Update app.py

app.py

@@ -1,142 +1,207 @@
+""" Basic Agent Evaluation Runner"""
 import os
+import inspect
 import gradio as gr
 import requests
 import pandas as pd
+from langchain_core.messages import HumanMessage
+from agent import build_graph
 
-# GAIA evaluation API
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-
-# --- Basic Agent Definition (HTTP to Hugging Face Inference API) ---
-class BasicAgent:
-    def __init__(self):
-        self.api_token = os.getenv("HUGGINGFACEHUB_API_TOKEN")
-        if not self.api_token:
-            raise ValueError("Missing HUGGINGFACEHUB_API_TOKEN in Secrets")
-
-        # Mistral-7B-Instruct inference endpoint
-        self.model_url = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.1"
-        self.headers = {
-            "Authorization": f"Bearer {self.api_token}",
-            "Content-Type": "application/json"
-        }
-        print("✅ BasicAgent initialized with Mistral-7B-Instruct-v0.1")
-
-    def __call__(self, question: str) -> str:
-        # Wrap for instruct format (Mistral expects <s>[INST] … [/INST])
-        prompt = f"<s>[INST] {question.strip()} [/INST]"
-        print(f"📥 Prompting: {prompt[:60]}…")
-
-        payload = {
-            "inputs": prompt,
-            "options": {"wait_for_model": True},
-            "parameters": {"max_new_tokens": 200}
-        }
 
-        try:
-            resp = requests.post(self.model_url, headers=self.headers, json=payload, timeout=60)
-            resp.raise_for_status()
-            result = resp.json()
-
-            # The API returns a list of {generated_text: …}
-            if isinstance(result, list) and "generated_text" in result[0]:
-                answer = result[0]["generated_text"].strip()
-            elif isinstance(result, dict) and "generated_text" in result:
-                answer = result["generated_text"].strip()
-            else:
-                print("⚠️ Unexpected format:", result)
-                answer = "error: unexpected format"
-
-            print(f"📤 Answer: {answer}")
-            return answer
-
-        except Exception as e:
-            print(f"❌ Inference error: {e}")
-            return f"error: {e}"
 
+# (Keep Constants as is)
+# --- Constants ---
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-# --- Fetch GAIA questions, run agent, submit answers ---
-def run_and_submit_all(profile: gr.OAuthProfile | None):
-    if not profile:
-        return "Please log in to Hugging Face.", None
+# --- Basic Agent Definition ---
+# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
 
-    username = profile.username
-    print(f"🔐 Logged in as {username}")
 
-    # Endpoints
-    questions_url = f"{DEFAULT_API_URL}/questions"
-    submit_url    = f"{DEFAULT_API_URL}/submit"
-    space_id      = os.getenv("SPACE_ID")
-    agent_code    = f"https://huggingface.co/spaces/{space_id}/tree/main"
+class BasicAgent:
+    """A langgraph agent."""
+    def __init__(self):
+        print("BasicAgent initialized.")
+        self.graph = build_graph()
 
-    # 1. Initialize agent
+    def __call__(self, question: str) -> str:
+        print(f"Agent received question (first 50 chars): {question[:50]}...")
+        messages = [HumanMessage(content=question)]
+        result = self.graph.invoke({"messages": messages})
+        answer = result['messages'][-1].content
+        return answer  # kein [14:] mehr nötig!
+
+
+
+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:
-        return f"Agent init error: {e}", None
-
-    # 2. Fetch questions
+        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:
-        r = requests.get(questions_url, timeout=15)
-        r.raise_for_status()
-        questions = r.json()
-    except Exception as e:
+        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 agent
-    results = []
+    # 3. Run your Agent
+    results_log = []
     answers_payload = []
-    for item in questions:
+    print(f"Running agent on {len(questions_data)} questions...")
+    for item in questions_data:
         task_id = item.get("task_id")
-        q_text  = item.get("question")
-        if not task_id or not q_text:
+        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
-
-        ans = agent(q_text)
-        answers_payload.append({"task_id": task_id, "submitted_answer": ans})
-        results.append({"Task ID": task_id, "Question": q_text, "Answer": ans})
+        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:
-        return "No answers generated.", pd.DataFrame(results)
-
-    # 4. Submit
-    submission = {
-        "username":   username,
-        "agent_code": agent_code,
-        "answers":    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:
-        r = requests.post(submit_url, json=submission, timeout=60)
-        r.raise_for_status()
-        data = r.json()
-        status = (
-            f"🎉 Submission Successful!\n"
-            f"User: {data.get('username')}\n"
-            f"Score: {data.get('score')}% "
-            f"({data.get('correct_count')}/{data.get('total_attempted')} correct)\n"
-            f"{data.get('message')}"
+        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 = f"Submission error: {e}"
-
-    return status, pd.DataFrame(results)
+        status_message = f"An unexpected error occurred during submission: {e}"
+        print(status_message)
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
 
 
-# --- Gradio UI ---
+# --- Build Gradio Interface using Blocks ---
 with gr.Blocks() as demo:
-    gr.Markdown("# 🤖 GAIA Level 1 Agent (Mistral-7B API)")
+    gr.Markdown("# Basic Agent Evaluation Runner")
     gr.Markdown(
-        "1. Log in with Hugging Face  \n"
-        "2. Click **Run Evaluation & Submit All Answers**  \n"
-        "3. View your score on the leaderboard"
+        """
+        **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 Evaluation & Submit All Answers")
-    status_box   = gr.Textbox(label="Result", lines=5, interactive=False)
-    results_table = gr.DataFrame(label="QA Log")
 
-    run_btn.click(fn=run_and_submit_all, outputs=[status_box, results_table])
+    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("🚀 Launching GAIA Agent App")
-    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)