Upload app.py

app.py

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+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
+
+# --- Constants ---
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+
+# --- 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:
+            return str(eval(expression))
+        except:
+            return "Error: Could not evaluate the expression"
+
+    def get_current_time(self) -> str:
+        """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"""
+        try:
+            options = [x.strip() for x in items.split(",")]
+            return f"I choose: {random.choice(options)}"
+        except:
+            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
+        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
+        
+        # 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 ", ","))
+        
+        # Fallback to HF Agent for complex questions
+        try:
+            agent = HfAgent(
+                "https://api-inference.huggingface.co/models/bigcode/starcoder",
+                max_new_tokens=150,
+                temperature=0.5
+            )
+            return agent.run(question)
+        except:
+            return "I couldn't find an answer to that question."
+
+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
+    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
+    
+    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(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:
+        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:
+        status_message = f"An unexpected error occurred during submission: {e}"
+        print(status_message)
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
+
+
+# --- Build Gradio Interface using Blocks ---
+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.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(
+        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)