Update app.py

app.py

@@ -4,7 +4,6 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
 
 MODEL_ID = "ibm-granite/granite-3.3-2b-instruct"
 
-# Load tokenizer & model
 tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
 model = AutoModelForCausalLM.from_pretrained(
     MODEL_ID,
@@ -12,9 +11,16 @@ model = AutoModelForCausalLM.from_pretrained(
     device_map="auto"
 )
 
-def chat(user_input):
+def summarize(text):
     messages = [
-        {"role": "user", "content": user_input},
+        {
+            "role": "system",
+            "content": "You are an expert assistant. Summarize the given text into clear, concise bullet points."
+        },
+        {
+            "role": "user",
+            "content": text
+        }
     ]
 
     inputs = tokenizer.apply_chat_template(
@@ -27,34 +33,32 @@ def chat(user_input):
 
     outputs = model.generate(
         **inputs,
-        max_new_tokens=128,
-        do_sample=True,
-        temperature=0.7,
-        top_p=0.9
+        max_new_tokens=200,
+        temperature=0.3,
+        do_sample=False
     )
 
-    response = tokenizer.decode(
+    summary = tokenizer.decode(
         outputs[0][inputs["input_ids"].shape[-1]:],
         skip_special_tokens=True
     )
 
-    return response
+    return summary
 
 
-# Gradio Interface
 demo = gr.Interface(
-    fn=chat,
+    fn=summarize,
     inputs=gr.Textbox(
-        lines=3,
-        placeholder="Ask something...",
-        label="User Input"
+        lines=10,
+        placeholder="Paste text to summarize...",
+        label="Input Text"
     ),
     outputs=gr.Textbox(
-        lines=6,
-        label="Model Response"
+        lines=8,
+        label="Summary (Bullet Points)"
     ),
-    title="IBM Granite 3.3-2B Instruct",
-    description="A simple Gradio chat interface using IBM Granite on Hugging Face Spaces."
+    title="Text Summarizer (Point-wise)",
+    description="Summarizes input text into clear bullet points using IBM Granite 3.3-2B Instruct."
 )
 
 if __name__ == "__main__":