Update app.py

app.py

@@ -2,33 +2,20 @@ import os
 import gradio as gr
 import requests
 import pandas as pd
-#from transformers import Tool, HfAgent
 from datetime import datetime
 import random
-import wikipediaapi
-#import wolframalpha
-from transformers import Tool
-from transformers.agents import HfAgent
-
-# --- Constants ---
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+from smolagents import Tool, HfAgent
 
 # --- Enhanced Agent Definition ---
 class EnhancedAgent:
     def __init__(self):
         print("EnhancedAgent initialized with tools.")
-        # Initialize tools
         self.tools = {
             "calculator": self.calculator,
             "time": self.get_current_time,
-            "wikipedia": self.wikipedia_search,
             "random_choice": self.random_choice
         }
         
-        # Initialize external APIs (would need proper API keys in production)
-        self.wiki_wiki = wikipediaapi.Wikipedia('en')
-       # self.wolfram_client = wolframalpha.Client('YOUR_WOLFRAM_APP_ID')  # Replace with actual ID
-        
     def calculator(self, expression: str) -> str:
         """Evaluate mathematical expressions"""
         try:
@@ -40,15 +27,8 @@ class EnhancedAgent:
         """Get current UTC time"""
         return datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S UTC")
 
-    def wikipedia_search(self, term: str) -> str:
-        """Search Wikipedia for information"""
-        page = self.wiki_wiki.page(term)
-        if page.exists():
-            return page.summary[:500]  # Return first 500 chars of summary
-        return f"No Wikipedia page found for '{term}'"
-
     def random_choice(self, items: str) -> str:
-        """Randomly select from a list of comma-separated items"""
+        """Randomly select from comma-separated items"""
         try:
             options = [x.strip() for x in items.split(",")]
             return f"I choose: {random.choice(options)}"
@@ -56,30 +36,18 @@ class EnhancedAgent:
             return "Error: Please provide comma-separated options"
 
     def __call__(self, question: str) -> str:
-        print(f"Agent processing question: {question[:100]}...")
-        
-        # Simple question classification and routing
+        print(f"Processing question: {question[:100]}...")
         question_lower = question.lower()
         
         # Math questions
         if any(word in question_lower for word in ["calculate", "what is", "how much is", "+", "-", "*", "/"]):
-            try:
-                # Extract math expression
-                expr = question.replace("?", "").replace("what is", "").replace("calculate", "").strip()
-                return self.tools["calculator"](expr)
-            except:
-                pass
+            expr = question.replace("?", "").replace("what is", "").replace("calculate", "").strip()
+            return self.tools["calculator"](expr)
         
         # Time questions
         if any(word in question_lower for word in ["time", "current time", "what time is it"]):
             return self.tools["time"]()
         
-        # Wikipedia questions
-        if any(word in question_lower for word in ["who is", "what is a", "tell me about", "explain"]):
-            # Extract search term
-            term = question.replace("?", "").replace("who is", "").replace("what is a", "").replace("tell me about", "").strip()
-            return self.tools["wikipedia"](term)
-        
         # Random choice questions
         if " or " in question_lower and not any(word in question_lower for word in ["who", "what", "when", "where", "why", "how"]):
             return self.tools["random_choice"](question.replace("?", "").replace(" or ", ","))
@@ -87,179 +55,93 @@ class EnhancedAgent:
         # Fallback to HF Agent for complex questions
         try:
             agent = HfAgent(
-                "https://api-inference.huggingface.co/models/bigcode/starcoder",
+                "bigcode/starcoder",
                 max_new_tokens=150,
                 temperature=0.5
             )
             return agent.run(question)
-        except:
+        except Exception as e:
+            print(f"Agent error: {e}")
             return "I couldn't find an answer to that question."
 
+# --- Gradio App ---
 def run_and_submit_all(profile: gr.OAuthProfile | None):
-    """
-    Fetches all questions, runs the EnhancedAgent 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
-
-    api_url = DEFAULT_API_URL
+    """Main function to run evaluation"""
+    if not profile:
+        return "Please login with your Hugging Face account", None
+    
+    username = profile.username
+    api_url = os.getenv("API_URL", "https://agents-course-unit4-scoring.hf.space")
     questions_url = f"{api_url}/questions"
     submit_url = f"{api_url}/submit"
-
-    # 1. Instantiate Agent
-    try:
-        agent = EnhancedAgent()
-    except Exception as e:
-        print(f"Error instantiating agent: {e}")
-        return f"Error initializing agent: {e}", None
-    
+    space_id = os.getenv("SPACE_ID", "your-space-id")
     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:
+        agent = EnhancedAgent()
+        
+        # Fetch questions
         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:
-        print(f"An unexpected error occurred fetching questions: {e}")
-        return f"An unexpected error occurred fetching questions: {e}", None
-
-    # 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:
-            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:
-             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)
-
-    # 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:
+            return "No questions received from server", None
+
+        # Process questions
+        results_log = []
+        answers_payload = []
+        for item in questions_data:
+            task_id = item.get("task_id")
+            question = item.get("question")
+            if not task_id or not question:
+                continue
+            
+            try:
+                answer = agent(question)
+                answers_payload.append({"task_id": task_id, "submitted_answer": answer})
+                results_log.append({"Task ID": task_id, "Question": question, "Answer": answer})
+            except Exception as e:
+                results_log.append({"Task ID": task_id, "Question": question, "Answer": f"Error: {str(e)}"})
+
+        # Submit answers
+        submission_data = {
+            "username": username,
+            "agent_code": agent_code,
+            "answers": answers_payload
+        }
         response = requests.post(submit_url, json=submission_data, timeout=60)
         response.raise_for_status()
-        result_data = response.json()
-        final_status = (
+        result = response.json()
+        
+        return (
             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.')}"
+            f"Score: {result.get('score', 'N/A')}%\n"
+            f"Correct: {result.get('correct_count', '?')}/{result.get('total_attempted', '?')}\n"
+            f"Message: {result.get('message', '')}",
+            pd.DataFrame(results_log)
         )
-        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
+        return f"Error: {str(e)}", None
 
-
-# --- Build Gradio Interface using Blocks ---
+# --- Gradio Interface ---
 with gr.Blocks() as demo:
-    gr.Markdown("# Enhanced 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' to fetch questions, run the enhanced agent, submit answers, and see the score.
-
-        This agent includes:
-        - Math calculation capabilities
-        - Time lookup
-        - Wikipedia integration
-        - Random choice selection
-        - Fallback to HF's StarCoder model for complex questions
-        """
-    )
-
+    gr.Markdown("""
+    # Hugging Face Agent Evaluation
+    Submit your agent to be evaluated against the benchmark questions.
+    """)
+    
     gr.LoginButton()
-
-    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(
+    run_btn = gr.Button("Run Evaluation & Submit Answers")
+    
+    status_output = gr.Textbox(label="Results", interactive=False)
+    results_table = gr.DataFrame(label="Question Log", wrap=True)
+    
+    run_btn.click(
         fn=run_and_submit_all,
         outputs=[status_output, results_table]
     )
 
 if __name__ == "__main__":
-    print("\n" + "-"*30 + " App Starting " + "-"*30)
-    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}.hf.space")
-
-    if space_id_startup:
-        print(f"✅ SPACE_ID found: {space_id_startup}")
-        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
-
-    print("-"*(60 + len(" App Starting ")) + "\n")
-    print("Launching Gradio Interface for Enhanced Agent Evaluation...")
-    demo.launch(debug=True, share=False)
+    demo.launch()