app.py

# app.py
from llama_index.llms.huggingface import HuggingFaceLLM
from llama_index.core.agent import ReActAgent
from llama_index.core.tools import FunctionTool
from transformers import AutoTokenizer
import os
import gradio as gr
import requests
import pandas as pd
import traceback
import torch

# Import real tool dependencies
try:
    from duckduckgo_search import DDGS
except ImportError:
    print("Warning: duckduckgo_search not installed. Web search will be limited.")
    DDGS = None

try:
    from sympy import sympify
    from sympy.core.sympify import SympifyError
except ImportError:
    print("Warning: sympy not installed. Math calculator will be limited.")
    sympify = None
    SympifyError = Exception

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

# --- Advanced Agent Definition ---
class SmartAgent:
    def __init__(self):
        print("Initializing Local LLM Agent...")
        
        # Check available memory and CUDA
        if torch.cuda.is_available():
            print(f"CUDA available. GPU memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f}GB")
        else:
            print("CUDA not available, using CPU")
        
        # Use a smaller, more efficient model for Hugging Face Spaces
        model_options = [
            "microsoft/DialoGPT-medium",  # Much smaller, works well for chat
            "google/flan-t5-base",        # Good for reasoning tasks
            "microsoft/DialoGPT-small",   # Smallest fallback
            "HuggingFaceH4/zephyr-7b-beta"  # Original (may fail in limited memory)
        ]
        
        model_name = model_options[1]  # Start with flan-t5-base
        print(f"Attempting to load model: {model_name}")
        
        try:
            # Initialize with memory-efficient settings
            self.llm = HuggingFaceLLM(
                model_name=model_name,
                tokenizer_name=model_name,
                context_window=1024,  # Increased for better reasoning
                max_new_tokens=256,   # Increased for better responses
                generate_kwargs={
                    "temperature": 0.3,   # Lower temperature for more focused responses
                    "do_sample": True,
                    "top_p": 0.9,
                    "repetition_penalty": 1.1
                },
                device_map="auto",
                # Add memory optimization parameters
                model_kwargs={
                    "torch_dtype": torch.float16,  # Use half precision
                    "low_cpu_mem_usage": True,
                    "load_in_8bit": True,  # Enable 8-bit quantization if available
                },
                # Add system message for better instruction following
                system_message="You are a helpful AI assistant that can search the web and perform calculations. Always provide detailed, accurate answers."
            )
            print(f"Successfully loaded model: {model_name}")
            
        except Exception as e:
            print(f"Failed to load {model_name}: {e}")
            # Fallback to an even smaller model
            try:
                fallback_model = "microsoft/DialoGPT-small"
                print(f"Falling back to: {fallback_model}")
                self.llm = HuggingFaceLLM(
                    model_name=fallback_model,
                    tokenizer_name=fallback_model,
                    context_window=256,
                    max_new_tokens=64,
                    generate_kwargs={"temperature": 0.7, "do_sample": True},
                    device_map="cpu",  # Force CPU to avoid memory issues
                    model_kwargs={"low_cpu_mem_usage": True}
                )
                print(f"Successfully loaded fallback model: {fallback_model}")
            except Exception as e2:
                print(f"Flan-T5 also failed: {e2}")
                # Try an even more basic approach with a very small model
                try:
                    basic_model = "microsoft/DialoGPT-small"
                    print(f"Final fallback to: {basic_model}")
                    self.llm = HuggingFaceLLM(
                        model_name=basic_model,
                        tokenizer_name=basic_model,
                        context_window=512,
                        max_new_tokens=128,
                        generate_kwargs={"temperature": 0.3, "do_sample": True},
                        device_map="cpu",  # Force CPU to avoid memory issues
                        model_kwargs={"low_cpu_mem_usage": True}
                    )
                    print(f"Successfully loaded final fallback: {basic_model}")
                except Exception as e3:
                    print(f"All model loading attempts failed: {e3}")
                    raise Exception("Unable to load any language model")
        
        # Define tools with real implementations
        self.tools = [
            FunctionTool.from_defaults(
                fn=self.web_search,
                name="web_search",
                description="Searches the web for current information using DuckDuckGo when questions require up-to-date knowledge"
            ),
            FunctionTool.from_defaults(
                fn=self.math_calculator,
                name="math_calculator",
                description="Performs mathematical calculations and symbolic math using SymPy when questions involve numbers or equations"
            )
        ]
        
        # Create ReAct agent with tools
        try:
            self.agent = ReActAgent.from_tools(
                tools=self.tools,
                llm=self.llm,
                verbose=True,
                max_iterations=3  # Limit iterations to prevent infinite loops
            )
            print("Local LLM Agent initialized successfully.")
        except Exception as e:
            print(f"Error creating ReAct agent: {e}")
            # Create a simple fallback agent
            self.agent = None
            print("Using fallback direct tool calling approach")

    def web_search(self, query: str) -> str:
        """Real web search using DuckDuckGo"""
        print(f"Web search triggered for: {query[:50]}...")
        
        if not DDGS:
            return "Web search unavailable - duckduckgo_search not installed"
        
        try:
            with DDGS() as ddgs:
                results = list(ddgs.text(query, max_results=5))  # Get more results
                if results:
                    formatted_results = []
                    for i, r in enumerate(results, 1):
                        title = r.get('title', 'No title')
                        body = r.get('body', 'No description')[:300]  # More context
                        url = r.get('href', '')
                        formatted_results.append(f"{i}. **{title}**\n{body}...\nSource: {url}")
                    
                    return "\n\n".join(formatted_results)
                else:
                    return f"No search results found for '{query}'. Try rephrasing your search terms."
        except Exception as e:
            print(f"Web search error: {e}")
            return f"Error during web search for '{query}': {str(e)}"

    def math_calculator(self, expression: str) -> str:
        """Safe math evaluation using SymPy"""
        print(f"Math calculation triggered for: {expression}")
        
        if not sympify:
            # Fallback to basic eval with safety checks
            try:
                # Only allow basic math operations
                allowed_chars = set('0123456789+-*/().^ ')
                if not all(c in allowed_chars for c in expression.replace(' ', '')):
                    return "Error: Only basic math operations are allowed"
                result = eval(expression.replace('^', '**'))
                return str(result)
            except Exception as e:
                return f"Error: Could not evaluate the mathematical expression - {str(e)}"
        
        try:
            # Use SymPy for safe evaluation
            result = sympify(expression).evalf()
            return str(result)
        except SympifyError as e:
            return f"Error: Could not parse the mathematical expression - {str(e)}"
        except Exception as e:
            return f"Error: Calculation failed - {str(e)}"

    def __call__(self, question: str) -> str:
        print(f"Processing question (first 50 chars): {question[:50]}...")
        
        # Enhanced reasoning approach
        question_lower = question.lower()
        
        # Check if we need to analyze files
        if any(word in question_lower for word in ['file', 'excel', 'csv', 'spreadsheet', 'data', 'attached']):
            return "I cannot access attached files in this environment. Please ensure the file is accessible via a direct URL or describe the data content directly in your question."
        
        # Check if we need web search
        needs_web_search = any(word in question_lower for word in [
            'current', 'latest', 'recent', 'today', 'news', 'who is', 'what is',
            'competition', 'winner', 'recipient', 'nationality', 'country',
            'malko', 'century', 'award', 'born', 'died'
        ])
        
        # Check if we need math calculation
        needs_calculation = any(word in question_lower for word in [
            'calculate', 'compute', 'sum', 'total', 'average', 'percentage',
            'equation', 'solve', 'math', 'number'
        ]) or any(char in question for char in '+-*/=()0123456789')
        
        try:
            if self.agent:
                # Try using the ReAct agent first
                response = self.agent.query(question)
                response_str = str(response)
                
                # Check if the response is too short or nonsensical
                if len(response_str.strip()) < 3 or response_str.strip() in ['!', '?', 'what', 'I', 'The', 'A']:
                    print("Agent gave a poor response, trying direct tool approach...")
                    return self._direct_tool_approach(question, needs_web_search, needs_calculation)
                
                return response_str
            else:
                # Use direct tool approach
                return self._direct_tool_approach(question, needs_web_search, needs_calculation)
                
        except Exception as e:
            print(f"Agent error: {str(e)}")
            print(f"Full traceback: {traceback.format_exc()}")
            # Try direct tool approach as fallback
            try:
                return self._direct_tool_approach(question, needs_web_search, needs_calculation)
            except:
                return f"I apologize, but I'm having technical difficulties processing your question. The question appears to be: {question[:100]}..."
    
    def _direct_tool_approach(self, question: str, needs_web_search: bool, needs_calculation: bool) -> str:
        """Direct tool usage when agent fails"""
        
        if needs_web_search:
            # Extract key search terms
            search_terms = []
            important_words = question.split()
            for word in important_words:
                if len(word) > 3 and word.lower() not in ['what', 'when', 'where', 'who', 'how', 'the', 'and', 'or', 'but', 'from', 'with']:
                    search_terms.append(word)
            
            search_query = ' '.join(search_terms[:5])  # Limit to 5 key terms
            print(f"Performing web search for: {search_query}")
            
            search_result = self.web_search(search_query)
            return f"Based on my web search for '{search_query}':\n\n{search_result}\n\nPlease review the search results above to find the specific information you're looking for."
        
        if needs_calculation:
            # Try to extract mathematical expressions
            import re
            # Look for mathematical expressions
            math_patterns = re.findall(r'[\d+\-*/().\s]+', question)
            for pattern in math_patterns:
                if any(char in pattern for char in '+-*/') and any(char.isdigit() for char in pattern):
                    result = self.math_calculator(pattern.strip())
                    return f"Mathematical calculation result: {result}"
        
        # Default response with better reasoning
        return f"I understand you're asking about: {question[:150]}... However, I need more specific information or context to provide an accurate answer. Could you please rephrase your question or provide additional details?"


# --- Memory cleanup function ---
def cleanup_memory():
    """Clean up GPU memory"""
    if torch.cuda.is_available():
        torch.cuda.empty_cache()
        print("GPU memory cleared")


# --- Submission Logic ---
def run_and_submit_all(profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the agent on them, submits all answers,
    and displays the results.
    """
    space_id = os.getenv("SPACE_ID")

    if profile:
        username = f"{profile.username}"
        print(f"User logged in: {username}")
    else:
        print("User not logged in.")
        return "Please Login to Hugging Face with the button.", None

    api_url = DEFAULT_API_URL
    questions_url = f"{api_url}/questions"
    submit_url = f"{api_url}/submit"

    # Clean memory before starting
    cleanup_memory()

    # Instantiate Agent
    try:
        agent = SmartAgent()
    except Exception as e:
        print(f"Error instantiating agent: {e}")
        print(f"Full traceback: {traceback.format_exc()}")
        return f"Error initializing agent: {e}", None

    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
    print(f"Agent code URL: {agent_code}")

    # Fetch 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:
            print("Fetched questions list is empty.")
            return "Fetched questions list is empty or invalid format.", None
        print(f"Fetched {len(questions_data)} questions.")
    except requests.exceptions.RequestException as e:
        print(f"Error fetching questions: {e}")
        return f"Error fetching questions: {e}", None
    except requests.exceptions.JSONDecodeError as e:
        print(f"Error decoding JSON response from questions endpoint: {e}")
        return f"Error decoding server response for questions: {e}", None
    except Exception as e:
        print(f"An unexpected error occurred fetching questions: {e}")
        return f"An unexpected error occurred fetching questions: {e}", None

    # Run Agent on all questions
    results_log = []
    answers_payload = []
    print(f"Running agent on {len(questions_data)} questions...")
    
    for i, item in enumerate(questions_data, 1):
        task_id = item.get("task_id")
        question_text = item.get("question")
        
        if not task_id or question_text is None:
            print(f"Skipping item with missing task_id or question: {item}")
            continue
            
        print(f"Processing question {i}/{len(questions_data)}: {task_id}")
        
        try:
            submitted_answer = agent(question_text)
            answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
            results_log.append({
                "Task ID": task_id, 
                "Question": question_text[:100] + "..." if len(question_text) > 100 else question_text,
                "Submitted Answer": submitted_answer[:200] + "..." if len(submitted_answer) > 200 else submitted_answer
            })
            print(f"✅ Completed question {i}: {task_id}")
            
            # Clean memory after each question
            if i % 5 == 0:  # Every 5 questions
                cleanup_memory()
                
        except Exception as e:
            print(f"❌ Error running agent on task {task_id}: {e}")
            error_answer = f"AGENT ERROR: {str(e)}"
            answers_payload.append({"task_id": task_id, "submitted_answer": error_answer})
            results_log.append({
                "Task ID": task_id, 
                "Question": question_text[:100] + "..." if len(question_text) > 100 else question_text,
                "Submitted Answer": error_answer
            })

    if not answers_payload:
        print("Agent did not produce any answers to submit.")
        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)

    # Prepare submission
    submission_data = {
        "username": username.strip(), 
        "agent_code": agent_code, 
        "answers": answers_payload
    }
    
    status_update = f"Agent finished processing. Submitting {len(answers_payload)} answers for user '{username}'..."
    print(status_update)

    # Submit answers
    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\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.')}"
        )
        print("✅ Submission successful!")
        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 requests.exceptions.JSONDecodeError:
            error_detail += f" Response: {e.response.text[:500]}"
        status_message = f"❌ Submission Failed: {error_detail}"
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df
        
    except requests.exceptions.Timeout:
        status_message = "❌ Submission Failed: The request timed out."
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df
        
    except requests.exceptions.RequestException as e:
        status_message = f"❌ Submission Failed: Network error - {e}"
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df
        
    except Exception as e:
        status_message = f"❌ An unexpected error occurred during submission: {e}"
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df


# --- Gradio UI ---
with gr.Blocks(title="Local LLM Agent Evaluation") as demo:
    gr.Markdown("# 🤖 Local LLM Agent Evaluation Runner")
    gr.Markdown(
        """
        **Instructions:**
        1. 🔐 Log in to your Hugging Face account using the button below
        2. 🚀 Click 'Run Evaluation & Submit All Answers'
        3. ⏳ Wait for the local LLM to process all questions (using memory-optimized smaller model)
        4. 📊 View your results and submission status
        
        **Features:**
        - 🔍 Real web search using DuckDuckGo
        - 🧮 Advanced math calculations with SymPy
        - 🧠 Memory-optimized language model with fallback options
        - 🛡️ Error handling and recovery mechanisms
        """
    )

    with gr.Row():
        gr.LoginButton()
    
    with gr.Row():
        run_button = gr.Button(
            "🚀 Run Evaluation & Submit All Answers", 
            variant="primary", 
            size="lg"
        )
    
    status_output = gr.Textbox(
        label="📋 Run Status / Submission Result", 
        lines=8, 
        interactive=False,
        placeholder="Click the button above to start the evaluation..."
    )
    
    results_table = gr.DataFrame(
        label="📊 Questions and Agent Answers", 
        wrap=True,
        interactive=False
    )

    # Wire up the button
    run_button.click(
        fn=run_and_submit_all,
        outputs=[status_output, results_table]
    )


if __name__ == "__main__":
    print("\n" + "="*60)
    print("🚀 Application Startup at", pd.Timestamp.now().strftime("%Y-%m-%d %H:%M:%S"))
    print("="*60)
    
    space_host_startup = os.getenv("SPACE_HOST")
    space_id_startup = os.getenv("SPACE_ID")

    if space_host_startup:
        print(f"✅ SPACE_HOST found: {space_host_startup}")
        print(f"   Runtime URL should be: https://{space_host_startup}")
    else:
        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")

    if space_id_startup:
        print(f"✅ SPACE_ID found: {space_id_startup}")
        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
        print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
    else:
        print("ℹ️  SPACE_ID environment variable not found (running locally?).")

    print("-" * 60)
    print("🎯 Launching Gradio Interface for Local LLM Agent Evaluation...")
    
    # Launch without share=True for Hugging Face Spaces
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        show_error=True
    )