Update app.py

app.py

@@ -10,14 +10,50 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
 # --- Basic Agent Definition ---
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+
 class BasicAgent:
     def __init__(self):
-        print("BasicAgent initialized.")
+        model_name = "Qwen/Qwen3-0.6B-MLX-bf16"  # or "Qwen/Qwen3‑0.6B" whichever you have access to
+        print(f"Loading model {model_name} …")
+        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
+        self.model = AutoModelForCausalLM.from_pretrained(
+            model_name,
+            torch_dtype="auto",
+            device_map="auto"
+        )
+        self.generator = pipeline(
+            "text-generation",
+            model=self.model,
+            tokenizer=self.tokenizer,
+            max_new_tokens=200,
+            temperature=0.0,  # greedy / deterministic
+            do_sample=False
+        )
+
     def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
-        fixed_answer = "This is a default answer."
-        print(f"Agent returning fixed answer: {fixed_answer}")
-        return fixed_answer
+        print("Received question:", question)
+        # Use Qwen’s chat template (if available)
+        messages = [
+            {"role": "user", "content": question}
+        ]
+        # The Qwen tokenizer may support chat templates
+        prompt = self.tokenizer.apply_chat_template(
+            messages,
+            tokenize=False,
+            add_generation_prompt=True,
+            enable_thinking=False  # or True, depending on mode
+        )
+
+        outputs = self.generator(prompt)
+        text = outputs[0]["generated_text"]
+        # Remove prompt prefix, get only the answer part
+        # The answer begins after prompt
+        answer = text[len(prompt):].strip()
+        # Return first line
+        answer = answer.split("\n")[0].strip()
+        return answer
+
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """