Update app.py

app.py

@@ -1,118 +1,427 @@
 import os
 import gradio as gr
 import requests
+import inspect
 import pandas as pd
-from transformers import pipeline
+import re
+import json
+import math
+import time
+from typing import Dict, Any, List, Optional, Union
 
+# (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-HF_MODEL_NAME = "facebook/bart-large-mnli"  # Free model that works in Spaces
 
-# --- Agent Definition ---
-class BasicAgent:
-    def __init__(self):
-        print("Initializing Agent...")
+# --- Tool Definitions ---
+class Tools:
+    @staticmethod
+    def calculator(expression: str) -> Union[float, str]:
+        """Safely evaluate mathematical expressions"""
+        # Clean the expression to only contain valid math operations
         try:
-            self.llm = pipeline(
-                "text-generation",
-                model=HF_MODEL_NAME,
-                device_map="auto"
-            )
+            # Extract numbers and operators
+            safe_expr = re.sub(r'[^0-9+\-*/().%\s]', '', expression)
+            # Calculate using a safer approach than eval()
+            # Use a restricted namespace for evaluation
+            safe_globals = {"__builtins__": {}}
+            safe_locals = {"math": math}
+            # Add basic math functions
+            for func in ['sin', 'cos', 'tan', 'sqrt', 'log', 'exp', 'floor', 'ceil']:
+                safe_locals[func] = getattr(math, func)
+            
+            result = eval(safe_expr, safe_globals, safe_locals)
+            return result
         except Exception as e:
-            print(f"LLM initialization failed: {e}")
-            self.llm = None
-
-    def __call__(self, question: str) -> str:
-        if not self.llm:
-            return "Default answer (LLM not available)"
+            return f"Error in calculation: {str(e)}"
+    
+    @staticmethod
+    def search(query: str) -> str:
+        """Simulate a web search with predefined responses for common queries"""
+        # This is a mock search function - in a real scenario, you might 
+        # use a proper search API like SerpAPI or DuckDuckGo
+        knowledge_base = {
+            "population": "The current world population is approximately 8 billion people.",
+            "capital of france": "The capital of France is Paris.",
+            "largest planet": "Jupiter is the largest planet in our solar system.",
+            "tallest mountain": "Mount Everest is the tallest mountain above sea level at 8,848.86 meters.",
+            "deepest ocean": "The Mariana Trench is the deepest ocean trench, located in the Pacific Ocean.",
+            "president": "The current president of the United States is Joe Biden (as of 2024).",
+            "water boiling point": "Water boils at 100 degrees Celsius (212 degrees Fahrenheit) at standard pressure.",
+            "pi": "The mathematical constant pi (π) is approximately 3.14159.",
+            "speed of light": "The speed of light in vacuum is approximately 299,792,458 meters per second.",
+            "human body temperature": "Normal human body temperature is around 37 degrees Celsius (98.6 degrees Fahrenheit)."
+        }
+        
+        # Try to find a relevant answer in our knowledge base
+        for key, value in knowledge_base.items():
+            if key in query.lower():
+                return value
         
+        return "No relevant information found in the knowledge base."
+    
+    @staticmethod
+    def date_info() -> str:
+        """Provide the current date"""
+        return time.strftime("%Y-%m-%d")
+
+# --- LLM Interface ---
+class LLMInterface:
+    @staticmethod
+    def query_llm(prompt: str) -> str:
+        """Query a free LLM through Hugging Face's inference API"""
         try:
-            response = self.llm(question, max_length=100)
-            return response[0]['generated_text']
+            # Using FLAN-T5-XXL which is available for free
+            API_URL = "https://api-inference.huggingface.co/models/google/flan-t5-xxl"
+            headers = {"Content-Type": "application/json"}
+            
+            # Use a well-formatted prompt
+            payload = {
+                "inputs": prompt,
+                "parameters": {"max_length": 200, "temperature": 0.7}
+            }
+            
+            response = requests.post(API_URL, headers=headers, json=payload, timeout=10)
+            
+            if response.status_code == 200:
+                result = response.json()
+                # Handle different response formats
+                if isinstance(result, list) and len(result) > 0:
+                    return result[0].get("generated_text", "").strip()
+                elif isinstance(result, dict):
+                    return result.get("generated_text", "").strip()
+                else:
+                    return str(result).strip()
+            else:
+                # Fallback for rate limits or API issues
+                return "The model is currently unavailable. Please try again later."
+                
         except Exception as e:
             return f"Error: {str(e)}"
 
-def run_and_submit_all():
-    """Simplified version that works with Gradio auth"""
-    # Get username from Gradio's auth system
-    username = os.getenv("GRADIO_AUTH_USERNAME")
-    if not username:
-        return "Please login first", None
+# --- Advanced Agent Implementation ---
+class BasicAgent:
+    def __init__(self):
+        print("Advanced Agent initialized.")
+        self.tools = {
+            "calculator": Tools.calculator,
+            "search": Tools.search,
+            "date": Tools.date_info
+        }
+        self.llm = LLMInterface()
+    
+    def __call__(self, question: str) -> str:
+        print(f"Agent received question: {question[:50]}...")
+        
+        # Step 1: Analyze the question
+        tool_needed, tool_name = self._analyze_question(question)
+        
+        # Step 2: Use appropriate tool or direct answer
+        if tool_needed:
+            if tool_name == "calculator":
+                # Extract the math expression from the question
+                expression = self._extract_math_expression(question)
+                if expression:
+                    result = self.tools["calculator"](expression)
+                    # Format numerical answers appropriately
+                    if isinstance(result, (int, float)):
+                        if result == int(result):
+                            answer = str(int(result))  # Remove decimal for whole numbers
+                        else:
+                            answer = str(result)  # Keep decimal for fractions
+                    else:
+                        answer = str(result)
+                else:
+                    answer = "Unable to extract a mathematical expression from the question."
+            
+            elif tool_name == "search":
+                result = self.tools["search"](question)
+                answer = self._extract_direct_answer(question, result)
+            
+            elif tool_name == "date":
+                result = self.tools["date"]()
+                answer = result
+            
+            else:
+                # Use LLM for other types of questions
+                answer = self._get_answer_from_llm(question)
+        else:
+            # Direct answer for simpler questions
+            answer = self._get_answer_from_llm(question)
+        
+        print(f"Agent returning answer: {answer[:50]}...")
+        return answer
+    
+    def _analyze_question(self, question: str) -> tuple:
+        """Determine if the question requires a tool and which one"""
+        # Check for mathematical questions
+        math_patterns = [
+            r'calculate', r'compute', r'what is \d+', r'how much is',
+            r'sum of', r'multiply', r'divide', r'subtract', r'plus', r'minus',
+            r'\d+\s*[\+\-\*\/\%]\s*\d+', r'squared', r'cubed', r'square root'
+        ]
+        
+        for pattern in math_patterns:
+            if re.search(pattern, question.lower()):
+                return True, "calculator"
+        
+        # Check for factual questions that might need search
+        search_patterns = [
+            r'^what is', r'^who is', r'^where is', r'^when', r'^how many',
+            r'capital of', r'largest', r'tallest', r'population', r'president',
+            r'temperature', r'boiling point', r'freezing point', r'speed of'
+        ]
+        
+        for pattern in search_patterns:
+            if re.search(pattern, question.lower()):
+                return True, "search"
+        
+        # Check for date-related questions
+        date_patterns = [r'what day is today', r'current date', r'today\'s date']
+        
+        for pattern in date_patterns:
+            if re.search(pattern, question.lower()):
+                return True, "date"
+        
+        # Default to direct answer
+        return False, None
+    
+    def _extract_math_expression(self, question: str) -> str:
+        """Extract a mathematical expression from the question"""
+        # Look for common pattern: "Calculate X" or "What is X"
+        patterns = [
+            r'calculate\s+(.*?)(?:\?|$)',
+            r'what is\s+(.*?)(?:\?|$)',
+            r'compute\s+(.*?)(?:\?|$)',
+            r'find\s+(.*?)(?:\?|$)',
+            r'how much is\s+(.*?)(?:\?|$)'
+        ]
+        
+        for pattern in patterns:
+            match = re.search(pattern, question.lower())
+            if match:
+                expression = match.group(1).strip()
+                # Further clean the expression
+                expression = re.sub(r'[^0-9+\-*/().%\s]', '', expression)
+                return expression
+        
+        # If no clear pattern, attempt to extract any mathematical operation
+        nums_and_ops = re.findall(r'(\d+(?:\.\d+)?|\+|\-|\*|\/|\(|\)|\%)', question)
+        if nums_and_ops:
+            return ''.join(nums_and_ops)
+        
+        return ""
+    
+    def _extract_direct_answer(self, question: str, search_result: str) -> str:
+        """Extract a concise answer from search results based on the question"""
+        # For simple factual questions, return the search result directly
+        return search_result
+    
+    def _get_answer_from_llm(self, question: str) -> str:
+        """Get an answer from the LLM with appropriate prompting"""
+        prompt = f"""
+        Answer the following question with a very concise, direct response:
+        
+        Question: {question}
+        
+        Answer in 1-2 sentences maximum, focusing only on the specific information requested.
+        """
+        
+        # Simple responses for common questions to avoid LLM latency
+        common_answers = {
+            "what color is the sky": "Blue.",
+            "how many days in a week": "7 days.",
+            "how many months in a year": "12 months.",
+            "what is the capital of france": "Paris.",
+            "what is the capital of japan": "Tokyo.",
+            "what is the capital of italy": "Rome.",
+            "what is the capital of germany": "Berlin.",
+            "what is the capital of spain": "Madrid.",
+            "what is water made of": "H2O (hydrogen and oxygen).",
+            "who wrote romeo and juliet": "William Shakespeare.",
+            "who painted the mona lisa": "Leonardo da Vinci.",
+            "what is the largest ocean": "The Pacific Ocean.",
+            "what is the smallest planet": "Mercury."
+        }
+        
+        # Check if we have a hardcoded answer
+        for key, answer in common_answers.items():
+            if question.lower().strip('?').strip() == key:
+                return answer
+        
+        # If no hardcoded answer, use the LLM
+        return self.llm.query_llm(prompt)
+
+def run_and_submit_all(profile: gr.OAuthProfile | None):
+    """
+    Fetches all questions, runs the BasicAgent on them, submits all answers,
+    and displays the results.
+    """
+    # --- Determine HF Space Runtime URL and Repo URL ---
+    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
+
+    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
 
-    space_id = os.getenv("SPACE_ID")
     api_url = DEFAULT_API_URL
-    agent = BasicAgent()
+    questions_url = f"{api_url}/questions"
+    submit_url = f"{api_url}/submit"
+
+    # 1. Instantiate Agent (now using our improved agent)
+    try:
+        agent = BasicAgent()
+    except Exception as e:
+        print(f"Error instantiating agent: {e}")
+        return f"Error initializing agent: {e}", None
     
-    # Fetch questions
+    # In the case of an app running as a hugging Face space, this link points toward your codebase
+    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
+    print(agent_code)
+
+    # 2. Fetch Questions
+    print(f"Fetching questions from: {questions_url}")
     try:
-        response = requests.get(f"{api_url}/questions", timeout=15)
-        questions = response.json()
+        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}")
+         print(f"Response text: {response.text[:500]}")
+         return f"Error decoding server response for questions: {e}", None
     except Exception as e:
-        return f"Failed to get questions: {str(e)}", None
+        print(f"An unexpected error occurred fetching questions: {e}")
+        return f"An unexpected error occurred fetching questions: {e}", None
 
-    # Process questions
-    results = []
-    answers = []
-    for q in questions:
+    # 3. Run your Agent
+    results_log = []
+    answers_payload = []
+    print(f"Running agent on {len(questions_data)} questions...")
+    for item in questions_data:
+        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
         try:
-            answer = agent(q.get("question", ""))
-            answers.append({
-                "task_id": q.get("task_id"),
-                "submitted_answer": answer
-            })
-            results.append({
-                "Task ID": q.get("task_id"),
-                "Question": q.get("question"),
-                "Answer": answer
-            })
+            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, "Submitted Answer": submitted_answer})
         except Exception as e:
-            results.append({
-                "Task ID": q.get("task_id"),
-                "Question": q.get("question"),
-                "Answer": f"Error: {str(e)}"
-            })
+             print(f"Error running agent on task {task_id}: {e}")
+             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
+
+    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)
 
-    # Submit answers
+    # 4. Prepare Submission 
+    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
+    status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
+    print(status_update)
+
+    # 5. Submit
+    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
     try:
-        response = requests.post(
-            f"{api_url}/submit",
-            json={
-                "username": username,
-                "agent_code": f"https://huggingface.co/spaces/{space_id}",
-                "answers": answers
-            },
-            timeout=60
-        )
-        result = response.json()
-        return (
-            f"Success! Score: {result.get('score', 'N/A')}%\n"
-            f"Correct: {result.get('correct_count', 0)}/{result.get('total_attempted', 0)}",
-            pd.DataFrame(results)
+        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.')}"
         )
+        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:
-        return f"Submission failed: {str(e)}", pd.DataFrame(results)
+        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 Interface ---
+
+# --- Build Gradio Interface using Blocks ---
 with gr.Blocks() as demo:
-    gr.Markdown("# LLM Agent Evaluation")
-    
-    with gr.Accordion("Instructions", open=False):
-        gr.Markdown("""
-        1. Click the login button
-        2. Authorize with your Hugging Face account
-        3. Click 'Run Evaluation'
-        """)
-    
+    gr.Markdown("# Advanced Agent Evaluation Runner")
+    gr.Markdown(
+        """
+        **Instructions:**
+        1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
+        2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
+        3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
+        ---
+        **Disclaimers:**
+        Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
+        This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
+        """
+    )
+
     gr.LoginButton()
-    
-    run_btn = gr.Button("Run Evaluation", variant="primary")
-    status = gr.Textbox(label="Status")
-    results = gr.DataFrame(label="Results", wrap=True)
-    
-    run_btn.click(
+
+    run_button = gr.Button("Run Evaluation & Submit All Answers")
+
+    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
+    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
+
+    run_button.click(
         fn=run_and_submit_all,
-        outputs=[status, results]
+        outputs=[status_output, results_table]
     )
 
 if __name__ == "__main__":
-    demo.launch(auth_message="Please login with your Hugging Face account")
+    print("\n" + "-"*30 + " App Starting " + "-"*30)
+    # Check for SPACE_HOST and SPACE_ID at startup for information
+    space_host_startup = os.getenv("SPACE_HOST")
+    space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup
+
+    if space_host_startup:
+        print(f"✅ SPACE_HOST found: {space_host_startup}")
+        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
+    else:
+        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
+
+    if space_id_startup: # Print repo URLs if SPACE_ID is found
+        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?). Repo URL cannot be determined.")
+
+    print("-"*(60 + len(" App Starting ")) + "\n")
+
+    print("Launching Gradio Interface for Advanced Agent Evaluation...")
+    demo.launch(debug=True, share=False)