Update app.py

app.py

@@ -1,86 +1,111 @@
 import os
-import openai                              # ← official OpenAI client
-from openai import OpenAI
+import openai
 import gradio as gr
 import requests
 import pandas as pd
-from smolagents import DuckDuckGoSearchTool, tool
+from smolagents import CodeAgent, LiteLLMModel, DuckDuckGoSearchTool, tool
 
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-# --- Configure OpenAI SDK & Client ---
-
+# --- Configure OpenAI SDK ---
 openai_api_key = os.getenv("OPENAI_API_KEY")
 if not openai_api_key:
-    raise RuntimeError("Set OPENAI_API_KEY in your Space secrets or env!")
-
+    raise RuntimeError("Please set OPENAI_API_KEY in your Space secrets.")
 openai.api_key = openai_api_key
-client = OpenAI()  # new client object
+openai_model_id = os.getenv("OPENAI_MODEL_ID", "gpt-4.1")
 
 # --- Tool Definitions ---
 
 @tool
 def summarize_query(query: str) -> str:
     """
-    Provides a structured summary to reframe a query if search results are unclear or poor.
+    Reframes an unclear query into a better one.
 
     Args:
-        query (str): The search query that needs summarization.
+        query (str): The search query to refine.
     Returns:
-        str: A concise summary of key facts about the given query.
+        str: A concise, improved query.
     """
     return f"Summarize and reframe: {query}"
 
-search_tool = DuckDuckGoSearchTool()
+@tool
+def wikipedia_search(page: str) -> str:
+    """
+    Fetches the summary extract of an English Wikipedia page.
+
+    Args:
+        page (str): The page title (e.g. 'Mercedes_Sosa_discography').
+    Returns:
+        str: The extract section text.
+    """
+    url = f"https://en.wikipedia.org/api/rest_v1/page/summary/{page}"
+    resp = requests.get(url, timeout=10)
+    resp.raise_for_status()
+    return resp.json().get("extract", "")
+
+search_tool    = DuckDuckGoSearchTool()
+wiki_tool      = wikipedia_search
+summarize_tool = summarize_query
 
-# --- ReACT + Scratchpad + Auto‐Retry Instruction Prompt ---
+# --- ReACT + Scratchpad + Auto-Retry Prompt ---
 
 instruction_prompt = """
+You are a ReACT agent with three tools: 
+ • DuckDuckGoSearchTool(query: str)
+ • wikipedia_search(page: str)
+ • summarize_query(query: str)
 
-You are a high-precision AI agent. Internally, you may follow the ReACT pattern—thinking step-by-step, invoking tools, observing results, retrying if needed—but you must NOT show any of that. Instead, after you finish reasoning privately, output **exactly** one line:
+Internally, for each question:
+1. Thought: decide which tool to call.
+2. Action: call the chosen tool.
+3. Observation: record the result.
+4. If empty/irrelevant:
+   Thought: retry with summarize_query + DuckDuckGoSearchTool.
+   Record new Observation.
+5. Thought: integrate observations.
 
-FINAL ANSWER: [your concise answer]
+Finally, output your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER]. YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string.
 
-Rules for the final answer:
-- If it’s a number, output only the digits (no commas, units, or extra text).
-- If it’s a list, output a comma-separated list with no extra punctuation or articles.
-- If it’s a string, output only the words, no “um,” “the,” or other fillers.
 
 """
 
-# --- BasicAgent using the new OpenAI client ---
+# --- Build the CodeAgent with the same model ---
+
+llm = LiteLLMModel(
+    model_id=openai_model_id,
+    api_key=openai_api_key
+)
+
+smart_agent = CodeAgent(
+    tools=[search_tool, wiki_tool, summarize_tool],
+    model=llm
+)
+
+# --- Wrap in BasicAgent for Gradio ---
 
 class BasicAgent:
     def __init__(self):
-        print("SmolAgent (GPT-4.1) with ReACT, Scratchpad & Retry initialized.")
+        print("SmolAgent (GPT-4.1) with ReACT & tools initialized.")
 
     def __call__(self, question: str) -> str:
-        # Build the full prompt
         prompt = instruction_prompt.strip() + "\n\nQUESTION: " + question.strip()
-        print(f"Agent prompt (first 150 chars): {prompt[:150]}…")
-        # Call GPT-4.1 via the new client.responses.create API
+        print(f"Agent prompt: {prompt[:120]}…")
         try:
-            response = client.responses.create(
-                model="gpt-4.1",
-                input=prompt
-            )
-            return response.output_text
+            return smart_agent.run(prompt)
         except Exception as e:
             return f"AGENT ERROR: {e}"
 
-# --- Gradio / HF‐Spaces submission logic ---
+# --- Gradio & Submission Logic ---
 
 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"
 
-    # 1. Fetch questions
     try:
         resp = requests.get(f"{DEFAULT_API_URL}/questions", timeout=15)
         resp.raise_for_status()
@@ -88,12 +113,11 @@ 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
     logs, payload = [], []
     for item in questions:
         tid = item.get("task_id")
         q   = item.get("question")
-        if not tid or q is None:
+        if not tid or not q:
             continue
         ans = agent(q)
         logs.append({"Task ID": tid, "Question": q, "Submitted Answer": ans})
@@ -102,7 +126,6 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
     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}
     try:
         post = requests.post(f"{DEFAULT_API_URL}/submit", json=submission, timeout=60)
@@ -113,28 +136,23 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
             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"Message: {res.get('message','')}"
         )
         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 Runner (GPT-4.1) 🚀")
-    gr.Markdown(
-        """
-        **Instructions:**
-        1. Clone this space.
-        2. In Settings → Secrets add `OPENAI_API_KEY`.
-        3. Log in to Hugging Face.
-        4. Click **Run Evaluation & Submit All Answers**.
-        **Note:** Evaluation may take several minutes.
-        """
-    )
+    gr.Markdown("# SmolAgent GAIA Runner 🚀")
+    gr.Markdown("""
+**Instructions:**
+1. Clone this space.
+2. Add `OPENAI_API_KEY` (and optionally `OPENAI_MODEL_ID`) in Settings → Secrets.
+3. Log in to Hugging Face.
+4. Click **Run Evaluation & Submit All Answers**.
+""")
     gr.LoginButton()
-    run_btn    = gr.Button("Run Evaluation & Submit All Answers")
+    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)