Update app.py

app.py

@@ -2,152 +2,221 @@ import os
 import gradio as gr
 import requests
 import pandas as pd
-import math
+from dotenv import load_dotenv
 
-from smolagents import ToolCallingAgent, tool
-from duckduckgo_search import DDGS
-from openai import OpenAI
+from myagent import BasicAgent  # Unused basic single agent
+from multiagents import MultiAgent
 
-# Load OpenAI API key
-client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+from phoenix.otel import register
+from openinference.instrumentation.smolagents import SmolagentsInstrumentor
 
-# ------------------------
-# Define Tools
-# ------------------------
+# use space_host var to determine if running in HF space or locally, if so register local instrumentation
+space_host_startup = os.getenv("SPACE_HOST")
+if not space_host_startup:
+    register()
+    SmolagentsInstrumentor().instrument()
 
-@tool
-def web_search(query: str) -> str:
-    """Search the web using DuckDuckGo.
+# (Keep Constants as is)
+# --- Constants ---
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+load_dotenv()
 
-    Args:
-        query: The search query to look up.
+max_questions = 20
 
-    Returns:
-        A summary of the top web results.
+def run_and_submit_all(nb_questions: int, profile: gr.OAuthProfile | None): 
     """
+    Fetches all questions, runs my Agent 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"
+    file_url = f"{api_url}/files"    
+    submit_url = f"{api_url}/submit"
+
+    # 1. Instantiate Agent 
     try:
-        with DDGS() as ddgs:
-            results = ddgs.text(query, max_results=3)
-            if not results:
-                return "No results found."
-            return "\n\n".join(
-                f"Title: {r['title']}\nSnippet: {r['body']}\nURL: {r['href']}"
-                for r in results
-            )
+        # agent = BasicAgent()
+        agent = MultiAgent()
     except Exception as e:
-        return f"Search error: {str(e)}"
-
-@tool
-def calculate(expression: str) -> str:
-    """Evaluate a mathematical expression.
-
-    Args:
-        expression: The math expression to evaluate (e.g. '2 + 3 * 5').
-
-    Returns:
-        Result of the calculation.
-    """
+        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:
-        safe_math = {k: v for k, v in math.__dict__.items() if not k.startswith("__")}
-        result = eval(expression, {"__builtins__": None}, safe_math)
-        return str(result)
+        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:
-        return f"Calculation error: {str(e)}"
-
-# ------------------------
-# Define Agent
-# ------------------------
-
-class GAIAAgent:
-    def __init__(self):
-        self.agent = ToolCallingAgent(
-            name="GAIA_Agent",
-            description="""You are an AI assistant that answers questions using tools:
-- Use 'web_search' for looking up facts and recent information.
-- Use 'calculate' for evaluating math expressions.
-Be accurate and concise.""",
-            tools=[web_search, calculate],
-            model=client.chat.completions
-        )
-
-    def __call__(self, question: str) -> str:
+        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 = []
+
+    # for testing keep only some questions
+    questions_data = questions_data[:nb_questions]
+
+    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")
+        file_question_url = None
+        if file_name:
+            file_question_url = f"{file_url}/{task_id}"
+        if not task_id or question_text is None:
+            print(f"Skipping item with missing task_id or question: {item}")
+            continue
         try:
-            response = self.agent.run(question)
-            return str(response)
-        except Exception as e:
-            return f"Agent error: {str(e)}"
+            agent_question = question_text
+            if file_question_url:
+                agent_question += f"\n\nFile URL: {file_question_url}"
 
-# ------------------------
-# Gradio App Logic
-# ------------------------
+            submitted_answer = agent(agent_question)
 
-def run_agent_and_submit(profile: gr.OAuthProfile | None):
-    if not profile:
-        return "⚠️ Please log in to Hugging Face.", None
+            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}"})
 
-    try:
-        agent = GAIAAgent()
-        response = requests.get("https://agents-course-unit4-scoring.hf.space/questions", timeout=20)
-        questions = response.json()
-    except Exception as e:
-        return f"❌ Error fetching questions: {e}", None
+    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)
 
-    results = []
-    answers = []
+    # 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)
 
-    for q in questions:
-        task_id = q.get("task_id")
-        question_text = q.get("question")
-        if not task_id or not question_text:
-            continue
-        try:
-            answer = agent(question_text)
-        except Exception as e:
-            answer = f"Agent error: {e}"
-
-        answers.append({
-            "task_id": task_id,
-            "submitted_answer": answer[:1000]
-        })
-        results.append({
-            "Task ID": task_id,
-            "Question": question_text,
-            "Answer": answer
-        })
-
-    # Submit answers
+    # 5. Submit
+    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
     try:
-        submit_url = "https://agents-course-unit4-scoring.hf.space/submit"
-        payload = {
-            "username": profile.username,
-            "agent_code": f"https://huggingface.co/spaces/{os.getenv('SPACE_ID')}/tree/main",
-            "answers": answers
-        }
-        submit_resp = requests.post(submit_url, json=payload, timeout=60)
-        result_data = submit_resp.json()
-        summary = (
-            f"✅ Submitted {len(answers)} answers\n"
-            f"📊 Score: {result_data.get('score', 'N/A')}%\n"
-            f"✔️ Correct: {result_data.get('correct_count', '?')}/{len(answers)}"
+        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:
-        summary = f"❌ Submission error: {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
 
-    return summary, pd.DataFrame(results)
-
-# ------------------------
-# Gradio Interface
-# ------------------------
 
+# --- Build Gradio Interface using Blocks ---
 with gr.Blocks() as demo:
-    gr.Markdown("# 🤖 GAIA Tool Agent")
-    gr.Markdown("This agent answers GAIA benchmark questions using tool-calling with search and math.")
+    gr.Markdown("# Basic Agent Evaluation Runner")
+    gr.Markdown("""
+                *Special Considerations*: Due to limitation issues, this code depend on local search engine and local speech to text model. Both run through docker, see the readme file."
+                One can achieve similar result, by using Google search API and OpenAI Whisper API.
+        """)
+    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_btn = gr.Button("🔍 Run Agent & Submit")
-    status = gr.Textbox(label="Status", lines=4)
-    results_df = gr.DataFrame(label="Results")
 
-    run_btn.click(fn=run_agent_and_submit, outputs=[status, results_df])
+    nb_questions = gr.Number(value=20)
+
+    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,
+        inputs=[nb_questions],
+        outputs=[status_output, results_table]
+    )
 
 if __name__ == "__main__":
-    demo.launch()
+    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)