prompt model change

app.py

@@ -10,7 +10,8 @@ from transformers import (
     AutoFeatureExtractor,
     AutoModelForImageClassification,
     AutoTokenizer,
-    AutoModelForCausalLM
+    AutoModelForCausalLM,
+    pipeline     # <-- added
 )
 
 # ------------------ LOAD CLASSIFIER ------------------
@@ -42,33 +43,50 @@ chat_model = AutoModelForCausalLM.from_pretrained(
     tiny_model,
     torch_dtype=torch.float32)
 
+# ✅ Create pipeline (this was missing)
+pipe = pipeline(
+    "text-generation",
+    model=chat_model,
+    tokenizer=tokenizer,
+    device_map="auto",
+    max_new_tokens=200
+)
+
+
 def explain_recycling(label):
-    prompt = f"""
-You are an expert in waste sorting.
-Return ONLY the following 2 bullet points:
-
-• Recycling type: (one short category)
-• Disposal: (1 clear and correct sentence)
-
-Example:
-Item: Glass bottle
-• Recycling type: Glass recycling
-• Disposal: Rinse and put in the glass-recycling bin.
-
-Now answer for:
-Item: {label}
-"""
-
-    inputs = tokenizer(prompt, return_tensors="pt")
-    outputs = chat_model.generate(
-        **inputs,
-        max_new_tokens=80,
-        temperature=0.3,
-        do_sample=True,
-        top_p=0.9
+    system_msg = {
+        "role": "system",
+        "content": (
+            "You are an expert in waste sorting. "
+            "You ALWAYS answer using exactly two bullet points:\n"
+            "• Recycling type: <one short category>\n"
+            "• Disposal: <one clear correct sentence>\n"
+            "No extra text, no introductions, no explanations."
+        )
+    }
+
+    user_msg = {
+        "role": "user",
+        "content": f"Item: {label}\nReturn the two bullet points now."
+    }
+
+    messages = [system_msg, user_msg]
+
+    # Chat template
+    prompt = tokenizer.apply_chat_template(
+        messages, 
+        tokenize=False, 
+        add_generation_prompt=True
     )
 
-    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+    # Use the pipeline (fixed)
+    outputs = pipe(
+        prompt,
+        do_sample=False   # deterministic to avoid repeating instructions
+    )
+
+    return outputs[0]["generated_text"]
+
 
 # ------------------ PIPELINE ------------------
 def full_pipeline(image):