Update app.py

app.py

@@ -2,128 +2,96 @@ import os
 import gradio as gr
 import requests
 import pandas as pd
-
-from smolagents.models import InferenceClientModel
-from smolagents.agents import ToolCallingAgent
-from smolagents.tools import DuckDuckGoSearchTool, WebSearchTool, WikipediaSearchTool
+from huggingface_hub import InferenceClient
+from duckduckgo_search import DDGS
+from datasets import load_dataset
 
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-def create_agent():
-    model = InferenceClientModel(repo_id="HuggingFaceH4/zephyr-7b-beta")
-    wiki_tool = WikipediaSearchTool()
-    duck_tool = DuckDuckGoSearchTool()
-    web_tool = WebSearchTool()
-    agent = ToolCallingAgent(
-        tools=[wiki_tool, duck_tool, web_tool],
-        model=model
-    )
-    return agent
+# Hugging Face Token (set in environment)
+HF_TOKEN = os.environ.get("HF_TOKEN")
+deepseek_model = "deepseek-ai/DeepSeek-R1"
+hf_client = InferenceClient(model=deepseek_model, token=HF_TOKEN)
 
-def run_and_submit_all(profile: gr.OAuthProfile | None):
-    space_id = os.getenv("SPACE_ID")
-    if profile:
-        username = f"{profile.username}"
-    else:
-        return "Please Login to Hugging Face with the button.", None
+# Load Wikipedia dataset (small subset for efficient retrieval)
+wiki_dataset = load_dataset("wikipedia", "20220301.en", split="train[:10000]")
+
+def search_wikipedia(question):
+    results = wiki_dataset.filter(lambda x: question.lower() in x["text"].lower())
+    if len(results):
+        return results[0]["text"][:1000]  # limit to first 1000 chars
+    return "No relevant information found on Wikipedia."
 
-    api_url = DEFAULT_API_URL
-    questions_url = f"{api_url}/questions"
-    submit_url = f"{api_url}/submit"
+def duckduckgo_search(query):
+    with DDGS() as ddgs:
+        results = [r["body"] for r in ddgs.text(query, max_results=3)]
+        return "\n".join(results) if results else "No results found."
 
+def ask_deepseek(prompt, max_tokens=512):
     try:
-        agent = create_agent()
+        response = hf_client.text_generation(
+            prompt, max_new_tokens=max_tokens, temperature=0.2, repetition_penalty=1.1
+        )
+        return response
     except Exception as e:
-        return f"Error initializing agent: {e}", None
+        return f"DeepSeek Error: {e}"
 
-    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
+class SmartAgent:
+    def __call__(self, question: str) -> str:
+        q_lower = question.lower()
+        if any(term in q_lower for term in ["current", "latest", "2024", "2025", "recent", "live", "today", "now"]):
+            return duckduckgo_search(question)
+        deepseek_response = ask_deepseek(question)
+        if "DeepSeek Error" not in deepseek_response and deepseek_response.strip():
+            return deepseek_response
+        # fallback to Wikipedia if DeepSeek fails
+        return search_wikipedia(question)
 
+def run_and_submit_all(profile: gr.OAuthProfile | None):
+    if not profile:
+        return "Please Login to Hugging Face with the button.", None
+    username = profile.username
+    questions_url = f"{DEFAULT_API_URL}/questions"
+    submit_url = f"{DEFAULT_API_URL}/submit"
+    agent_code = f"https://huggingface.co/spaces/{os.getenv('SPACE_ID')}/tree/main"
+    
     try:
-        response = requests.get(questions_url, timeout=15)
-        response.raise_for_status()
-        questions_data = response.json()
-        if not questions_data:
-            return "Fetched questions list is empty or invalid format.", None
+        agent = SmartAgent()
     except Exception as e:
-        return f"Error fetching questions: {e}", None
+        return f"Agent Error: {e}", None
+
+    questions_data = requests.get(questions_url).json()
+    results_log, answers_payload = [], []
 
-    results_log = []
-    answers_payload = []
     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
-        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:
-            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"(Agent error: {e})"})
-
-    if not answers_payload:
-        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
-
-    submission_data = {
-        "username": username.strip(),
-        "agent_code": agent_code,
-        "answers": answers_payload
-    }
+        if task_id and question_text:
+            answer = agent(question_text)
+            answers_payload.append({"task_id": task_id, "submitted_answer": answer})
+            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": answer})
 
-    try:
-        response = requests.post(submit_url, json=submission_data, timeout=60)
-        response.raise_for_status()
-        result_data = response.json()
-        final_status = (
-            f"Submission Successful!\n"
-            f"User: {result_data.get('username')}\n"
-            f"Overall Score: {result_data.get('score', 'N/A')}% "
-            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
-            f"Message: {result_data.get('message', 'No message received.')}"
-        )
-        results_df = pd.DataFrame(results_log)
-        return final_status, results_df
-    except Exception as e:
-        status_message = f"Submission Failed: {e}"
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
+    submission_data = {"username": username, "agent_code": agent_code, "answers": answers_payload}
+    response = requests.post(submit_url, json=submission_data).json()
 
-with gr.Blocks() as demo:
-    gr.Markdown("# SmolAgent Evaluation Runner")
-    gr.Markdown(
-        """
-        **Instructions:**
-        - Clone and modify this space to improve your agent logic as you see fit.
-        - Log in to your Hugging Face account with the button below.
-        - Click 'Run Evaluation & Submit All Answers' to begin.
-        Disclaimer: Submission may take a while depending on the number of questions and agent speed.
-        """
+    final_status = (
+        f"Submission Successful!\n"
+        f"User: {response.get('username')}\n"
+        f"Overall Score: {response.get('score', 'N/A')}%\n"
+        f"({response.get('correct_count', '?')}/{response.get('total_attempted', '?')} correct)\n"
+        f"Message: {response.get('message', 'No message received.')}"
     )
+
+    return final_status, pd.DataFrame(results_log)
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Smart Agent Evaluation Runner")
     gr.LoginButton()
     run_button = gr.Button("Run Evaluation & Submit All Answers")
-    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
-    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
+    status_output = gr.Textbox(label="Run Status", lines=5, interactive=False)
+    results_table = gr.DataFrame(label="Questions and Answers")
 
-    run_button.click(
-        fn=run_and_submit_all,
-        outputs=[status_output, results_table]
-    )
+    run_button.click(run_and_submit_all, outputs=[status_output, results_table])
 
 if __name__ == "__main__":
-    print("\n" + "-"*30 + " App Starting " + "-"*30)
-    space_host_startup = os.getenv("SPACE_HOST")
-    space_id_startup = os.getenv("SPACE_ID")
-    if space_host_startup:
-        print(f"✅ SPACE_HOST found: {space_host_startup}")
-        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
-    else:
-        print("ℹ️  SPACE_HOST not found (running locally?)")
-    if space_id_startup:
-        print(f"✅ SPACE_ID found: {space_id_startup}")
-        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
-        print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
-    else:
-        print("ℹ️  SPACE_ID not found")
-    print("-"*(60 + len(" App Starting ")) + "\n")
-    print("Launching Gradio Interface for SmolAgent Evaluation...")
-    demo.launch(debug=True, share=False)
+    demo.launch(debug=True)