Update app.py

app.py

@@ -1,229 +1,194 @@
+import os
+import gradio as gr
 import requests
 import inspect
 import pandas as pd
-from agent.agent import chat_with_agent  # Import your agent function
-
 
+# (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-# --- Your Smart Agent Definition ---
-# ----- THIS IS WHERE YOU BUILD YOUR AGENT ------
-class SmartAgent:
+# --- Basic Agent Definition ---
+# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
+class BasicAgent:
     def __init__(self):
-        print("🤖 SmartAgent initialized with weather, time, and web search capabilities!")
-        print("   Available tools: get_weather, web_search")
-    
+        print("BasicAgent initialized.")
     def __call__(self, question: str) -> str:
-        print(f"📝 Agent received question: {question[:100]}...")
-        try:
-            # Use your chat_with_agent function with tools
-            answer = chat_with_agent(question)
-            print(f"✅ Agent returning answer: {answer[:100]}...")
-            return answer
-        except Exception as e:
-            error_msg = f"❌ Agent error: {str(e)}"
-            print(f"❌ Agent error: {error_msg}")
-            return f"I apologize, but I encountered an error: {str(e)}"
+        print(f"Agent received question (first 50 chars): {question[:50]}...")
+        fixed_answer = "This is a default answer."
+        print(f"Agent returning fixed answer: {fixed_answer}")
+        return fixed_answer
 
-def run_and_submit_all(profile: gr.OAuthProfile | None):
+def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
-    Fetches all questions, runs the SmartAgent on them, submits all answers,
+    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}")
+        username= f"{profile.username}"
+        print(f"User logged in: {username}")
     else:
-        print("❌ User not logged in.")
+        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 Your Smart Agent
+    # 1. Instantiate Agent ( modify this part to create your agent)
     try:
-        print("�� Initializing SmartAgent...")
-        agent = SmartAgent()  # Use your SmartAgent instead of BasicAgent
-        print("✅ SmartAgent initialized successfully!")
+        agent = BasicAgent()
     except Exception as e:
-        print(f"❌ Error instantiating agent: {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
+    # 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)
     agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
-    print(f"�� Agent code link: {agent_code}")
+    print(agent_code)
 
     # 2. Fetch Questions
-    print(f"�� Fetching questions from: {questions_url}")
+    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.")
+             print("Fetched questions list is empty.")
              return "Fetched questions list is empty or invalid format.", None
-        print(f"✅ Fetched {len(questions_data)} questions successfully!")
+        print(f"Fetched {len(questions_data)} questions.")
     except requests.exceptions.RequestException as e:
-        print(f"❌ Error fetching questions: {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"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}")
+        print(f"An unexpected error occurred fetching questions: {e}")
         return f"An unexpected error occurred fetching questions: {e}", None
 
-    # 3. Run your Smart Agent
+    # 3. Run your Agent
     results_log = []
     answers_payload = []
-    print(f"�� Running SmartAgent on {len(questions_data)} questions...")
-    
-    for i, item in enumerate(questions_data, 1):
+    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}")
+            print(f"Skipping item with missing task_id or question: {item}")
             continue
-        
-        print(f"   [{i}/{len(questions_data)}] Processing task {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, "Submitted Answer": submitted_answer})
-            print(f"   ✅ Task {task_id} completed successfully")
         except Exception as e:
-             print(f"   ❌ Error running agent on task {task_id}: {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.")
+        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"🎉 SmartAgent finished! Submitting {len(answers_payload)} answers for user '{username}'..."
+    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_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"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"Message: {result_data.get('message', 'No message received.')}"
         )
-        print("✅ Submission successful!")
+        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}"
+        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."
+        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}"
+        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}"
+        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:  # Removed auth=None parameter
-    gr.Markdown("# 🤖 Smart Agent Evaluation Runner")
+with gr.Blocks() as demo:
+    gr.Markdown("# Basic Agent Evaluation Runner")
     gr.Markdown(
         """
-        ## 🎯 **Instructions:**
-
-        1. **🔧 Agent Setup**: This space uses a SmartAgent with weather, time, and web search capabilities
-        2. **�� Login**: Log in to your Hugging Face account using the button below
-        3. **🚀 Run**: Click 'Run Evaluation & Submit All Answers' to test your agent and get scored
-
+        **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.
         ---
-        ## 🛠️ **Your Agent Features:**
-        - **🌤️ Weather Tool**: Get current weather for any location
-        - **🔍 Web Search**: Search the web for information
-        - **⏰ Time Tool**: Get current time in different timezones
-        - **�� Smart Responses**: Concise, accurate answers
-
-        ---
-        ## ⚠️ **Disclaimers:**
-        - The submission process may take several minutes as your agent processes all questions
-        - This is a basic setup - feel free to enhance it with caching, async processing, etc.
-        - Keep your space public so others can see your code
+        **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.
         """
     )
 
-    run_button = gr.Button("🚀 Run Evaluation & Submit All Answers", variant="primary")
+    gr.LoginButton()
 
-    status_output = gr.Textbox(
-        label="📊 Run Status / Submission Result", 
-        lines=8, 
-        interactive=False,
-        placeholder="Click the button above to start evaluation..."
-    )
-    
-    results_table = gr.DataFrame(
-        label="📋 Questions and Agent Answers", 
-        wrap=True,
-        headers=["Task ID", "Question", "Submitted Answer"]
-    )
+    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
+    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" + "="*50)
-    print("🚀 Smart Agent Evaluation System Starting")
-    print("="*50)
-    
+    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: https://{space_host_startup}.hf.space")
+        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
     else:
-        print("ℹ️  SPACE_HOST environment variable not found (running locally?)")
+        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: https://huggingface.co/spaces/{space_id_startup}/tree/main")
+        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("-"*50)
-    print("🎯 Launching Smart Agent Evaluation Interface...")
-    print("-"*50)
-    
-    # Launch with proper configuration for Hugging Face
-    demo.launch(
-        debug=False,
-        share=True
-    )
+        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)