Update app.py

app.py

@@ -2,98 +2,85 @@ import os
 import requests
 import gradio as gr
 import pandas as pd
-from langchain_core.messages import HumanMessage
-
-from smolagents import GradioUI, CodeAgent, HfApiModel
+from smolagents import SmolAgent
+from smolagents.models import InferenceClientModel
 from smolagents.tools import Tool
-from langchain_community.tools import DuckDuckGoSearchRun
-
+from duckduckgo_search import DDGS
 
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-
-# --- Custom Tool using DuckDuckGo ---
+# --- Custom Search Tool ---
 class CustomSearchTool(Tool):
     name = "duckduckgo_search"
-    description = "Search the web for up-to-date information using DuckDuckGo."
-    inputs = ["query"]
-    outputs = ["result"]
-
-    def __init__(self):
-        self.search = DuckDuckGoSearchRun()
-        super().__init__()
+    description = "A simple tool that performs web search using DuckDuckGo."
+    inputs = {"query": str}
+    outputs = str
 
     def run(self, query: str) -> str:
-        return self.search.run(query)
-
-
-# --- Initialize the model and tools ---
-model = HfApiModel()
-search_tool = CustomSearchTool()
-
-agent = CodeAgent(
-    tools=[search_tool],
-    model=model,
-    add_base_tools=True,
-    planning_interval=3,
-)
-
+        print(f"[SearchTool] Searching DuckDuckGo for: {query}")
+        try:
+            with DDGS() as ddgs:
+                results = ddgs.text(query, max_results=3)
+                output = "\n".join([r["body"] for r in results if "body" in r])
+                return output.strip() or "No results found."
+        except Exception as e:
+            print(f"[SearchTool] Search failed: {e}")
+            return f"Search error: {e}"
 
-# --- Evaluation Agent Wrapper ---
-class BasicAgent:
+# --- BasicAgent Class ---
+class BasicAgent(SmolAgent):
     def __init__(self):
-        self.graph = agent
-        print("✅ BasicAgent initialized with SmolAgent.")
+        model = InferenceClientModel("mistralai/Mixtral-8x7B-Instruct-v0.1")
+        tools = [CustomSearchTool()]
+        super().__init__(model=model, tools=tools)
 
-    def __call__(self, question: str) -> str:
-        print(f"Received question: {question}")
-        messages = [HumanMessage(content=question)]
-        result = self.graph.invoke({"messages": messages})
-        answer = result['messages'][-1].content
-        print(f"Returning answer: {answer}")
-        return answer[14:]  # optional slicing depending on model behavior
-
-
-# --- Evaluation Logic ---
+# --- Submission + Evaluation ---
 def run_and_submit_all(profile: gr.OAuthProfile | None):
     space_id = os.getenv("SPACE_ID")
 
     if profile:
-        username = profile.username
-        print(f"🔐 User logged in: {username}")
+        username = f"{profile.username}"
+        print(f"User logged in: {username}")
     else:
-        return "⚠️ Please log in to Hugging Face.", None
+        print("User not logged in.")
+        return "Please Login to Hugging Face with the button.", None
 
     api_url = DEFAULT_API_URL
     questions_url = f"{api_url}/questions"
     submit_url = f"{api_url}/submit"
-    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
 
     try:
-        agent_instance = BasicAgent()
+        agent = BasicAgent()
     except Exception as e:
-        return f"Agent initialization error: {e}", None
+        print(f"Error instantiating agent: {e}")
+        return f"Error initializing agent: {e}", None
 
-    # 1. Fetch questions
+    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
+    print(f"Agent code: {agent_code}")
+
+    # Fetch questions
+    print(f"Fetching questions from: {questions_url}")
     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
     except Exception as e:
         return f"Error fetching questions: {e}", None
 
-    # 2. Run Agent
     results_log = []
     answers_payload = []
 
+    print(f"Running agent on {len(questions_data)} questions...")
     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_instance(question_text)
+            submitted_answer = agent(question_text)
             answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
             results_log.append({
                 "Task ID": task_id,
@@ -108,55 +95,64 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
             })
 
     if not answers_payload:
-        return "Agent did not produce answers.", pd.DataFrame(results_log)
+        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
 
-    # 3. Submit answers
+    # Submit
     submission_data = {
         "username": username.strip(),
         "agent_code": agent_code,
         "answers": answers_payload
     }
-
     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"Submission Successful!\n"
             f"User: {result_data.get('username')}\n"
-            f"Score: {result_data.get('score', 'N/A')}%\n"
-            f"Correct: {result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')}\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.')}"
         )
         return final_status, pd.DataFrame(results_log)
     except Exception as e:
         return f"Submission Failed: {e}", pd.DataFrame(results_log)
 
-
-# --- Gradio Interface ---
+# --- Gradio UI ---
 with gr.Blocks() as demo:
-    gr.Markdown("## 🧠 SmolAgent Evaluation App")
+    gr.Markdown("# Basic Agent Evaluation Runner")
     gr.Markdown(
-        "Log in with Hugging Face and click the button to fetch questions, run the agent, and submit answers."
+        """
+        **Instructions:**
+        1. Clone this space, then modify your agent logic, tools, and setup.
+        2. Log in to your Hugging Face account below.
+        3. Click "Run Evaluation & Submit All Answers" to start.
+        ---
+        **Note:** This is intentionally basic. Improve and optimize your agent for better scores.
+        """
     )
 
     gr.LoginButton()
-    run_button = gr.Button("▶️ Run Evaluation & Submit All Answers")
-    status_output = gr.Textbox(label="Submission Status", lines=5, interactive=False)
-    results_table = gr.DataFrame(label="Agent Answers")
 
-    run_button.click(
-        fn=run_and_submit_all,
-        outputs=[status_output, results_table]
-    )
+    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)
 
+    run_button.click(fn=run_and_submit_all, outputs=[status_output, results_table])
 
-# --- App Launch ---
+# --- Launch ---
 if __name__ == "__main__":
-    print("🚀 Launching app...")
+    print("\n" + "-" * 30 + " App Starting " + "-" * 30)
+    space_host = os.getenv("SPACE_HOST")
     space_id = os.getenv("SPACE_ID")
+
+    if space_host:
+        print(f"✅ SPACE_HOST found: {space_host}")
+        print(f"   Runtime URL: https://{space_host}.hf.space")
     if space_id:
-        print(f"HF Space ID: {space_id}")
-        print(f"Repo: https://huggingface.co/spaces/{space_id}/tree/main")
-    demo.launch(debug=True)
+        print(f"✅ SPACE_ID found: {space_id}")
+        print(f"   Repo URL: https://huggingface.co/spaces/{space_id}")
+    print("-" * 70 + "\n")
+
+    print("Launching Gradio Interface...")
+    demo.launch(debug=True, share=False)