n{snippet}\n\n{f'Source: {link}' if link else ''}"
                        else:
                            return str(first_result)
                    else:
                        return str(search_results)
                else:
                    return "Search completed but no usable results found."

        except Exception as e:
            return f"Search failed: {e}. Let me try to answer based on my knowledge:\n\n" + self._answer_with_llm(question, attachment_context)

    def process_question_with_attachments(self, question_data: dict) -> str:
        """
        Process a question that may have attachments.
        """
        question_text = question_data.get('question', '')
        
        if self.debug:
            print(f"Processing question with potential attachments: {question_text[:100]}...")

        try:
            # Detect and download attachments
            image_files, audio_files, code_files = self._detect_and_download_attachments(question_data)
            
            # Process attachments to get context
            attachment_context = self._process_attachments(image_files, audio_files, code_files)
            
            if self.debug and attachment_context:
                print(f"Attachment context: {attachment_context[:200]}...")

            # Decide whether to search
            if self._should_search(question_text, attachment_context):
                if self.debug:
                    print("Using search-based approach")
                answer = self._answer_with_search(question_text, attachment_context)
            else:
                if self.debug:
                    print("Using LLM-only approach")
                answer = self._answer_with_llm(question_text, attachment_context)

            # Cleanup temporary files
            if image_files or audio_files or code_files:
                try:
                    all_files = image_files + audio_files + code_files
                    temp_dirs = set(os.path.dirname(f) for f in all_files)
                    for temp_dir in temp_dirs:
                        import shutil
                        shutil.rmtree(temp_dir, ignore_errors=True)
                except Exception as cleanup_error:
                    if self.debug:
                        print(f"Cleanup error: {cleanup_error}")

        except Exception as e:
            answer = f"Sorry, I encountered an error: {e}"

        if self.debug:
            print(f"Agent returning answer: {answer[:100]}...")
        
        return answer

    def __call__(self, question: str, image_files: List[str] = None, audio_files: List[str] = None) -> str:
        """
        Main entry point for manual testing - process media files and generate response.
        """
        if self.debug:
            print(f"Agent received question: {question}")
            print(f"Image files: {image_files}")
            print(f"Audio files: {audio_files}")

        # Early validation
        if not question or not question.strip():
            return "Please provide a valid question."

        try:
            # Process media files first
            attachment_context = self._process_attachments(image_files, audio_files, [])
            
            if self.debug and attachment_context:
                print(f"Media context: {attachment_context[:200]}...")

            # Decide whether to search
            if self._should_search(question, attachment_context):
                if self.debug:
                    print("Using search-based approach")
                answer = self._answer_with_search(question, attachment_context)
            else:
                if self.debug:
                    print("Using LLM-only approach")
                answer = self._answer_with_llm(question, attachment_context)

        except Exception as e:
            answer = f"Sorry, I encountered an error: {e}"

        if self.debug:
            print(f"Agent returning answer: {answer[:100]}...")
        
        return answer

def fetch_questions() -> Tuple[str, Optional[pd.DataFrame]]:
    """
    Fetch questions from the API and cache them.
    """
    global cached_questions
    
    api_url = DEFAULT_API_URL
    questions_url = f"{api_url}/questions"
    
    print(f"Fetching questions from: {questions_url}")
    try:
        response = requests.get(questions_url, timeout=15)
        response.raise_for_status()
        questions_data = response.json()
        
        if not questions_data:
            return "Fetched questions list is empty.", None
            
        cached_questions = questions_data
        
        # Create DataFrame for display
        display_data = []
        for item in questions_data:
            # Check for attachments
            has_attachments = False
            attachment_info = ""
            
            # Check various fields for attachments
            attachment_fields = ['attachments', 'files', 'media', 'resources']
            for field in attachment_fields:
                if field in item and item[field]:
                    has_attachments = True
                    if isinstance(item[field], list):
                        attachment_info += f"{len(item[field])} {field}, "
                    else:
                        attachment_info += f"{field}, "
            
            # Check if question contains URLs
            question_text = item.get("question", "")
            if 'http' in question_text:
                has_attachments = True
                attachment_info += "URLs in text, "
            
            if attachment_info:
                attachment_info = attachment_info.rstrip(", ")
            
            display_data.append({
                "Task ID": item.get("task_id", "Unknown"),
                "Question": question_text[:100] + "..." if len(question_text) > 100 else question_text,
                "Has Attachments": "Yes" if has_attachments else "No",
                "Attachment Info": attachment_info
            })
        
        df = pd.DataFrame(display_data)
        
        attachment_count = sum(1 for item in display_data if item["Has Attachments"] == "Yes")
        status_msg = f"Successfully fetched {len(questions_data)} questions. {attachment_count} questions have attachments. Ready to generate answers."
        
        return status_msg, df
        
    except requests.exceptions.RequestException as e:
        return f"Error fetching questions: {e}", None
    except Exception as e:
        return f"An unexpected error occurred: {e}", None

def generate_answers_async(model_name: str = "meta-llama/Llama-3.1-8B-Instruct", progress_callback=None):
    """
    Generate answers for all cached questions asynchronously using the intelligent agent.
    """
    global cached_answers, processing_status
    
    if not cached_questions:
        return "No questions available. Please fetch questions first."
    
    processing_status["is_processing"] = True
    processing_status["progress"] = 0
    processing_status["total"] = len(cached_questions)
    
    try:
        agent = IntelligentAgent(debug=True, model_name=model_name)
        cached_answers = {}
        
        for i, question_data in enumerate(cached_questions):
            if not processing_status["is_processing"]:  # Check if cancelled
                break
                
            task_id = question_data.get("task_id")
            question_text = question_data.get("question")
            
            if not task_id or question_text is None:
                continue
                
            try:
                # Use the new method that handles attachments
                answer = agent.process_question_with_attachments(question_data)
                cached_answers[task_id] = {
                    "question": question_text,
                    "answer": answer
                }
            except Exception as e:
                cached_answers[task_id] = {
                    "question": question_text,
                    "answer": f"AGENT ERROR: {e}"
                }
            
            processing_status["progress"] = i + 1
            if progress_callback:
                progress_callback(i + 1, len(cached_questions))
                
    except Exception as e:
        print(f"Error in generate_answers_async: {e}")
    finally:
        processing_status["is_processing"] = False

def start_answer_generation(model_choice: str):
    """
    Start the answer generation process in a separate thread.
    """
    if processing_status["is_processing"]:
        return "Answer generation is already in progress."
    
    if not cached_questions:
        return "No questions available. Please fetch questions first."
    
    # Map model choice to actual model name
    model_map = {
        "Llama 3.1 8B": "meta-llama/Llama-3.1-8B-Instruct",
        "Llama 3.3 70B": "meta-llama/Llama-3.3-70B-Instruct",
        "Mistral 7B": "mistralai/Mistral-7B-Instruct-v0.3"
    }
    
    selected_model = model_map.get(model_choice, "meta-llama/Llama-3.1-8B-Instruct")
    
    # Start generation in background thread
    thread = threading.Thread(target=generate_answers_async, args=(selected_model,))
    thread.daemon = True
    thread.start()
    
    return f"Answer generation started using {model_choice}. Check progress."

   
def get_generation_progress():
    """
    Get the current progress of answer generation.
    """
    if not processing_status["is_processing"] and processing_status["progress"] == 0:
        return "Not started"
    
    if processing_status["is_processing"]:
        progress = processing_status["progress"]
        total = processing_status["total"]
        status_msg = f"Generating answers... {progress}/{total} completed"
        return status_msg
    else:
        # Generation completed
        if cached_answers:
            # Create DataFrame with results
            display_data = []
            for task_id, data in cached_answers.items():
                display_data.append({
                    "Task ID": task_id,
                    "Question": data["question"][:100] + "..." if len(data["question"]) > 100 else data["question"],
                    "Generated Answer": data["answer"][:200] + "..." if len(data["answer"]) > 200 else data["answer"]
                })
            
            df = pd.DataFrame(display_data)
            status_msg = f"Answer generation completed! {len(cached_answers)} answers ready for submission."
            return status_msg, df
        else:
            return "Answer generation completed but no answers were generated."

def submit_cached_answers(profile: gr.OAuthProfile | None):
    """
    Submit the cached answers to the evaluation API.
    """
    global cached_answers
    
    if not profile:
        return "Please log in to Hugging Face first.", None
    
    if not cached_answers:
        return "No cached answers available. Please generate answers first.", None
    
    username = profile.username
    space_id = os.getenv("SPACE_ID")
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else "Unknown"
    
    # Prepare submission payload
    answers_payload = []
    for task_id, data in cached_answers.items():
        answers_payload.append({
            "task_id": task_id,
            "submitted_answer": data["answer"]
        })
    
    submission_data = {
        "username": username.strip(),
        "agent_code": agent_code,
        "answers": answers_payload
    }
    
    # Submit to API
    api_url = DEFAULT_API_URL
    submit_url = f"{api_url}/submit"
    
    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
    
    try:
        response = requests.post(submit_url, json=submission_data, timeout=60)
        response.raise_for_status()
        result_data = response.json()
        
        final_status = (
            f"Submission Successful!\n"
            f"User: {result_data.get('username')}\n"
            f"Overall Score: {result_data.get('score', 'N/A')}% "
            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
            f"Message: {result_data.get('message', 'No message received.')}"
        )
        
        # Create results DataFrame
        results_log = []
        for task_id, data in cached_answers.items():
            results_log.append({
                "Task ID": task_id,
                "Question": data["question"],
                "Submitted Answer": data["answer"]
            })
        
        results_df = pd.DataFrame(results_log)
        return final_status, results_df
        
    except requests.exceptions.HTTPError as e:
        error_detail = f"Server responded with status {e.response.status_code}."
        try:
            error_json = e.response.json()
            error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
        except:
            error_detail += f" Response: {e.response.text[:500]}"
        return f"Submission Failed: {error_detail}", None
        
    except requests.exceptions.Timeout:
        return "Submission Failed: The request timed out.", None
        
    except Exception as e:
        return f"Submission Failed: {e}", None

def clear_cache():
    """
    Clear all cached data.
    """
    global cached_answers, cached_questions, processing_status
    cached_answers = {}
    cached_questions = []
    processing_status = {"is_processing": False, "progress": 0, "total": 0}
    return "Cache cleared successfully.", None

def test_media_processing(image_files, audio_files, question):
    """
    Test the media processing functionality with uploaded files.
    """
    if not question:
        question = "What can you tell me about the uploaded media?"
    
    agent = IntelligentAgent(debug=True)
    
    # Convert file paths to lists
    image_paths = [img.name for img in image_files] if image_files else None
    audio_paths = [aud.name for aud in audio_files] if audio_files else None
    
    try:
        result = agent(question, image_files=image_paths, audio_files=audio_paths)
        return result
    except Exception as e:
        return f"Error processing media: {e}"

# --- Enhanced Gradio Interface ---
with gr.Blocks(title="Intelligent Agent with Media Processing") as demo:
    gr.Markdown("# Intelligent Agent with Conditional Search and Media Processing")
    gr.Markdown("This agent can process images and audio files, uses an LLM to decide when search is needed, optimizing for both accuracy and efficiency.")

    with gr.Row():
        gr.LoginButton()
        clear_btn = gr.Button("Clear Cache", variant="secondary")

    with gr.Tab("Step 1: Fetch Questions"):
        gr.Markdown("### Fetch Questions from API")
        fetch_btn = gr.Button("Fetch Questions", variant="primary")
        fetch_status = gr.Textbox(label="Fetch Status", lines=2, interactive=False)
        
        fetch_btn.click(
            fn=fetch_questions,
            outputs=[fetch_status, questions_table]
        )

    with gr.Tab("Step 2: Generate Answers"):
        gr.Markdown("### Generate Answers with Intelligent Search Decision")
        
        with gr.Row():
            model_choice = gr.Dropdown(
                choices=["Llama 3.1 8B", "Llama 3.3 70B", "Mistral 7B"],
                value="Llama 3.1 8B",
                label="Select Model"
            )
            generate_btn = gr.Button("Start Answer Generation", variant="primary")
            refresh_btn = gr.Button("Refresh Progress", variant="secondary")
        
        generation_status = gr.Textbox(label="Generation Status", lines=2, interactive=False)
        answers_table = gr.DataFrame(label="Generated Answers", wrap=True)
        
        generate_btn.click(
            fn=start_answer_generation,
            inputs=[model_choice],
            outputs=generation_status
        )
        
        refresh_btn.click(
            fn=get_generation_progress,
            outputs=[generation_status, answers_table]
        )

    with gr.Tab("Step 3: Submit Results"):
        gr.Markdown("### Submit Generated Answers")
        submit_btn = gr.Button("Submit Answers", variant="primary")
        submit_status = gr.Textbox(label="Submission Status", lines=4, interactive=False)
        results_table = gr.DataFrame(label="Submission Results", wrap=True)
        
        submit_btn.click(
            fn=submit_cached_answers,
            outputs=[submit_status, results_table]
        )

    # Clear cache functionality
    clear_btn.click(
        fn=clear_cache,
        outputs=[fetch_status, questions_table]
    )

if __name__ == "__main__":
    demo.launch()