Update app.py

app.py

@@ -8,34 +8,250 @@ from smolagents import ToolCallingAgent
 from smolagents import DuckDuckGoSearchTool
 from smolagents import Tool
 import traceback
-from huggingface_hub import InferenceClient, HfApi
-from huggingface_hub.utils import HfHubHTTPError
 # (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 print("Token loaded:", bool(os.getenv("chatbotagenthf")))
-from huggingface_hub import InferenceClient, HfApi
-api_key=os.getenv("chatbotagenthf")
-api = HfApi()
-client = InferenceClient(token=api_key)
-
-# Example list of chat/instruction models to test
-models = [
-    "mistralai/Mistral-7B-Instruct-v0.3",
-    "HuggingFaceH4/zephyr-7b-beta",
-    "google/gemma-2b-it",
-    "tiiuae/falcon-7b-instruct",
-    "meta-llama/Llama-3.1-8B-Instruct"
-]
-
-client = InferenceClient()
-api = HfApi()
-
-for model in models:
-    print(f"🔎 Testing {model}...")
+custom_prompt = """
+You are an intelligent AI agent participating in the GALA (Generative Agent Learning Assignment).
+Your goal is to answer each question accurately, concisely, and factually.
+
+Instructions:
+1. Analyze the user's question carefully.
+2. If you can answer it directly from your own knowledge, do so in one or two sentences.
+3. If the question requires recent, factual, or numeric data (e.g. "current CEO", "latest version", "in 2025", etc.), use the DuckDuckGoSearchTool to find the answer.
+4. When using a tool, be precise in your query and summarize the result clearly.
+5. Always provide clean, readable text without markdown or citations unless explicitly requested.
+6. If a question is ambiguous, briefly explain your assumption before answering.
+7. Never refuse to answer unless the question is unrelated to the task or violates safety rules.
+
+Format:
+- Respond directly with the final answer only.
+- Do not include reasoning steps or technical metadata.
+
+Example behaviors:
+User: Who is the CEO of OpenAI?
+→ Use search tool → "The CEO of OpenAI is Sam Altman."
+
+User: What is the capital of Japan?
+→ Direct answer → "The capital of Japan is Tokyo."
+
+Remember: you are being evaluated for correctness and clarity.
+"""
+
+@Tool
+def calculatorTool(query:str) -> str:
+    try:
+        result=eval(query)
+        return str(result)
+    except Exception as e:
+        return f"error while calculating {e}"        
+CalculatorTool=Tool(
+    name="calculator",
+    description="it calculates basic arithmetics",
+    func=calculatorTool
+)
+class BasicAgent:
+    def __init__(self):
+        try:
+            api_key=os.getenv("chatbotagenthf")
+            model =InferenceClientModel(model_id="HuggingFaceH4/zephyr-7b-beta",token=api_key)
+
+            self.agent = ToolCallingAgent(
+                tools=[DuckDuckGoSearchTool()],
+                model=model
+            )
+
+            print("✅ Agent initialized successfully.")
+
+        except Exception as e:
+            print(f"❌ Error initializing agent: {e}")
+            traceback.print_exc()
+            raise e
+
+    def __call__(self, question: str) -> str:
+        try:
+            print(f"\n➡️ Agent received question: {question[:80]}")
+            agent_answer = self.agent.run(question)
+            print(f"✅ Agent returning answer: {agent_answer}")
+            return agent_answer
+        except Exception as e:
+            print(f"❌ Agent encountered an error: {e}")
+            traceback.print_exc()
+            return f"Error generating answer: {e}"
+
+
+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:
-        # just check if model exists and is accessible
-        api.model_info(model, token="your_HF_token_here")
-        print(f"✅ Accessible: {model}")
-    except HfHubHTTPError as e:
-        print(f"❌ Not accessible: {model} -> {e}")
+        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:
+            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:
+             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.")
+        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}")
+    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
+    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
+
+
+# --- 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
+    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" + "-"*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)