Update app.py

app.py

@@ -1,167 +1,202 @@
 import os
 import gradio as gr
 import requests
+import inspect
 import pandas as pd
-from smolagents import CodeAgent, DuckDuckGoSearchTool
+from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel
 
+# (Keep Constants and BasicAgent class as is)
+# --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-class HfApiModel:
-    def __init__(self, model_name="tiiuae/falcon-7b-instruct", api_key=None):
-        self.model_name = model_name
-        self.api_key = api_key or os.getenv("HF_TOKEN")
-        if not self.api_key:
-            raise ValueError("HF_TOKEN environment variable is missing.")
-
-    def __call__(self, prompt):
-        # Handle prompt safely (ChatMessage, nested lists, etc.)
-        if hasattr(prompt, "content"):
-            prompt = prompt.content
-        elif isinstance(prompt, list):
-            flat_parts = []
-            for m in prompt:
-                if isinstance(m, list):
-                    flat_parts.extend(str(sub) for sub in m)
-                elif hasattr(m, "content"):
-                    flat_parts.append(str(m.content))
-                else:
-                    flat_parts.append(str(m))
-            prompt = " ".join(flat_parts)
-        else:
-            prompt = str(prompt)
-
-        url = f"https://api-inference.huggingface.co/models/{self.model_name}"
-        headers = {"Authorization": f"Bearer {self.api_key}"}
-        payload = {
-            "inputs": prompt,
-            "options": {"wait_for_model": True}
-        }
-
-        response = requests.post(url, headers=headers, json=payload, timeout=60)
-        response.raise_for_status()
-        output = response.json()
-
-        try:
-            return output[0]["generated_text"]
-        except Exception:
-            return f"ERROR: {output}"
-
-    def generate(self, prompt, **kwargs):
-        return self.__call__(prompt)
-
+# --- Basic Agent Definition ---
 class BasicAgent:
     def __init__(self):
-        print("✅ BasicAgent initialized.")
-        self.agent = CodeAgent(
-            tools=[DuckDuckGoSearchTool()],
-            model=HfApiModel(model_name="tiiuae/falcon-7b-instruct")
-        )
+        print("BasicAgent initialized.")
+        self.agent = CodeAgent(tools=[DuckDuckGoSearchTool()], model=HfApiModel())
 
         SYSTEM_PROMPT = """You are a general AI assistant. I will ask you a question. Report your thoughts, and
-finish your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER].
-YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated
-list of numbers and/or strings.
-If you are asked for a number, don't use comma to write your number neither use units such as $ or
-percent sign unless specified otherwise.
-If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the
-digits in plain text unless specified otherwise.
-If you are asked for a comma separated list, apply the above rules depending of whether the element
-to be put in the list is a number or a string.
-"""
-        self.agent.prompt_templates["system_prompt"] += SYSTEM_PROMPT
-
+        finish your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER].
+        YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated
+        list of numbers and/or strings.
+        If you are asked for a number, don't use comma to write your number neither use units such as $ or
+        percent sign unless specified otherwise.
+        If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the
+        digits in plain text unless specified otherwise.
+        If you are asked for a comma separated list, apply the above rules depending of whether the element
+        to be put in the list is a number or a string.
+        """
+        self.agent.prompt_templates["system_prompt"] = self.agent.prompt_templates["system_prompt"] + SYSTEM_PROMPT
+        
     def __call__(self, question: str) -> str:
-        print(f"📥 Agent received question: {question[:60]}...")
+        print(f"Agent received question (first 50 chars): {question[:50]}...")
         final_answer = self.agent.run(question)
-        print(f"📤 Agent returning answer: {final_answer}")
+        print(f"Agent returning final answer: {final_answer}")
         return final_answer
 
-def run_and_submit_all(profile: gr.OAuthProfile | None):
-    space_id = os.getenv("SPACE_ID")
+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 = profile.username
-        print(f"🔐 Logged in as: {username}")
+        username= f"{profile.username}"
+        print(f"User logged in: {username}")
     else:
-        return "⚠️ Please login with Hugging Face to continue.", None
+        print("User not logged in.")
+        return "Please Login to Hugging Face with the button.", None
 
-    questions_url = f"{DEFAULT_API_URL}/questions"
-    submit_url = f"{DEFAULT_API_URL}/submit"
+    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)
     try:
         agent = BasicAgent()
     except Exception as e:
-        return f"❌ Agent initialization failed: {e}", None
-
-    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else "Unknown"
-
+        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}")
     try:
-        response = requests.get(questions_url, timeout=30)
+        response = requests.get(questions_url, timeout=15)
         response.raise_for_status()
         questions_data = response.json()
         if not questions_data:
-            return "❌ No questions received from the server.", None
+             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:
-        return f"❌ Failed to fetch questions: {e}", None
+        print(f"An unexpected error occurred fetching questions: {e}")
+        return f"An unexpected error occurred fetching questions: {e}", None
 
-    answers_payload = []
+    # 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:
-            submitted_answer = f"AGENT ERROR: {e}"
-        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"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:
-        return "⚠️ Agent failed to generate any answers.", pd.DataFrame(results_log)
+        print("Agent did not produce any answers to submit.")
+        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
 
-    submission_data = {
-        "username": username.strip(),
-        "agent_code": agent_code,
-        "answers": answers_payload
-    }
+    # 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()
-        status = (
-            f"✅ Submission Successful!\n"
+        final_status = (
+            f"Submission Successful!\n"
             f"User: {result_data.get('username')}\n"
-            f"Score: {result_data.get('score', '?')}% "
-            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')})\n"
-            f"Message: {result_data.get('message', 'No message.')}"
+            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.')}"
         )
-        return status, 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:
-        return f"❌ Submission failed: {e}", pd.DataFrame(results_log)
+        status_message = f"An unexpected error occurred during submission: {e}"
+        print(status_message)
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
+
 
-# Gradio UI
+# --- Build Gradio Interface using Blocks ---
 with gr.Blocks() as demo:
-    gr.Markdown("# 🤖 Basic Agent Evaluation Runner")
-    gr.Markdown("Log in with your Hugging Face account, run your agent, and submit your answers.")
+    gr.Markdown("# Basic Agent Evaluation Runner")
+    gr.Markdown(
+        "Please clone this space, then modify the code to define your agent's logic within the `BasicAgent` class. "
+        "Log in to your Hugging Face account using the button below. This uses your HF username for submission. "
+        "Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score."
+    )
 
     gr.LoginButton()
-    run_button = gr.Button("▶️ Run Evaluation & Submit All Answers")
-    status_output = gr.Textbox(label="📝 Run Status", lines=5, interactive=False)
-    results_table = gr.DataFrame(label="📋 Results", wrap=True)
 
-    run_button.click(fn=run_and_submit_all, outputs=[status_output, results_table])
+    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("🚀 Launching Gradio app...")
-    demo.launch(debug=True, share=False)
+    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)