Update app.py

app.py

@@ -1,71 +1,149 @@
 # app.py
+
 import os
-from dotenv import load_dotenv
-import gradio as gr
+import time
 import requests
 import pandas as pd
+import gradio as gr
 
-from agent import GeminiAgent
-
-# Load environment variables
-load_dotenv()
-
-API_URL = os.getenv("API_URL", "https://agents-course-unit4-scoring.hf.space")
-SPACE_ID = os.getenv("SPACE_ID")
-GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
-
-class BasicAgent:
-    def __init__(self, api_key: str):
-        if not api_key:
-            raise ValueError("GOOGLE_API_KEY environment variable not set.")
-        self.agent = GeminiAgent(api_key)
-
-    def __call__(self, question: str) -> str:
-        return self.agent.run(question)
-
+from smolagents import (
+    CodeAgent,
+    DuckDuckGoSearchTool,
+    PythonInterpreterTool,
+    InferenceClientModel
+)
+
+# --- Configuration ---
+API_URL  = os.getenv("API_URL", "https://agents-course-unit4-scoring.hf.space")
+SPACE_ID = os.getenv("SPACE_ID")                     # e.g. "your-username/your-space"
+HF_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")     # Hugging Face token
+# No need for HF_USERNAME—Gradio OAuthProfile provides it
+
+if not all([SPACE_ID, HF_TOKEN]):
+    raise RuntimeError(
+        "Please set the following environment variables in your Space settings:\n"
+        "  • SPACE_ID\n"
+        "  • HUGGINGFACEHUB_API_TOKEN"
+    )
+
+WELCOME_TEXT = """
+## Welcome to the GAIA Benchmark Runner 🎉
+
+This challenge is your final hands-on project:
+- Build an agent and evaluate it on a subset of the GAIA benchmark.
+- You need **≥30%** to earn your Certificate of Completion. 🏅
+- Submit your score and see how you stack up on the Student Leaderboard!
+"""
+
+# --- Agent Definition ---
+class GAIAAgent:
+    def __init__(self, model_id="meta-llama/Llama-3-70B-Instruct"):
+        # Initialize HF Inference client
+        self.model = InferenceClientModel(
+            model_id=model_id,
+            token=HF_TOKEN,
+            provider="hf-inference",
+            timeout=120,
+            temperature=0.2
+        )
+        # Attach search + code execution tools
+        tools = [
+            DuckDuckGoSearchTool(),
+            PythonInterpreterTool()
+        ]
+        self.agent = CodeAgent(
+            tools=tools,
+            model=self.model,
+            executor_type="local"
+        )
+
+    def answer(self, question: str, task_file: str = None) -> str:
+        prompt = question
+        if task_file:
+            try:
+                with open(task_file, "r") as f:
+                    content = f.read()
+                prompt += f"\n\nAttached file:\n```\n{content}\n```"
+            except:
+                pass
+        return self.agent.run(prompt)
+
+# --- Runner & Submission ---
+def run_and_submit_all(profile: gr.OAuthProfile | None):
+    if profile is None:
+        return "⚠️ Please log in with your Hugging Face account.", pd.DataFrame()
 
-def run_and_submit(profile):
-    if not profile:
-        return "Please log in to Hugging Face.", None
     username = profile.username
 
-    # Fetch questions
-    resp = requests.get(f"{API_URL}/questions", timeout=15)
-    resp.raise_for_status()
-    questions = resp.json() or []
-
-    # Run agent
-    basic = BasicAgent(GOOGLE_API_KEY)
-    results, answers = [], []
-    for q in questions:
-        ans = basic(q.get("question", ""))
-        results.append({"Task ID": q.get("task_id"), "Question": q.get("question"), "Answer": ans})
-        answers.append({"task_id": q.get("task_id"), "submitted_answer": ans})
-
-    # Submit answers
+    # 1) Fetch GAIA questions
+    q_resp = requests.get(f"{API_URL}/questions", timeout=15)
+    q_resp.raise_for_status()
+    questions = q_resp.json() or []
+    if not questions:
+        return "❌ No questions returned; check your API_URL.", pd.DataFrame()
+
+    # 2) Initialize your agent
+    agent = GAIAAgent()
+
+    # 3) Run agent on each question
+    results, payload = [], []
+    for item in questions:
+        task_id   = item.get("task_id")
+        question  = item.get("question", "")
+        file_path = item.get("task_file_path")  # optional
+
+        try:
+            answer = agent.answer(question, file_path)
+        except Exception as e:
+            answer = f"ERROR: {e}"
+
+        results.append({
+            "Task ID": task_id,
+            "Question": question,
+            "Answer": answer
+        })
+        payload.append({
+            "task_id": task_id,
+            "submitted_answer": answer
+        })
+
+        time.sleep(0.5)  # throttle requests
+
+    # 4) Submit all answers
     submission = {
-        "username": username,
+        "username":   username,
         "agent_code": f"https://huggingface.co/spaces/{SPACE_ID}/tree/main",
-        "answers": answers
+        "answers":    payload
     }
-    resp2 = requests.post(f"{API_URL}/submit", json=submission, timeout=60)
-    resp2.raise_for_status()
-    data = resp2.json()
-    status = f"Score: {data.get('score')}% ({data.get('correct_count')}/{data.get('total_attempted')})"
+    s_resp = requests.post(f"{API_URL}/submit", json=submission, timeout=60)
+    s_resp.raise_for_status()
+    data = s_resp.json()
+
+    # 5) Build status message
+    status = (
+        f"✅ **Submission Successful!**\n\n"
+        f"**User:** {data.get('username')}\n"
+        f"**Score:** {data.get('score')}% "
+        f"({data.get('correct_count')}/{data.get('total_attempted')} correct)\n"
+        f"**Message:** {data.get('message')}"
+    )
 
     return status, pd.DataFrame(results)
 
-# Gradio UI
+
+# --- Gradio Interface ---
 with gr.Blocks() as demo:
-    gr.Markdown("# Basic Agent Evaluation Runner")
-    gr.LoginButton()
-    run_btn = gr.Button("Run Evaluation & Submit All Answers")
-    status_out = gr.Textbox(label="Run Status", lines=5)
-    table_out = gr.DataFrame(label="Results")
-    run_btn.click(run_and_submit, outputs=[status_out, table_out])
+    gr.Markdown(WELCOME_TEXT)
+    login = gr.LoginButton()
+    run_btn = gr.Button("▶️ Run Benchmark & Submit")
+    status_out = gr.Markdown()
+    table_out  = gr.Dataframe(headers=["Task ID","Question","Answer"], wrap=True)
+
+    run_btn.click(
+        fn=run_and_submit_all,
+        inputs=[login],
+        outputs=[status_out, table_out]
+    )
 
 if __name__ == "__main__":
-    demo.launch(debug=True)
-
-
-
+    demo.launch()