Update app.py

app.py

@@ -1,60 +1,3 @@
-# app.py
+import gradio as gr
 
-import streamlit as st
-from unsloth import FastLanguageModel
-from transformers import TextStreamer
-
-# To speed up model loading in repeated queries, you can use st.cache_resource (Streamlit 1.18+).
-@st.cache_resource
-def load_unsloth_model(
-    model_name="azizsi/model2", 
-    max_seq_length=4096, 
-    dtype="float16", 
-    load_in_4bit=False
-):
-    """
-    Loads and prepares the model for inference using FastLanguageModel from Unsloth.
-    Returns (model, tokenizer).
-    """
-    model, tokenizer = FastLanguageModel.from_pretrained(
-        model_name=model_name,
-        max_seq_length=max_seq_length,
-        dtype=dtype,
-        load_in_4bit=load_in_4bit
-    )
-
-    # Enable 2x faster inference (per Unsloth docs)
-    FastLanguageModel.for_inference(model)
-
-    return model, tokenizer
-
-
-def main():
-    st.title("Unsloth Model Demo")
-
-    # Provide a text input area for the user
-    user_input = st.text_area("Enter your prompt:", "")
-
-    # Generate button
-    if st.button("Generate"):
-        with st.spinner("Generating response..."):
-            # Load the model & tokenizer
-            model, tokenizer = load_unsloth_model()
-            
-            # Create a TextStreamer to stream tokens or capture final text
-            streamer = TextStreamer(tokenizer)
-
-            # Tokenize user prompt and move to GPU (or the model's device)
-            inputs = tokenizer(user_input, return_tensors="pt").to(model.device)
-            
-            # Generate up to 128 new tokens (modify as desired)
-            outputs = model.generate(**inputs, streamer=streamer, max_new_tokens=128)
-            
-            # If you want to display the entire response at once:
-            generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-            st.markdown("**Response:**")
-            st.write(generated_text)
-
-if __name__ == "__main__":
-    main()
+gr.load("azizsi/model2").launch()