Update app.py

app.py

@@ -1,142 +1,198 @@
-from smolagents import CodeAgent, tool, OpenAIServerModel
+import os
+import gradio as gr
 import requests
-import io
-from PIL import Image
-from constants import DEFAULT_API_URL
+import inspect
+import pandas as pd
+from agent import Mod4Agent
 
+# (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, OPENROUTER_API: str, OPENROUTER_MODEL: str, api_url: str):
-        self.api_url = api_url
-        self.model = OpenAIServerModel(
-            model_id=OPENROUTER_MODEL,
-            api_base="https://openrouter.ai/api/v1",
-            api_key=OPENROUTER_API
-        )
-        self.agent = None
-
-    def initialize_agent(self):
-        self.agent = CodeAgent(
-            model=self.model,
-            tools=[get_file],
-            add_base_tools=True,
-            additional_authorized_imports=['pandas', 'io']
+    def __init__(self):
+        print("BasicAgent initialized.")
+    def __call__(self, question: str) -> str:
+        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()
+        agent = Mod4Agent()
+    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:
+            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.')}"
         )
-        # self.agent.tools.pop('visit_webpage') # replacing tool
-        return self.agent
-
-
-    def run(self, input: tuple):
-        id, question, file = input
-
-        # check if has image
-        img = None
-        if file and file.split('.')[1] == 'png':
-            img = [Image.open(get_file(file))]
-
-        # make a prompt
-        prompt = f"""
-        You are CodeAgent assistant. The user asks you a question and you provide them with a verified and specific answer.
-        As an agent you have some tools to use. You may use them on demand.
-        Your normal workflow should follow the following sequence:
-        1. You received the question and (in some cases) additional information as a file_id. You analize it.
-        2. At the planning stage you making a sequence of steps to complete this task in planning variable
-        3. At the execution stage you provide an executable python code which will be parsed and executed by the program.
-            Remember to provide executable code here, without any additional characters which may crash the execution and parsing.
-        4. After execution you decide whether you had a final result an you can close the task providing this result or you should continue.
-            If ccontinue, review you previous planning sequence: what is done, what should be change in this plan or what we should add to it?
-        EACH STEP INCLUDES BOTH PLANNING AND EXECUTIONS STAGES:
-            * you provide planning in a separate variable at the beggining of your answer, variable name: planning
-            * execution is the rest code where you trying to achive the planned goals.
-        
-        The example:
-        User: What is Axelord?
-        [Step 1]
-        Agent: 
-            Planning stage:
-                planning = "
-                    Okay, user want me to tell him what is Axelord. I do not have this answer in my memory so I need to find it.
-                    Plan:
-                    1. To search: What is Axelord.
-                    2. Provide an aswer.
-                "
-            Execution stage:
-                query = "What is Axelord?"                                 
-                search_result = web_search(query)                                             
-                print(search_result)
-            Result:
-                The pages about this topic.
-            Output:
-                None
-        [Step 2]
-        Agent:
-            Planning stage:
-                planning = "
-                    Okay, I found some websites about that.
-                    Plan:
-                    1. To search: What is Axelord. - DONE.
-                    2. Visit the webpage to get more inforamtion.
-                    3. Provide an answer.
-                "
-            Execution stage:
-                url = "https://en.wikipedia.org/wiki/Axelord"                           
-                wikipedia_page = visit_webpage(url)                                           
-                print(wikipedia_page) 
-            Result:
-                page html output which contains an answer.
-            Output:
-                FinalAnswerTool(Answer)
-        
-        In the example above you saw how you can use searching tools, but you also has some others.
-        You complete the task step by step. You recognise when the planning stage and you can do planning, and when
-        the execution stage and you MUST provide an executable python code.
-        
-        The user's question is:
-        {question}
-        
+        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.
+
         ---
-        Additional information you may use:
-        file_id: {file}
-        
-        IMPORTANT:
-            * You do not answer in plain text.
-            * You only answer in python code. Every plain text you need to provide you should include in a text variable
-            * You may receive both images sometimes as a context. Do not forget to use them to handle the task.
+        **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.
         """
+    )
 
-        # run
-        return self.agent.run(
-            task = prompt,
-            images = img
-        )
+    gr.LoginButton()
 
-# tools
-@tool
-def get_file(file_id: str) -> io.BytesIO | io.StringIO | None:
-    """A tool that fetches the file's content from the server.
-    Use it every time you need to get file for completing the task.
-    IMPORTANT: ONLY USE IT WHEN THE FILE_ID ARGUMENT IS NOT EMPTY.
-    YOU MUST ENSURE THAT FILE_ID ARGUMENT IS NOT EMPTY WHEN YOU USE IT.
-    Args:
-        file_id: id of a file required to fetch
-    Returns:
-        io.BytesIO: file emulation for .xlsx or .png files. You can use this object as a file itself. It is already converted to BytesIO.
-        io.StringIO: file emulation for .py files. You can use this object as a file itself. It is already converted to StringIO.
-    """
-    # make request
-    response = requests.get(DEFAULT_API_URL+'/files/'+file_id.split('.')[0], timeout=15)
-    response.raise_for_status()
-
-    # prepare the output
-    file_format = file_id.split('.')[1]
-    match file_format:
-        case 'png':
-            print('png uploaded')
-            return io.BytesIO(response.content)
-        case 'xlsx':
-            print('xlsx uploaded')
-            return io.BytesIO(response.content)
-        case 'py':
-            print('py uploaded')
-            return io.StringIO(response.text)
-        case _:
-            return None
+    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)