Create app.py

app.py

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+import os
+import gradio as gr
+import requests
+import inspect
+import pandas as pd
+
+from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI
+from llama_index.core.agent.workflow import AgentWorkflow
+from llama_index.core.tools import FunctionTool
+
+# --- 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 ):
+        llm = HuggingFaceInferenceAPI(model_name="Qwen/Qwen2.5-Coder-32B-Instruct")
+        self.agent = AgentWorkflow.from_tools_or_functions(
+            [FunctionTool.from_defaults(multiply)],
+            llm=llm
+        )
+    def __call__(self, question: str) -> str:
+        print(f"Agent received question (first 50 chars): {question[:50]}...")
+        response = self.agent.run(question)
+        return str(response)
+
+def multiply(a: int, b: int) -> int:
+    """Multiplies two integers and returns the resulting integer"""
+    return a * b
+
+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("Error instantiating agent: " + str(e))
+        return "Error initializing agent: " + str(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 = "https://huggingface.co/spaces/" + str(space_id ) + "/tree/main"
+    print(agent_code)
+
+    # 2. Fetch Questions
+    print("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("Fetched " + str(len(questions_data)) + " questions.")
+    except requests.exceptions.RequestException as e:
+        print("Error fetching questions: " + str(e))
+        return "Error fetching questions: " + str(e), None
+    except requests.exceptions.JSONDecodeError as e:
+         print("Error decoding JSON response from questions endpoint: " + str(e))
+         print("Response text: " + response.text[:500])
+         return "Error decoding server response for questions: " + str(e), None
+    except Exception as e:
+        print("An unexpected error occurred fetching questions: " + str(e))
+        return "An unexpected error occurred fetching questions: " + str(e), None
+
+    # 3. Run your Agent
+    results_log = []
+    answers_payload = []
+    print("Running agent on " + str(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("Skipping item with missing task_id or question: " + str(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("Error running agent on task " + str(task_id) + ": " + str(e))
+             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": "AGENT ERROR: " + str(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 = "Agent finished. Submitting " + str(len(answers_payload)) + " answers for user \'" + username + "\'..."
+    print(status_update)
+
+    # 5. Submit
+    print("Submitting " + str(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 = (
+            "Submission Successful!\n" +
+            "User: " + str(result_data.get("username")) + "\n" +
+            "Overall Score: " + str(result_data.get("score", "N/A")) + "% " +
+            "(" + str(result_data.get("correct_count", "?")) + "/" + str(result_data.get("total_attempted", "?")) + " correct)\n" +
+            "Message: " + str(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 = "Server responded with status " + str(e.response.status_code) + "."
+        try:
+            error_json = e.response.json()
+            error_detail += " Detail: " + str(error_json.get("detail", e.response.text))
+        except requests.exceptions.JSONDecodeError:
+            error_detail += " Response: " + e.response.text[:500]
+        status_message = "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 = "Submission Failed: Network error - " + str(e)
+        print(status_message)
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
+    except Exception as e:
+        status_message = "An unexpected error occurred during submission: " + str(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("✅ SPACE_HOST found: " + space_host_startup)
+        print("   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("✅ SPACE_ID found: " + space_id_startup)
+        print("   Repo URL: https://huggingface.co/spaces/" + space_id_startup )
+        print("   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)