Update app.py

app.py

@@ -1,115 +1,194 @@
-import logging
-import hashlib
-import json
 import os
-from smolagents import CodeAgent, tool
-from huggingface_hub import InferenceClient
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-# Cache for answers
-CACHE_FILE = "answer_cache.json"
-if os.path.exists(CACHE_FILE):
-    with open(CACHE_FILE) as f:
-        answer_cache = json.load(f)
-else:
-    answer_cache = {}
-
-def save_cache():
-    with open(CACHE_FILE, "w") as f:
-        json.dump(answer_cache, f)
-
-# ---------- Tools ----------
-@tool
-def calculator(expression: str) -> str:
+import gradio as gr
+import requests
+import inspect
+import pandas as pd
+
+# (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):
+        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):
     """
-    Safely evaluate a mathematical expression.
-
-    Args:
-        expression: A string containing a simple arithmetic expression (e.g., '2 + 2').
-
-    Returns:
-        The result as a string, or an error message if the expression is invalid.
+    Fetches all questions, runs the BasicAgent on them, submits all answers,
+    and displays the results.
     """
-    allowed_chars = set("0123456789+-*/(). ")
-    if not all(c in allowed_chars for c in expression):
-        return "Error: Expression contains disallowed characters."
-    try:
-        result = eval(expression, {"__builtins__": {}}, {})
-        return str(result)
-    except Exception as e:
-        return f"Error: {e}"
+    # --- Determine HF Space Runtime URL and Repo URL ---
+    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
 
-@tool
-def web_search(query: str) -> str:
-    """
-    Search the web for up-to-date information.
+    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
 
-    Args:
-        query: The search query string.
+    api_url = DEFAULT_API_URL
+    questions_url = f"{api_url}/questions"
+    submit_url = f"{api_url}/submit"
 
-    Returns:
-        A string containing up to three search result snippets with titles and URLs,
-        or an error message if the search fails.
-    """
+    # 1. Instantiate Agent ( modify this part to create your agent)
     try:
-        from duckduckgo_search import DDGS
-        with DDGS() as ddgs:
-            results = list(ddgs.text(query, max_results=3))
-        if not results:
-            return "No results found."
-        snippets = []
-        for r in results:
-            snippets.append(f"Title: {r['title']}\nBody: {r['body']}\nURL: {r['href']}")
-        return "\n\n".join(snippets)
-    except ImportError:
-        return "Web search tool not available: install duckduckgo-search"
+        agent = BasicAgent()
     except Exception as e:
-        return f"Search error: {e}"
-
-# ---------- Custom model ----------
-class CustomHFModel:
-    def __init__(self, model_id="HuggingFaceH4/zephyr-7b-beta"):
-        self.client = InferenceClient(model=model_id, token=os.getenv("HF_TOKEN"))
-        self.model_id = model_id
-
-    def __call__(self, messages, **kwargs):
-        response = self.client.chat_completion(
-            messages=messages,
-            max_tokens=500,
-            temperature=0.7,
-            **kwargs
-        )
-        return response.choices[0].message.content
-
-# ---------- Assemble agent ----------
-tools = [calculator]
-try:
-    import duckduckgo_search
-    tools.append(web_search)
-    logger.info("Web search tool enabled.")
-except ImportError:
-    logger.warning("duckduckgo-search not installed, web_search disabled.")
-
-model = CustomHFModel()
-agent = CodeAgent(tools=tools, model=model)
-
-# ---------- Main entry point (called by app.py) ----------
-def solve(question: str) -> str:
-    """This function must be named 'solve' because app.py imports it."""
-    q_hash = hashlib.md5(question.encode()).hexdigest()
-    if q_hash in answer_cache:
-        logger.info(f"Cache hit for question: {question[:50]}...")
-        return answer_cache[q_hash]
-
-    logger.info(f"Processing question: {question[:50]}...")
+        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:
-        answer = agent.run(question)
+        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:
-        logger.error(f"Agent error: {e}")
-        answer = f"Error: {e}"
-
-    answer_cache[q_hash] = answer
-    save_cache()
-    return answer
+        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)