Update app.py

app.py

@@ -1,46 +1,17 @@
-import os
-import re
-import gradio as gr
-import requests
-import pandas as pd
-
-from smolagents import CodeAgent, DuckDuckGoSearchTool
-from smolagents.models import OpenAIServerModel
-
-# ---------------------------------------------------
-# Constants
-# ---------------------------------------------------
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-
-# ---------------------------------------------------
-# Agent
-# ---------------------------------------------------
 class BasicAgent:
 
     def __init__(self):
 
-        print("Initializing Groq Agent...")
+        print("Initializing Fast Groq Agent...")
 
         self.model = OpenAIServerModel(
-            model_id="llama-3.3-70b-versatile",
+            model_id="llama-3.1-8b-instant",
             api_base="https://api.groq.com/openai/v1",
             api_key=os.getenv("GROQ_API_KEY")
         )
 
-        self.agent = CodeAgent(
-            tools=[
-                DuckDuckGoSearchTool()
-            ],
-            model=self.model,
-            max_steps=3,
-            verbosity_level=0
-        )
-
-        print("Groq Agent initialized.")
+        print("Fast agent ready.")
 
-    # ---------------------------------------------------
-    # Clean outputs for exact-match grading
-    # ---------------------------------------------------
     def clean_answer(self, text):
 
         if text is None:
@@ -48,38 +19,24 @@ class BasicAgent:
 
         text = str(text)
 
-        # remove markdown/code
         text = text.replace("```", "")
         text = text.replace("FINAL ANSWER:", "")
         text = text.replace("Answer:", "")
 
-        # remove excessive whitespace
-        text = re.sub(r"\s+", " ", text)
+        text = text.strip().split("\n")[0]
 
-        # first line only
-        text = text.split("\n")[0]
+        return text[:200]
 
-        # concise
-        text = text.strip()
-
-        return text[:300]
-
-    # ---------------------------------------------------
-    # Run agent
-    # ---------------------------------------------------
     def __call__(self, question: str) -> str:
 
         prompt = f"""
-You are solving a GAIA benchmark question.
+Answer the question.
 
 IMPORTANT:
 - Return ONLY the final answer.
 - No reasoning.
-- No explanations.
-- No markdown.
-- No bullet points.
-- Be concise and accurate.
-- Use web search if needed.
+- No explanation.
+- Keep answers concise.
 
 Question:
 {question}
@@ -87,220 +44,19 @@ Question:
 
         try:
 
-            result = self.agent.run(prompt)
+            response = self.model(
+                prompt,
+                max_tokens=80
+            )
 
-            cleaned = self.clean_answer(result)
+            cleaned = self.clean_answer(response)
 
-            print(f"\nQUESTION:\n{question}")
-            print(f"\nANSWER:\n{cleaned}")
+            print(cleaned)
 
             return cleaned
 
         except Exception as e:
 
-            print(f"Agent error: {e}")
-
-            return ""
-
-
-# ---------------------------------------------------
-# Evaluation + Submission
-# ---------------------------------------------------
-def run_and_submit_all(profile: gr.OAuthProfile | None):
-
-    space_id = os.getenv("SPACE_ID")
-
-    # Login check
-    if profile:
-        username = profile.username
-        print(f"Logged in as: {username}")
-    else:
-        return "Please login with Hugging Face.", None
-
-    api_url = DEFAULT_API_URL
-    questions_url = f"{api_url}/questions"
-    submit_url = f"{api_url}/submit"
-
-    # ---------------------------------------------------
-    # Initialize Agent
-    # ---------------------------------------------------
-    try:
-
-        agent = BasicAgent()
-
-    except Exception as e:
-
-        print(f"Initialization error: {e}")
-
-        return f"Error initializing agent: {e}", None
-
-    # Space repo link
-    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
-
-    # ---------------------------------------------------
-    # Fetch Questions
-    # ---------------------------------------------------
-    try:
-
-        print("Fetching questions...")
-
-        response = requests.get(
-            questions_url,
-            timeout=30
-        )
-
-        response.raise_for_status()
-
-        questions_data = response.json()
-
-        print(f"Fetched {len(questions_data)} questions.")
-
-    except Exception as e:
-
-        print(f"Question fetch error: {e}")
-
-        return f"Error fetching questions: {e}", None
-
-    # ---------------------------------------------------
-    # Run Agent
-    # ---------------------------------------------------
-    answers_payload = []
-    results_log = []
-
-    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:
-            continue
-
-        print(f"\nRunning task {task_id}")
-
-        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"Task error: {e}")
-
-            results_log.append({
-                "Task ID": task_id,
-                "Question": question_text,
-                "Submitted Answer": f"ERROR: {e}"
-            })
-
-    # ---------------------------------------------------
-    # Submit
-    # ---------------------------------------------------
-    submission_data = {
-        "username": username.strip(),
-        "agent_code": agent_code,
-        "answers": answers_payload
-    }
-
-    try:
-
-        print("Submitting answers...")
-
-        response = requests.post(
-            submit_url,
-            json=submission_data,
-            timeout=120
-        )
-
-        response.raise_for_status()
-
-        result_data = response.json()
-
-        final_status = (
-            f"Submission Successful!\n\n"
-            f"User: {result_data.get('username')}\n"
-            f"Score: {result_data.get('score', 'N/A')}%\n"
-            f"Correct: "
-            f"{result_data.get('correct_count', '?')}/"
-            f"{result_data.get('total_attempted', '?')}\n\n"
-            f"Message: {result_data.get('message', '')}"
-        )
-
-        print(final_status)
-
-        return final_status, pd.DataFrame(results_log)
-
-    except Exception as e:
-
-        error_message = f"Submission failed: {e}"
-
-        print(error_message)
-
-        return error_message, pd.DataFrame(results_log)
-
-
-# ---------------------------------------------------
-# UI
-# ---------------------------------------------------
-with gr.Blocks() as demo:
-
-    gr.Markdown("# Hugging Face Agents Course - Final Assignment")
-
-    gr.Markdown(
-        """
-This agent uses:
-
-- Groq API
-- Llama 3.3 70B
-- smolagents
-- DuckDuckGo Search
-"""
-    )
-
-    gr.LoginButton()
-
-    run_button = gr.Button(
-        "Run Evaluation & Submit"
-    )
-
-    status_output = gr.Textbox(
-        label="Submission Result",
-        lines=8,
-        interactive=False
-    )
-
-    results_table = gr.DataFrame(
-        label="Agent Answers",
-        wrap=True
-    )
-
-    run_button.click(
-        fn=run_and_submit_all,
-        outputs=[
-            status_output,
-            results_table
-        ]
-    )
-
-# ---------------------------------------------------
-# Launch
-# ---------------------------------------------------
-if __name__ == "__main__":
-
-    print("\n==============================")
-    print("Starting Groq Agent...")
-    print("==============================\n")
+            print(e)
 
-    demo.launch(
-        debug=True,
-        share=False
-    )
+            return ""