Update app.py

app.py

@@ -3,77 +3,60 @@ import gradio as gr
 import requests
 import pandas as pd
 from typing import Optional
+from smolagents import CodeAgent, DuckDuckGoSearchTool, OpenAIServerModel
 
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-GROQ_API_URL = "https://api.groq.com/openai/v1/chat/completions"
 
 class GroqAgent:
-    """使用 Groq API 的 LLM Agent"""
+    """使用 smolagents + Groq API + Search Tool 的 Agent"""
     def __init__(self):
         self.api_key = os.getenv("GROQ_API_KEY")
         if not self.api_key:
-            print("✗ GROQ_API_KEY not found in environment variables")
+            print("✗ GROQ_API_KEY not found")
             self.agent = None
             return
+            
+        # 1. 設定模型：使用 OpenAI 相容模式連接 Groq
+        model = OpenAIServerModel(
+            model_id="llama-3.3-70b-versatile",
+            api_base="https://api.groq.com/openai/v1",
+            api_key=self.api_key
+        )
         
-        self.agent = True
-        print("✓ Groq agent initialized successfully")
+        # 2. 建立 Agent：加入搜尋工具 (DuckDuckGoSearchTool)
+        # 這是拿到 > 30% 分數的關鍵，讓它能上網查資料
+        self.agent = CodeAgent(
+            tools=[DuckDuckGoSearchTool()], 
+            model=model,
+            max_steps=4, # 限制思考步數避免超時
+            verbosity_level=1
+        )
+        print("✓ Groq agent (smolagents) initialized successfully")
     
     def __call__(self, question: str) -> str:
         """回答問題"""
         if self.agent is None:
-            return "Error: GROQ_API_KEY not configured. Please add it to Secrets."
+            return "Error: GROQ_API_KEY not configured."
         
         try:
-            headers = {
-                "Authorization": f"Bearer {self.api_key}",
-                "Content-Type": "application/json"
-            }
-            
-            payload = {
-                "model": "llama-3.3-70b-versatile",
-                "messages": [
-                    {
-                        "role": "system",
-                        "content": (
-                            "You are taking a strict multiple-choice exam. "
-                            "For each question, give ONLY the final answer in 1-2 sentences. "
-                            "No explanations, no introductions, no 'I'm an AI' statements. "
-                            "If you can't see a video/image/file, use web knowledge and typical context to infer."
-                        )
-                    },
-                    {
-                        "role": "user",
-                        "content": f"Question: {question}\n\nAnswer:"
-                    }
-                ],
-                "temperature": 0.1,  # 更低的隨機性
-                "max_tokens": 300,
-                "top_p": 0.9
-            }
-            response = requests.post(
-                GROQ_API_URL,
-                headers=headers,
-                json=payload,
-                timeout=30
-            )
+            # 增加提示詞，告訴 Agent 如果遇到圖片題該怎麼辦
+            prompt = f"""
+            Answer the following question as strictly and concisely as possible.
+            If the question asks about a specific fact, use the search tool to find it.
+            If the question refers to an image, video, or audio file that you cannot see:
+               1. Try to infer the answer from the text context if possible.
+               2. Or search for the specific text descriptions in the question.
+               3. If absolutely impossible, make an educated guess.
             
-            if response.status_code != 200:
-                error_msg = response.text[:200] if response.text else "Unknown error"
-                return f"API Error {response.status_code}: {error_msg}"
+            Question: {question}
+            """
             
-            result = response.json()
-            answer = result['choices'][0]['message']['content'].strip()
+            # 執行 Agent
+            answer = self.agent.run(prompt)
+            return str(answer)
             
-            # 限制答案長度
-            return answer[:1000] if answer else "Unable to generate answer"
-            
-        except requests.exceptions.Timeout:
-            return "Request timeout - please try again"
-        except requests.exceptions.RequestException as e:
-            return f"Network error: {str(e)[:100]}"
         except Exception as e:
-            return f"Error: {str(e)[:150]}"
+            return f"Error running agent: {str(e)[:150]}"
 
 
 def run_and_submit_all(profile: Optional[gr.OAuthProfile] = None):
@@ -89,9 +72,9 @@ def run_and_submit_all(profile: Optional[gr.OAuthProfile] = None):
     
     # 初始化 Agent
     try:
-        agent = GroqAgent()
-        if agent.agent is None:
-            return "❌ Error: GROQ_API_KEY not found!\n\nPlease add your Groq API key to Secrets:\n1. Go to Settings → Secrets\n2. Add: GROQ_API_KEY=gsk_your_key", None
+        agent_wrapper = GroqAgent()
+        if agent_wrapper.agent is None:
+            return "❌ Error: GROQ_API_KEY not found!\n\nPlease add your Groq API key to Secrets.", None
     except Exception as e:
         return f"❌ Agent initialization failed: {str(e)}", None
     
@@ -118,7 +101,8 @@ def run_and_submit_all(profile: Optional[gr.OAuthProfile] = None):
         print(f"[{idx}/{total}] Processing: {task_id[:8]}...")
         
         try:
-            answer = agent(question_text)
+            # 呼叫我們的 Agent
+            answer = agent_wrapper(question_text)
             
             answers_payload.append({
                 "task_id": task_id,
@@ -128,7 +112,7 @@ def run_and_submit_all(profile: Optional[gr.OAuthProfile] = None):
             results_log.append({
                 "Task ID": task_id[:12] + "...",
                 "Question": question_text[:70] + "...",
-                "Answer": answer[:150] + ("..." if len(answer) > 150 else "")
+                "Answer": str(answer)[:150]
             })
             
         except Exception as e:
@@ -138,8 +122,8 @@ def run_and_submit_all(profile: Optional[gr.OAuthProfile] = None):
                 "submitted_answer": f"Error: {error_msg}"
             })
             results_log.append({
-                "Task ID": task_id[:12] + "...",
-                "Question": question_text[:70] + "...",
+                "Task ID": task_id,
+                "Question": question_text[:50],
                 "Answer": f"Error: {error_msg}"
             })
 
@@ -182,7 +166,7 @@ Great job! 🚀"""
 ❌ Required: 30% to pass
 📈 You need {int((30 * total_q / 100) - correct)} more correct answers
 
-💡 Try running again - different prompts may improve results."""
+💡 Tip: Check if the search tool is working correctly."""
             
     except requests.exceptions.RequestException as e:
         status_msg = f"❌ Submission failed (network error): {str(e)[:200]}"
@@ -195,9 +179,9 @@ Great job! 🚀"""
 # Gradio 介面
 with gr.Blocks(title="Unit 4 Final Assignment", theme=gr.themes.Soft()) as demo:
     gr.Markdown("""
-# 🎓 Unit 4 Final Project: AI Agent
+# 🎓 Unit 4 Final Project: AI Agent (Fixed with smolagents)
 
-### Using Groq API (Llama 3.3 70B)
+### Using Groq API (Llama 3.3 70B) + Search Tool
 **Goal**: Score ≥ 30% to get certificate
     """)
     
@@ -219,10 +203,10 @@ with gr.Blocks(title="Unit 4 Final Assignment", theme=gr.themes.Soft()) as demo:
     gr.Markdown("""
 ---
 ### 💡 Tips
-- Make sure you've added `GROQ_API_KEY` to Secrets (Settings → Secrets)
-- Get free API key at: https://console.groq.com/
-- If score < 30%, you can try running again
+- Make sure `GROQ_API_KEY` is in Secrets.
+- This agent uses DuckDuckGo Search to answer factual questions.
+- Image questions might still fail, but text questions should pass!
     """)
 
 if __name__ == "__main__":
-    demo.launch(debug=True)
+    demo.launch()