Update app.py

app.py

@@ -1,89 +1,51 @@
 import os
-import gradio as gr
+import openai  # 🍕 https://www.google.com/search?q=openai.ChatCompletion.create
+import gradio as gr  # 🍣 https://www.google.com/search?q=Gradio+python
 import requests
 import pandas as pd
 
-from smolagents import CodeAgent, HfApiModel, DuckDuckGoSearchTool, tool
-
-# --- Constants ---
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-
-# --- Tool Definitions ---
-
-@tool
-def summarize_query(query: str) -> str:
-    """
-    Provides a structured summary to reframe a query if search results are unclear or poor.
-
-    Args:
-        query (str): The search query that needs summarization.
-
-    Returns:
-        str: A concise summary of key facts about the given query.
-    """
-    return f"Summarize and reframe: {query}"
-
-search_tool = DuckDuckGoSearchTool()
-
-# --- ReACT + Scratchpad + Auto-Retry Instructions ---
-
-instruction_prompt = """
-You are a ReACT agent with scratchpad memory and a retry mechanism.
-
-For every question:
-1. Thought: Figure out what's needed.
-2. Action: (Optional) Call a tool with a precise query.
-3. Observation: Record tool output.
-
-If the first Observation is empty or irrelevant:
-4. Thought: Unclear result; reframe and retry.
-5. Action: summarize_query(original question).
-6. Action: DuckDuckGoSearchTool(reframed query).
-7. Observation: Record new result.
-
-Then:
-8. Thought: Reflect on all observations.
-9. FINAL ANSWER: Provide your answer.
-
-Formatting rules:
-- Begin with `FINAL ANSWER: [your answer]`
-- Numbers: plain (no commas unless in a list)
-- Strings: no articles unless part of proper names
-- Lists: comma-separated without extra punctuation
-"""
-
-# --- Build the Smart Agent ---
-
-smart_agent = CodeAgent(
-    tools=[search_tool, summarize_query],
-    model=HfApiModel()
-)
-
-# --- Integrate into Gradio App ---
+# --- Configuration ---
+openai.api_key = os.getenv("OPENAI_API_KEY")  # 🍱 https://www.google.com/search?q=python+environment+variables
+API_URL = "https://agents-course-unit4-scoring.hf.space"
+MODEL_NAME = "gpt-4.1"  # 🍜 https://www.google.com/search?q=gpt-4.1+openai+model+id
+
+# --- ChatGPT-4.1 Caller ---
+def ask_chatgpt_4_1(question: str) -> str:
+    response = openai.ChatCompletion.create(
+        model=MODEL_NAME,
+        messages=[
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user",   "content": question}
+        ],
+        temperature=0.7,
+        max_tokens=1500
+    )
+    return response.choices[0].message.content
 
+# --- Agent Class ---
 class BasicAgent:
     def __init__(self):
-        print("SmolAgent with ReACT, Scratchpad & Retry initialized.")
+        print("BasicAgent using OpenAI GPT-4.1 ready.")
 
     def __call__(self, question: str) -> str:
-        full_input = instruction_prompt.strip() + "\n\nQUESTION: " + question.strip()
-        print(f"Agent input (first 100 chars): {full_input[:100]}...")
+        print(f"Q>> {question}")
         try:
-            return smart_agent.run(full_input)
+            return ask_chatgpt_4_1(question)
         except Exception as e:
             return f"AGENT ERROR: {e}"
 
+# --- Evaluation & Submission ---
 def run_and_submit_all(profile: gr.OAuthProfile | None):
     if not profile:
-        return "Please log in to Hugging Face using the login button above.", None
+        return "Please log in to Hugging Face.", None
     username = profile.username
     space_id = os.getenv("SPACE_ID", "")
     agent = BasicAgent()
-    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
+    code_link = f"https://huggingface.co/spaces/{space_id}/tree/main"
 
     # 1. Fetch questions
     try:
-        resp = requests.get(f"{DEFAULT_API_URL}/questions", timeout=15)
+        resp = requests.get(f"{API_URL}/questions", timeout=15)
         resp.raise_for_status()
         questions = resp.json()
         if not questions:
@@ -91,7 +53,7 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
     except Exception as e:
         return f"Error fetching questions: {e}", None
 
-    # 2. Run agent on each question
+    # 2. Answer each
     logs = []
     payload = []
     for item in questions:
@@ -100,46 +62,46 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
         if not tid or q is None:
             continue
         ans = agent(q)
-        logs.append({"Task ID": tid, "Question": q, "Submitted Answer": ans})
+        logs.append({"Task ID": tid, "Question": q, "Answer": ans})
         payload.append({"task_id": tid, "submitted_answer": ans})
 
     if not payload:
-        return "Agent did not produce any answers.", pd.DataFrame(logs)
-
-    # 3. Submit answers
-    submission = {"username": username, "agent_code": agent_code, "answers": payload}
+        return "No answers generated.", pd.DataFrame(logs)
+
+    # 3. Submit
+    submission = {
+        "username": username,
+        "agent_code": code_link,
+        "answers": payload
+    }
     try:
-        post = requests.post(f"{DEFAULT_API_URL}/submit", json=submission, timeout=60)
+        post = requests.post(f"{API_URL}/submit", json=submission, timeout=60)
         post.raise_for_status()
         res = post.json()
         status = (
-            f"Submission Successful!\n"
-            f"User: {res.get('username')}\n"
-            f"Overall Score: {res.get('score', 'N/A')}% "
-            f"({res.get('correct_count', '?')}/{res.get('total_attempted', '?')})\n"
-            f"Message: {res.get('message', '')}"
+            f"Success! {res.get('username')} scored "
+            f"{res.get('score', 'N/A')}% "
+            f"({res.get('correct_count')}/{res.get('total_attempted')})"
         )
         return status, pd.DataFrame(logs)
     except Exception as e:
         return f"Submission Failed: {e}", pd.DataFrame(logs)
 
 # --- Gradio Interface ---
-
 with gr.Blocks() as demo:
     gr.Markdown("# SmolAgent GAIA Evaluation Runner 🚀")
     gr.Markdown(
         """
-        **Instructions:**
-        1. Clone this space and modify if needed.
-        2. Log in to Hugging Face.
-        3. Click **Run Evaluation & Submit All Answers** to evaluate your agent.
-        **Note:** Evaluation may take several minutes.
+        1. Clone space and modify if needed  
+        2. Log in to Hugging Face  
+        3. Click **Run Evaluation & Submit All Answers**  
+        (May take several minutes)
         """
     )
     gr.LoginButton()
     run_btn = gr.Button("Run Evaluation & Submit All Answers")
     status_out = gr.Textbox(label="Status", lines=5, interactive=False)
-    table_out = gr.DataFrame(label="Questions & Answers", wrap=True)
+    table_out = gr.DataFrame(label="Q&A Log", wrap=True)
 
     run_btn.click(fn=run_and_submit_all, outputs=[status_out, table_out])