Update app.py

app.py

@@ -4,164 +4,221 @@ import requests
 import pandas as pd
 from typing import Optional
 
-# --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-HF_API_URL = "https://api-inference.huggingface.co/models"
+GROQ_API_URL = "https://api.groq.com/openai/v1/chat/completions"
 
-class BasicAgent:
+class GroqAgent:
+    """使用 Groq API 的 LLM Agent"""
     def __init__(self):
-        """用 HuggingFace Inference API"""
-        self.hf_token = os.getenv("HF_TOKEN")
-        # 改用更穩定的小模型
-        self.model_name = "google/flan-t5-base"
-
-        
-        if not self.hf_token:
-            print("✗ HF_TOKEN not found")
+        self.api_key = os.getenv("GROQ_API_KEY")
+        if not self.api_key:
+            print("✗ GROQ_API_KEY not found in environment variables")
             self.agent = None
             return
         
-        self.agent_url = f"{HF_API_URL}/{self.model_name}"
         self.agent = True
-        print(f"✓ Agent initialized with model: {self.model_name}")
-
+        print("✓ Groq agent initialized successfully")
+    
     def __call__(self, question: str) -> str:
-        """透過 HF Inference API 呼叫模型"""
+        """回答問題"""
         if self.agent is None:
-            return "HF_TOKEN not configured!"
+            return "Error: GROQ_API_KEY not configured. Please add it to Secrets."
         
         try:
-            headers = {"Authorization": f"Bearer {self.hf_token}"}
+            headers = {
+                "Authorization": f"Bearer {self.api_key}",
+                "Content-Type": "application/json"
+            }
             
             payload = {
-                "inputs": question,
-                "parameters": {
-                    "max_new_tokens": 256,
-                }
+                "model": "llama-3.3-70b-versatile",
+                "messages": [
+                    {
+                        "role": "system",
+                        "content": "You are a helpful AI assistant. Answer questions accurately and concisely. Provide direct answers without unnecessary explanation."
+                    },
+                    {
+                        "role": "user",
+                        "content": question
+                    }
+                ],
+                "temperature": 0.2,
+                "max_tokens": 500,
+                "top_p": 0.9
             }
             
             response = requests.post(
-                self.agent_url,
+                GROQ_API_URL,
                 headers=headers,
                 json=payload,
-                timeout=60
+                timeout=30
             )
             
-            if response.status_code == 410:
-                return "Model loading... please try again"
-            
             if response.status_code != 200:
-                return f"API Error {response.status_code}: {response.text[:100]}"
+                error_msg = response.text[:200] if response.text else "Unknown error"
+                return f"API Error {response.status_code}: {error_msg}"
             
             result = response.json()
+            answer = result['choices'][0]['message']['content'].strip()
             
-            if isinstance(result, list) and len(result) > 0:
-                answer = result[0].get("generated_text", "")
-                answer = answer.replace(question, "").strip()
-                return answer[:1000] if answer else "No answer generated"
-            
-            return "Invalid response format"
+            # 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)[:100]}"
+            return f"Error: {str(e)[:150]}"
 
 
 def run_and_submit_all(profile: Optional[gr.OAuthProfile] = None):
     """主要評估和提交函數"""
     
+    # 檢查登入
     if profile is None:
-        return "Please Login to Hugging Face with the button.", None
+        return "⚠️ Please click 'Login with Hugging Face' button first!", None
     
     username = profile.username
     space_id = os.getenv("SPACE_ID", "s1144662")
-
     api_url = DEFAULT_API_URL
-    questions_url = f"{api_url}/questions"
-    submit_url = f"{api_url}/submit"
-
+    
+    # 初始化 Agent
     try:
-        agent = BasicAgent()
+        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
     except Exception as e:
-        return f"Error: {str(e)}", None
-
-    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
-
+        return f"❌ Agent initialization failed: {str(e)}", None
+    
+    # 獲取問題
     try:
-        response = requests.get(questions_url, timeout=30)
+        print("Fetching questions...")
+        response = requests.get(f"{api_url}/questions", timeout=30)
         response.raise_for_status()
         questions_data = response.json()
+        print(f"✓ Got {len(questions_data)} questions")
     except Exception as e:
-        return f"Error fetching questions: {str(e)}", None
+        return f"❌ Failed to fetch questions: {str(e)}", None
 
     answers_payload = []
     results_log = []
-
+    
     total = len(questions_data)
+    
+    # 回答每個問題
     for idx, item in enumerate(questions_data, 1):
         task_id = item.get("task_id")
         question_text = item.get("question")
         
-        print(f"[{idx}/{total}] Processing...")
+        print(f"[{idx}/{total}] Processing: {task_id[:8]}...")
         
         try:
-            submitted_answer = agent(question_text)
+            answer = agent(question_text)
+            
             answers_payload.append({
-                "task_id": task_id, 
-                "submitted_answer": submitted_answer
+                "task_id": task_id,
+                "submitted_answer": answer
             })
+            
             results_log.append({
-                "Task ID": task_id, 
-                "Question": question_text[:80],
-                "Answer": submitted_answer[:200]
+                "Task ID": task_id[:12] + "...",
+                "Question": question_text[:70] + "...",
+                "Answer": answer[:150] + ("..." if len(answer) > 150 else "")
             })
+            
         except Exception as e:
+            error_msg = str(e)[:100]
             answers_payload.append({
-                "task_id": task_id, 
-                "submitted_answer": str(e)
+                "task_id": task_id,
+                "submitted_answer": f"Error: {error_msg}"
             })
             results_log.append({
-                "Task ID": task_id, 
-                "Question": question_text[:80],
-                "Answer": str(e)[:100]
+                "Task ID": task_id[:12] + "...",
+                "Question": question_text[:70] + "...",
+                "Answer": f"Error: {error_msg}"
             })
 
-    submission_data = {
-        "username": username, 
-        "agent_code": agent_code, 
-        "answers": answers_payload
-    }
-    
+    # 提交答案
     try:
-        response = requests.post(submit_url, json=submission_data, timeout=120)
+        print("Submitting answers...")
+        submission_data = {
+            "username": username,
+            "agent_code": f"https://huggingface.co/spaces/{space_id}/tree/main",
+            "answers": answers_payload
+        }
+        
+        response = requests.post(
+            f"{api_url}/submit",
+            json=submission_data,
+            timeout=120
+        )
         response.raise_for_status()
-        result_data = response.json()
+        data = response.json()
         
-        score = result_data.get('score', 'N/A')
-        correct = result_data.get('correct_count', 0)
-        total_q = result_data.get('total_attempted', 0)
-        status_msg = f"✓ Score: {score}% ({correct}/{total_q} correct)"
+        score = data.get('score', 0)
+        correct = data.get('correct_count', 0)
+        total_q = data.get('total_attempted', 0)
+        
+        print(f"✓ Score: {score}% ({correct}/{total_q})")
+        
+        # 生成結果訊息
+        if score >= 30:
+            status_msg = f"""🎉 CONGRATULATIONS! YOU PASSED! 🎉
+
+📊 Final Score: {score}% ({correct}/{total_q} correct)
+✅ Required: 30% (You exceeded it!)
+
+🎓 Next Step: Get Your Certificate
+👉 Visit: https://huggingface.co/spaces/agents-course/Unit4-Final-Certificate
+
+Great job! 🚀"""
+        else:
+            status_msg = f"""📊 Score: {score}% ({correct}/{total_q} correct)
+❌ Required: 30% to pass
+📈 You need {int((30 * total_q / 100) - correct)} more correct answers
+
+💡 Try running again - different prompts may improve results."""
+            
+    except requests.exceptions.RequestException as e:
+        status_msg = f"❌ Submission failed (network error): {str(e)[:200]}"
     except Exception as e:
-        status_msg = f"✗ Submission Failed: {str(e)}"
+        status_msg = f"❌ Submission failed: {str(e)[:200]}"
 
     return status_msg, pd.DataFrame(results_log)
 
 
-with gr.Blocks(title="Unit 4 Final Assignment") as demo:
-    gr.Markdown("# Unit 4 Final Project: AI Agent")
-    gr.Markdown("_Using Hugging Face Inference API_")
+# 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
+
+### Using Groq API (Llama 3.3 70B)
+**Goal**: Score ≥ 30% to get certificate
+    """)
     
     with gr.Row():
         gr.LoginButton(scale=1)
-        run_button = gr.Button("Run Evaluation & Submit All Answers", scale=2, variant="primary")
+        run_btn = gr.Button("🚀 Run Evaluation & Submit All Answers", scale=3, variant="primary")
     
-    status_output = gr.Textbox(label="Result", lines=3, interactive=False)
-    results_table = gr.DataFrame(label="Details", interactive=False)
-
-    run_button.click(
+    gr.Markdown("---")
+    
+    status = gr.Textbox(label="📊 Submission Status", lines=8, interactive=False)
+    details = gr.DataFrame(label="📝 Detailed Results", interactive=False)
+    
+    run_btn.click(
         fn=run_and_submit_all,
         inputs=[],
-        outputs=[status_output, results_table]
+        outputs=[status, details]
     )
+    
+    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
+    """)
 
 if __name__ == "__main__":
     demo.launch(debug=True)