Update app.py

app.py

@@ -1,46 +1,31 @@
 import os
 import logging
-import openai
-from openai import OpenAI
+
 import gradio as gr
 import requests
 import pandas as pd
+import openai
+from openai import OpenAI
+
 from smolagents import CodeAgent, DuckDuckGoSearchTool, tool
+from smolagents.models import OpenAIServerModel
 
-# --- Setup logging ---
+# --- Logging ---
 logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
 logger = logging.getLogger(__name__)
 
 # --- Constants ---
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-MAX_PROMPT_LENGTH = 15000  # characters, naive cap to avoid token overflow
-
-# --- Configure OpenAI SDK & Client ---
-openai_api_key = os.getenv("OPENAI_API_KEY")
-if not openai_api_key:
-    raise RuntimeError("Environment variable OPENAI_API_KEY is required.")
-openai.api_key = openai_api_key
+DEFAULT_API_URL   = "https://agents-course-unit4-scoring.hf.space"
+OPENAI_MODEL_ID   = os.getenv("OPENAI_MODEL_ID", "gpt-4.1")
+OPENAI_API_KEY    = os.getenv("OPENAI_API_KEY")
+if not OPENAI_API_KEY:
+    raise RuntimeError("Please set OPENAI_API_KEY in your Space secrets.")
+
+# --- Configure OpenAI SDK (for tools if needed) ---
+openai.api_key = OPENAI_API_KEY
 client = OpenAI()
-OPENAI_MODEL_ID = os.getenv("OPENAI_MODEL_ID", "gpt-4.1")
-
-# --- Adapter so CodeAgent can call the OpenAI client correctly ---
-class OpenAIModelWrapper:
-    def __init__(self, model_id: str, client: OpenAI):
-        self.model_id = model_id
-        self.client = client
 
-    def __call__(self, prompt: str, **kwargs) -> str:
-        try:
-            resp = self.client.responses.create(
-                model=self.model_id,
-                input=prompt
-            )
-            return getattr(resp, "output_text", str(resp))
-        except Exception as e:
-            logger.exception("OpenAI inference error")
-            return f"AGENT ERROR (inference): {e}"
-
-# --- Tool Definitions ---
+# --- Tools ---
 
 @tool
 def summarize_query(query: str) -> str:
@@ -48,37 +33,33 @@ def summarize_query(query: str) -> str:
     Reframes an unclear search query to improve relevance.
 
     Args:
-        query (str): The original search query needing refinement.
+        query (str): The original search query.
 
     Returns:
-        str: A concise, improved version of the query.
+        str: A concise, improved version.
     """
     return f"Summarize and reframe: {query}"
 
 @tool
 def wikipedia_search(page: str) -> str:
     """
-    Fetches the summary extract of an English Wikipedia page via the REST API.
+    Fetches the summary extract of an English Wikipedia page.
 
     Args:
-        page (str): Title of the Wikipedia page (e.g. 'Mercedes_Sosa_discography').
+        page (str): e.g. 'Mercedes_Sosa_discography'
 
     Returns:
-        str: The page's summary (or an error message).
+        str: The page’s extract text.
     """
     try:
         url = f"https://en.wikipedia.org/api/rest_v1/page/summary/{page}"
-        resp = requests.get(url, timeout=10)
-        resp.raise_for_status()
-        extract = resp.json().get("extract", "")
-        if not extract:
-            logger.warning("Wikipedia returned empty extract for %s", page)
-        return extract
+        r = requests.get(url, timeout=10)
+        r.raise_for_status()
+        return r.json().get("extract", "")
     except Exception as e:
         logger.exception("Wikipedia lookup failed")
         return f"Wikipedia error: {e}"
 
-# Instantiate tools
 search_tool    = DuckDuckGoSearchTool()
 wiki_tool      = wikipedia_search
 summarize_tool = summarize_query
@@ -100,113 +81,114 @@ Internally, for each question:
    Record new Observation.
 5. Thought: integrate observations.
 
-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.
+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.
 
 """
 
-# --- Build CodeAgent ---
+# --- Build the Agent with OpenAIServerModel ---
+
+model = OpenAIServerModel(
+    model_id=OPENAI_MODEL_ID,
+    api_key=OPENAI_API_KEY
+)
 
-llm_wrapper = OpenAIModelWrapper(model_id=OPENAI_MODEL_ID, client=client)
 smart_agent = CodeAgent(
     tools=[search_tool, wiki_tool, summarize_tool],
-    model=llm_wrapper
+    model=model
 )
 
-# --- BasicAgent for Gradio ---
+# --- Gradio Wrapper ---
 
 class BasicAgent:
     def __init__(self):
-        logger.info("Initialized SmolAgent (GPT-4.1) with ReACT & tools.")
+        logger.info("Initialized SmolAgent with OpenAI GPT-4.1")
 
     def __call__(self, question: str) -> str:
-        # Validate question
-        if not question or len(question.strip()) == 0:
-            return "AGENT ERROR: Empty question."
-        # Build and truncate prompt
+        if not question.strip():
+            return "AGENT ERROR: empty question"
         prompt = instruction_prompt.strip() + "\n\nQUESTION: " + question.strip()
-        if len(prompt) > MAX_PROMPT_LENGTH:
-            prompt = prompt[:MAX_PROMPT_LENGTH]  # naive trim
-            logger.warning("Prompt truncated to %d chars", MAX_PROMPT_LENGTH)
         try:
             return smart_agent.run(prompt)
         except Exception as e:
             logger.exception("Agent run error")
-            return f"AGENT ERROR (run): {e}"
+            return f"AGENT ERROR: {e}"
 
-# --- Submission logic ---
+# --- Submission Logic ---
 
 def run_and_submit_all(profile: gr.OAuthProfile | None):
     if not profile:
         return "Please log in to Hugging Face.", None
 
-    username = profile.username
-    space_id = os.getenv("SPACE_ID", "")
+    username   = profile.username
+    space_id   = os.getenv("SPACE_ID", "")
     agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
-    agent = BasicAgent()
+    agent      = BasicAgent()
 
-    # Fetch questions
+    # fetch
     try:
         resp = requests.get(f"{DEFAULT_API_URL}/questions", timeout=15)
         resp.raise_for_status()
-        questions = resp.json()
-        if not isinstance(questions, list):
-            raise ValueError("Invalid questions format")
-        logger.info("Fetched %d questions", len(questions))
+        questions = resp.json() or []
     except Exception as e:
-        logger.exception("Failed to fetch questions")
+        logger.exception("Failed fetch")
         return f"Error fetching questions: {e}", None
 
-    # Run agent
-    logs, payload, skipped = [], [], 0
+    logs, payload = [], []
     for item in questions:
         tid = item.get("task_id")
         q   = item.get("question")
         if not tid or not q:
-            skipped += 1
             continue
         ans = agent(q)
         logs.append({"Task ID": tid, "Question": q, "Submitted Answer": ans})
         payload.append({"task_id": tid, "submitted_answer": ans})
-    if skipped:
-        logger.warning("Skipped %d malformed items", skipped)
 
     if not payload:
         return "Agent did not produce any answers.", pd.DataFrame(logs)
 
-    # Submit answers
-    submission = {"username": username, "agent_code": agent_code, "answers": payload}
+    # submit
     try:
-        post = requests.post(f"{DEFAULT_API_URL}/submit", json=submission, timeout=60)
+        post = requests.post(
+            f"{DEFAULT_API_URL}/submit",
+            json={"username": username, "agent_code": agent_code, "answers": payload},
+            timeout=60
+        )
         post.raise_for_status()
-        res = post.json()
+        result = post.json()
         status = (
             f"Submission Successful!\n"
-            f"User: {res.get('username')}\n"
-            f"Score: {res.get('score','N/A')}% "
-            f"({res.get('correct_count','?')}/{res.get('total_attempted','?')})\n"
-            f"Message: {res.get('message','')}"
+            f"User: {result.get('username')}\n"
+            f"Score: {result.get('score','N/A')}%\n"
+            f"({result.get('correct_count','?')}/"
+            f"{result.get('total_attempted','?')})\n"
+            f"Message: {result.get('message','')}"
         )
         return status, pd.DataFrame(logs)
     except Exception as e:
-        logger.exception("Submission failed")
+        logger.exception("Submit failed")
         return f"Submission Failed: {e}", pd.DataFrame(logs)
 
-# --- Gradio UI ---
+# --- Gradio App ---
 
 with gr.Blocks() as demo:
     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.
+**Instructions:**  
+1. Clone this space.  
+2. In Settings → Secrets, add `OPENAI_API_KEY` and (optionally) `OPENAI_MODEL_ID`.  
+3. Log in to Hugging Face.  
 4. Click **Run Evaluation & Submit All Answers**.
 """)
     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)
-    run_btn.click(fn=run_and_submit_all, outputs=[status_out, table_out])
+    btn = gr.Button("Run Evaluation & Submit All Answers")
+    out_status = gr.Textbox(label="Status", lines=5, interactive=False)
+    out_table  = gr.DataFrame(label="Questions & Answers", wrap=True)
+    btn.click(run_and_submit_all, outputs=[out_status, out_table])
 
 if __name__ == "__main__":
     demo.launch(debug=True, share=False)