Update app.py

app.py

@@ -1,13 +1,32 @@
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import os
 from huggingface_hub import login
+import gradio as gr
 
-
+# 1. Authenticate with Hugging Face token from secrets
 hf_token = os.environ.get("hf_space_token")
-
-
 login(token=hf_token)
 
+# 2. Load Gemma model and tokenizer (GATED model needs token)
 model_name = "google/gemma-3-1b-it"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name, token=hf_token)
+model = AutoModelForCausalLM.from_pretrained(model_name, token=hf_token)
+
+# 3. Define response generation function
+def generate_response(prompt):
+    inputs = tokenizer(prompt, return_tensors="pt")
+    outputs = model.generate(**inputs, max_new_tokens=100, do_sample=True, temperature=0.7)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# 4. Create Gradio interface
+iface = gr.Interface(
+    fn=generate_response,
+    inputs=gr.Textbox(lines=2, placeholder="Ask something..."),
+    outputs="text",
+    title="Chat with Gemma",
+    description="This chatbot is powered by Google's Gemma model running in Hugging Face Spaces."
+)
+
+# 5. Launch app
+iface.launch()
+