Upload app.py

app.py

@@ -1,206 +1,217 @@
 import os
-import inspect
 import gradio as gr
 import requests
 import pandas as pd
 from langchain_core.messages import HumanMessage
 from agent import build_graph
 
-
-
-# (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-# --- Basic Agent Definition ---
-# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
-
-
 class BasicAgent:
-    """A langgraph agent."""
+    """A langgraph agent using OpenAI."""
     def __init__(self):
-        print("BasicAgent initialized.")
-        self.graph = build_graph()
+        print("=== INITIALIZING OPENAI BASIC AGENT ===")
+        print(f"Current working directory: {os.getcwd()}")
+        print(f"Files in directory: {os.listdir('.')}")
+        
+        # Check environment variables
+        print("=== ENVIRONMENT VARIABLES ===")
+        for key in sorted(os.environ.keys()):
+            if any(term in key.upper() for term in ['OPENAI', 'API_KEY', 'TOKEN', 'TAVILY']):
+                value = os.environ[key]
+                print(f"{key}: {value[:10] if value else 'None'}...")
+        
+        # Check specifically for OpenAI API key
+        openai_key = os.getenv("OPENAI_API_KEY")
+        if openai_key:
+            print(f"✓ OpenAI API Key found: {openai_key[:15]}...")
+        else:
+            print("✗ OpenAI API Key not found!")
+            print("Please add OPENAI_API_KEY to your Hugging Face Space secrets")
+        
+        try:
+            self.graph = build_graph()
+            print("✓ Graph built successfully")
+        except Exception as e:
+            print(f"✗ Error building graph: {e}")
+            raise e
 
     def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
-        messages = [HumanMessage(content=question)]
-        result = self.graph.invoke({"messages": messages})
-        answer = result['messages'][-1].content
-        return answer  # kein [14:] mehr nötig!
-
-
-
-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.")
+        print(f"=== AGENT CALL ===")
+        print(f"Question: {question[:100]}...")
+        
+        try:
+            messages = [HumanMessage(content=question)]
+            print(f"Invoking graph with messages: {len(messages)}")
+            
+            result = self.graph.invoke({"messages": messages})
+            print(f"Graph result keys: {result.keys() if isinstance(result, dict) else 'Not a dict'}")
+            
+            if 'messages' in result and result['messages']:
+                answer = result['messages'][-1].content
+                print(f"Answer (first 100 chars): {answer[:100]}...")
+                return answer
+            else:
+                print("No messages in result")
+                return "I apologize, but I couldn't generate a response."
+                
+        except Exception as e:
+            print(f"Error in agent call: {e}")
+            return f"Error: {str(e)}"
+
+def run_and_submit_all(profile: gr.OAuthProfile | None):
+    print("=== STARTING RUN AND SUBMIT ===")
+    space_id = os.getenv("SPACE_ID")
+    print(f"Space ID: {space_id}")
+    
+    if not profile:
         return "Please Login to Hugging Face with the button.", None
-
+    
+    username = profile.username
+    print(f"Username: {username}")
+    
     api_url = DEFAULT_API_URL
     questions_url = f"{api_url}/questions"
     submit_url = f"{api_url}/submit"
-
-    # 1. Instantiate Agent ( modify this part to create your agent)
+    
+    print("=== INITIALIZING AGENT ===")
     try:
         agent = BasicAgent()
+        print("✓ Agent initialized successfully")
     except Exception as e:
-        print(f"Error instantiating agent: {e}")
-        return f"Error initializing agent: {e}", None
-    # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
+        error_msg = f"Error initializing agent: {e}"
+        print(error_msg)
+        return error_msg, None
+
     agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
-    print(agent_code)
+    print(f"Agent code URL: {agent_code}")
 
-    # 2. Fetch Questions
-    print(f"Fetching questions from: {questions_url}")
+    print("=== FETCHING QUESTIONS ===")
     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
+        resp_q = requests.get(questions_url, timeout=15)
+        resp_q.raise_for_status()
+        questions = resp_q.json()
+        print(f"✓ Fetched {len(questions)} questions")
     except Exception as e:
-        print(f"An unexpected error occurred fetching questions: {e}")
-        return f"An unexpected error occurred fetching questions: {e}", None
+        error_msg = f"Error fetching questions: {e}"
+        print(error_msg)
+        return error_msg, None
 
-    # 3. Run your Agent
     results_log = []
-    answers_payload = []
-    print(f"Running agent on {len(questions_data)} questions...")
-    for item in questions_data:
+    answers = []
+    
+    print("=== PROCESSING QUESTIONS ===")
+    for i, item in enumerate(questions):
         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}")
+        q = item.get("question")
+        
+        print(f"\n--- Question {i+1}/{len(questions)} ---")
+        print(f"Task ID: {task_id}")
+        print(f"Question: {q[:100]}...")
+        
+        if not task_id or q is None:
+            print("Skipping - missing task_id or question")
             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})
+            print("Calling agent...")
+            ans = agent(q)
+            print(f"Answer: {ans[:100]}...")
+            
+            answers.append({"task_id": task_id, "submitted_answer": ans})
+            results_log.append({"Task ID": task_id, "Question": q, "Submitted Answer": ans})
+            print("✓ Question processed successfully")
+            
         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}"})
+            error_msg = f"ERROR: {e}"
+            print(f"✗ Error processing question: {error_msg}")
+            results_log.append({"Task ID": task_id, "Question": q, "Submitted Answer": error_msg})
 
-    if not answers_payload:
-        print("Agent did not produce any answers to submit.")
+    if not answers:
         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}")
+    print(f"=== SUBMITTING {len(answers)} ANSWERS ===")
+    payload = {"username": username.strip(), "agent_code": agent_code, "answers": answers}
+    
     try:
-        response = requests.post(submit_url, json=submission_data, timeout=60)
-        response.raise_for_status()
-        result_data = response.json()
-        final_status = (
+        resp_s = requests.post(submit_url, json=payload, timeout=60)
+        resp_s.raise_for_status()
+        data = resp_s.json()
+        
+        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.')}"
+            f"User: {data.get('username')}\n"
+            f"Score: {data.get('score', 'N/A')}% "
+            f"({data.get('correct_count', '?')}/{data.get('total_attempted', '?')})\n"
+            f"{data.get('message', '')}"
         )
-        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
+        print("✓ Submission successful")
+        print(status)
+        return status, pd.DataFrame(results_log)
+        
     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
-
+        error_msg = f"Submission Failed: {e}"
+        print(error_msg)
+        return error_msg, pd.DataFrame(results_log)
+
+# Simple test function for debugging
+def test_agent():
+    """Test function to verify agent works"""
+    print("=== TESTING AGENT ===")
+    try:
+        agent = BasicAgent()
+        test_questions = [
+            "What is 2 + 3?",
+            "What is 10 * 5?",
+            "Search for information about Python programming"
+        ]
+        
+        for q in test_questions:
+            print(f"\nTesting: {q}")
+            answer = agent(q)
+            print(f"Answer: {answer}")
+            
+    except Exception as e:
+        print(f"Test failed: {e}")
 
-# --- Build Gradio Interface using Blocks ---
 with gr.Blocks() as demo:
-    gr.Markdown("# Basic 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_button = gr.Button("Run Evaluation & Submit All Answers")
-
-    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
-    # Removed max_rows=10 from DataFrame constructor
+    gr.Markdown("# OpenAI-Powered Agent Evaluation Runner")
+    gr.Markdown("""
+    This agent uses OpenAI's GPT models instead of Hugging Face.
+    
+    ## Setup Instructions:
+    1. Get an OpenAI API key from https://platform.openai.com/api-keys
+    2. Add it as `OPENAI_API_KEY` in your Hugging Face Space secrets
+    3. (Optional) Add `TAVILY_API_KEY` for web search functionality
+    4. Log in with the button below
+    5. Click **Run Evaluation & Submit All Answers**
+    
+    ## Current Configuration:
+    - Model: GPT-3.5-turbo (change to GPT-4 in agent.py if you have access)
+    - Tools: Math operations, Wikipedia search, Arxiv search, Web search (if Tavily configured)
+    """)
+    
+    with gr.Row():
+        gr.LoginButton()
+        test_btn = gr.Button("Test Agent", variant="secondary")
+    
+    run_btn = gr.Button("Run Evaluation & Submit All Answers", variant="primary")
+    status_out = gr.Textbox(label="Run Status / Submission Result", lines=5)
     results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
-
-    run_button.click(
-        fn=run_and_submit_all,
-        outputs=[status_output, results_table]
-    )
+    
+    # Button actions
+    run_btn.click(fn=run_and_submit_all, outputs=[status_out, results_table])
+    test_btn.click(fn=test_agent, outputs=[])
 
 if __name__ == "__main__":
-    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")
+    print("=== STARTING OPENAI GRADIO APP ===")
+    
+    # Quick environment check
+    openai_key = os.getenv("OPENAI_API_KEY")
+    if openai_key:
+        print(f"✓ OpenAI API Key configured: {openai_key[:15]}...")
     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 Basic Agent Evaluation...")
-    demo.launch(debug=True, share=False)
+        print("⚠️  OpenAI API Key not found - please add OPENAI_API_KEY to your Space secrets")
+    
+    demo.launch(debug=True, share=False)