Update app.py

app.py

@@ -1,249 +1,139 @@
 import os
 import gradio as gr
 import requests
-import inspect
 import pandas as pd
-
-# add ->
 from smolagents import CodeAgent, InferenceClientModel, DuckDuckGoSearchTool
-# <- add
 
-# (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:
     def __init__(self):
-        print("BasicAgent initialized.")
+        # uso el clasico Qwen 32B
+        model_id = "Qwen/Qwen2.5-Coder-32B-Instruct"
         
-        # 1. Definimos el modelo
-        # Usamos Qwen 2.5 Coder, excelente para seguir instrucciones lógicas
-        # model_id = "meta-llama/Llama-3.3-70B-Instruct"
-        model_id = "qwen/Qwen2.5-Coder-32B-Instruct"
-
-        # 2. Definimos la herramienta de búsqueda
         search_tool = DuckDuckGoSearchTool()
         
-        # 3. Inicializamos el modelo con la clase NUEVA (InferenceClientModel)
         self.model = InferenceClientModel(
             model_id=model_id,
             token=os.getenv("HF_TOKEN")
         )
 
-        # 4. Creamos el agente
-        # CodeAgent es ideal porque puede escribir código Python para resolver tareas complejas
+        # 4. CON PERMISOS EXTRA
         self.agent = CodeAgent(
             tools=[search_tool], 
             model=self.model,
-            max_steps=7, 
-            verbosity_level=1
+            max_steps=12,
+            verbosity_level=1,
+            additional_authorized_imports=['csv', 'pandas', 'bs4', 'requests', 're', 'collections', 'itertools']
         )
 
     def __call__(self, question: str) -> str:
-        print(f"Agent received question: {question}")
-        
-        prompt = f""" 
-        IMPORTANT INSTRUCTIONS:
-        1. Use Python code or Search ONLY if needed.
-        2. Once you have the answer, print it clearly.
-        3. CRITICAL: The final return value must be a CLEAN string. 
-           - NO markdown formatting (no **bold**, no `code`).
-           - NO explanations like "The answer is".
-           - Just the raw data (e.g., "14", "Paris", "apple, banana").
-
-        Answer the following question as accurately as possible.
+        prompt = f"""
+        TASK: Answer the following question.
         QUESTION: {question}
+        
+        RULES:
+        1. If reading a file (CSV/Excel), USE 'pandas' or 'csv' libraries.
+        2. Solve step by step using Python.
+        3. FINAL OUTPUT: Return ONLY the raw answer string.
+           - Do NOT output markdown code blocks (```).
+           - Do NOT write "The answer is".
+           - Just the value (e.g., "14", "Paris", "apple, banana").
         """
 
         try:
-            # Ejecutar el agente
             answer = self.agent.run(prompt)
             clean = str(answer).strip()
-
-            if "<code>" in clean or "Calling tools:" in clean:
-                return "Agent Error: Output contains raw code"
-                
-            return clean 
+            
+            # Limpieza básica de emergencia por si hay código
+            if "Final Answer:" in clean:
+                clean = clean.split("Final Answer:")[-1].strip()
+            
+            return clean
             
         except Exception as e:
             print(f"Error executing agent: {e}")
             return "Error parsing answer"
-        
 
-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
+def run_and_submit_all(profile: gr.OAuthProfile | None):
+    space_id = os.getenv("SPACE_ID")
 
     if profile:
-        username= f"{profile.username}"
+        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"
+    
+    agent_code = f"[https://huggingface.co/spaces/](https://huggingface.co/spaces/){space_id}/tree/main"
 
-    # 1. Instantiate Agent ( modify this part to create your agent)
-    try:
-        agent = BasicAgent()
-    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)
-    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
-    print(agent_code)
-
-    # 2. Fetch Questions
-    print(f"Fetching questions from: {questions_url}")
+    # 1. Fetch 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
     except Exception as e:
-        print(f"An unexpected error occurred fetching questions: {e}")
-        return f"An unexpected error occurred fetching questions: {e}", None
+        return f"Error fetching questions: {e}", None
 
-    # 3. Run your Agent
     results_log = []
     answers_payload = []
+    
     print(f"Running agent on {len(questions_data)} questions...")
+    
+    # 2. Loop de Preguntas
     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
+        if not task_id: continue
+        
+        print(f"Processing Task: {task_id}")
+        
         try:
-            submitted_answer = agent(question_text)
+            # <--- CORRECCIÓN IMPORTANTE 2: 
+            # ¡Instanciamos el agente AQUÍ DENTRO! 
+            # Esto reinicia su cerebro para cada pregunta nueva.
+            current_agent = BasicAgent() 
+            
+            submitted_answer = current_agent(question_text)
+            
+            # Verificación de seguridad para el Error 500
+            if len(submitted_answer) > 200:
+                submitted_answer = submitted_answer[:200]
+
             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}"})
+             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": "Error"})
 
-    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 
+    # 3. Submit
     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_payload)} answers...")
+    
     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
+            result_data = response.json()
+            final_status = f"Submission Successful! Score: {result_data.get('score', 'N/A')}%"
+        except:
+            final_status = f"Submission Failed. Response: {response.text}"
+            
+        return final_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
-
+        return f"Submission Failed: {e}", pd.DataFrame(results_log)
 
-# --- Build Gradio Interface using Blocks ---
+# --- Gradio Interface ---
 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.Markdown("# Basic Agent Evaluation Runner (Fixed Memory)")
     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
-    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
-
-    run_button.click(
-        fn=run_and_submit_all,
-        outputs=[status_output, results_table]
-    )
+    status_output = gr.Textbox(label="Status", interactive=False)
+    results_table = gr.DataFrame(label="Results", wrap=True)
+    run_button.click(fn=run_and_submit_all, outputs=[status_output, results_table])
 
 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")
-    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)
+    demo.launch()