Update app.py

app.py

@@ -1,145 +1,194 @@
 import os
 import gradio as gr
 import requests
+import inspect
 import pandas as pd
-from datetime import datetime
-from smolagents import Tool, ToolCallingAgent
-from smolagents.models import InferenceClientModel
 
-# Constants
+# (Keep Constants as is)
+# --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-# --- Custom Tools ---
-class CalculatorTool(Tool):
-    name = "calculator"
-    description = "Performs mathematical calculations"
-    input_schema = {
-        "expression": {
-            "type": "string",
-            "description": "Math expression to evaluate (e.g. '2+2')"
-        }
-    }
-    output_schema = {
-        "result": {
-            "type": "string",
-            "description": "The result of the expression"
-        }
-    }
-
-    def use(self, expression: str) -> dict:
-        try:
-            return {"result": str(eval(expression))}
-        except Exception as e:
-            return {"result": f"Error: {e}"}
-
-
-class TimeTool(Tool):
-    name = "current_time"
-    description = "Gets current UTC time"
-    input_schema = {}
-    output_schema = {
-        "time": {
-            "type": "string",
-            "description": "Current time in UTC"
-        }
-    }
-
-    def use(self) -> dict:
-        return {"time": datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S UTC")}
-
-# --- Agent ---
-class LocalAgent:
+# --- Basic Agent Definition ---
+# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
+class BasicAgent:
     def __init__(self):
-        self.tools = [CalculatorTool(), TimeTool()]
-        self.agent = ToolCallingAgent(
-            tools=self.tools,
-            model=InferenceClientModel(
-                model_id="HuggingFaceH4/zephyr-7b-beta",
-                api_base="https://api-inference.huggingface.co/models"
-            )
-        )
-
+        print("BasicAgent initialized.")
     def __call__(self, question: str) -> str:
-        question_lower = question.lower()
-        if any(op in question_lower for op in ["calculate", "+", "-", "*", "/", "what is"]):
-            return CalculatorTool().use(question.replace("?", ""))["result"]
-        if "time" in question_lower:
-            return TimeTool().use()["time"]
+        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):
+    """
+    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.")
+        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 ( 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}")
+    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:
-            return str(self.agent.run(question))
+            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:
-            return f"Error: {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}"})
 
-# --- Evaluation Function ---
-def run_and_submit_all(profile: gr.OAuthProfile | None):
-    if not profile:
-        return "Please login first.", None
+    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)
 
-    space_id = os.getenv("SPACE_ID", "local-test")
-    api_url = os.getenv("API_URL", DEFAULT_API_URL)
+    # 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:
-        agent = LocalAgent()
-        questions = requests.get(f"{api_url}/questions", timeout=15).json()
-
-        answers = []
-        logs = []
-
-        for q in questions:
-            try:
-                ans = agent(q["question"])
-                answers.append({
-                    "task_id": q["task_id"],
-                    "submitted_answer": ans
-                })
-                logs.append({
-                    "Task ID": q["task_id"],
-                    "Question": q["question"],
-                    "Answer": ans
-                })
-            except Exception as e:
-                logs.append({
-                    "Task ID": q["task_id"],
-                    "Question": q["question"],
-                    "Answer": f"Error: {e}"
-                })
-
-        submission = {
-            "username": profile.username,
-            "agent_code": f"https://huggingface.co/spaces/{space_id}",
-            "answers": answers
-        }
-
-        result = requests.post(f"{api_url}/submit", json=submission, timeout=60).json()
-
-        return (
-            f"✅ Score: {result.get('score', 'N/A')}%\n"
-            f"Correct: {result.get('correct_count', '?')}/{result.get('total_attempted', '?')}",
-            pd.DataFrame(logs)
+        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:
-        return f"Evaluation failed: {e}", pd.DataFrame([])
+        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("# 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.
+        """
+    )
 
-# --- Gradio UI ---
-with gr.Blocks(title="Agent Evaluation") as app:
-    gr.Markdown("## 🤖 Agent Evaluation with smolagents")
-    gr.Markdown("Login, then click 'Run Evaluation' to test your agent.")
+    gr.LoginButton()
 
-    gr.LoginButton()               # Login button visible
-    profile = gr.OAuthProfile()   # Profile input (not visible)
+    run_button = gr.Button("Run Evaluation & Submit All Answers")
 
-    run_btn = gr.Button("🚀 Run Evaluation")
-    output = gr.Textbox(label="Evaluation Result")
-    results_table = gr.DataFrame(label="Answer Log")
+    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_btn.click(
+    run_button.click(
         fn=run_and_submit_all,
-        inputs=[profile],          # ✅ Must include this
-        outputs=[output, results_table]
+        outputs=[status_output, results_table]
     )
 
 if __name__ == "__main__":
-    app.launch()
-
-
+    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)