Update app.py

app.py

@@ -1,139 +1,94 @@
 import os
 import gradio as gr
 import requests
+import inspect
 import pandas as pd
-import json
-import mimetypes
-import tempfile
-import fitz  # PyMuPDF for PDF parsing
-from openai import OpenAI
+from agent import BasicAgent
 
+# (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-OPENAI_FALLBACK_KEY = "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"  # <<< Replace with your actual key here
-
-# --- OpenAI Agent with enhanced formatting and logic ---
-class OpenAIAgent:
-    def __init__(self, model="gpt-4"):
-        self.model = model
-        self.system_prompt = (
-            "You are a precise assistant that always returns a single, direct answer, with no explanation or justification. "
-            "If the task asks for a list, format it exactly as instructed (e.g., comma-separated, alphabetized). "
-            "If there is a file missing, infer the answer using the question context. "
-            "Avoid all disclaimers, apologies, or messages about being a language model."
-        )
-        api_key = os.getenv("OPENAI_API_KEY") or OPENAI_FALLBACK_KEY
-        if not api_key or "sk-" not in api_key:
-            raise ValueError("A valid OpenAI API key must be set in the environment or OPENAI_FALLBACK_KEY.")
-        self.client = OpenAI(api_key=api_key)
-
-    def __call__(self, question: str, task_id: str = None, file_name: str = "") -> str:
-        context = ""
-        if task_id and file_name:
-            try:
-                context = self._fetch_file_context(task_id)
-            except Exception as e:
-                print(f"Warning: Failed to fetch or parse file for task {task_id}: {e}")
-        # Add custom cues based on the question content
-        if "comma separated" in question.lower():
-            question = "Format your answer as a comma-separated list in alphabetical order.\n\n" + question
-        if "only list the ingredients" in question.lower():
-            question = "Output just the ingredient names, comma-separated.\n\n" + question
-        if "numeric output" in question.lower():
-            question = "Only provide the final numeric result, no units or explanation.\n\n" + question
-
-        user_input = f"{question}\n\nAdditional context:\n{context}" if context else question
-        return self._call_openai(user_input)
-
-    def _call_openai(self, prompt: str) -> str:
-        try:
-            response = self.client.chat.completions.create(
-                model=self.model,
-                messages=[
-                    {"role": "system", "content": self.system_prompt},
-                    {"role": "user", "content": prompt},
-                ],
-                temperature=0,
-                max_tokens=256,
-            )
-            return response.choices[0].message.content.strip()
-        except Exception as e:
-            print(f"OpenAI Agent error: {e}")
-            return "ERROR"
-
-    def _fetch_file_context(self, task_id: str) -> str:
-        file_url = f"{DEFAULT_API_URL}/files/{task_id}"
-        response = requests.get(file_url, timeout=10)
-        if response.status_code == 404:
-            print(f"No file found for task {task_id}. Continuing without context.")
-            return ""
-        response.raise_for_status()
-        content_type = response.headers.get("content-type")
-        extension = mimetypes.guess_extension(content_type)
-
-        with tempfile.NamedTemporaryFile(delete=True, suffix=extension) as tmp_file:
-            tmp_file.write(response.content)
-            tmp_file.flush()
 
-            if extension == ".pdf":
-                return self._parse_pdf(tmp_file.name)
-            elif extension in [".txt", ".csv", ".py"]:
-                return response.text
-            else:
-                return f"[Non-text file of type {extension}, not processed.]"
 
-    def _parse_pdf(self, filepath):
-        doc = fitz.open(filepath)
-        return "\n".join([page.get_text() for page in doc]).strip()
+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
 
-
-def run_and_submit_all(profile: gr.OAuthProfile | None):
-    space_id = os.getenv("SPACE_ID")
     if profile:
-        username = f"{profile.username}"
+        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 = OpenAIAgent()
+        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:
-            return "Fetched questions list is empty or invalid format.", None
-    except Exception as e:
+             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")
-        file_name = item.get("file_name", "")
         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, task_id=task_id, file_name=file_name)
+            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:
-            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT 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}"})
 
     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()
@@ -145,27 +100,86 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
             f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
             f"Message: {result_data.get('message', 'No message received.')}"
         )
-        return final_status, pd.DataFrame(results_log)
+        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"Submission Failed: {e}", pd.DataFrame(results_log)
+        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("# OpenAI Agent Evaluation Runner")
-    gr.Markdown("""
+    gr.Markdown("# Basic Agent Evaluation Runner")
+    gr.Markdown(
+        """
         **Instructions:**
-        1.  Clone the space and modify code logic as needed.
-        2.  Login with your Hugging Face account.
-        3.  Click 'Run Evaluation & Submit All Answers' to evaluate.
-    """)
+        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])
+    run_button.click(
+        fn=run_and_submit_all,
+        outputs=[status_output, results_table]
+    )
 
 if __name__ == "__main__":
-    print("Launching Gradio Interface for OpenAI Agent Evaluation...")
-    demo.launch(debug=True, share=False)
+    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)