switch to gradio

app.py

@@ -1,12 +1,15 @@
 from transformers import AutoTokenizer, AutoModelForCausalLM
-import streamlit as st
+import gradio as gr
 
 tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-2", trust_remote_code=True)
 model = AutoModelForCausalLM.from_pretrained("microsoft/phi-2", torch_dtype="auto", flash_attn=True, flash_rotary=True, fused_dense=True, device_map="cuda", trust_remote_code=True)
 
-prompt = st.text_input("Input prompt", value="Write a detailed analogy between mathematics and a lighthouse.")
-length = st.number_input("Max token length", value=200)
-inputs = tokenizer(prompt, return_tensors="pt", return_attention_mask=False)
-outputs = model.generate(**inputs, max_length=length)
-text = tokenizer.batch_decode(outputs)[0]
-st.write(text)
+def generate(prompt, length):
+    inputs = tokenizer(prompt, return_tensors="pt", return_attention_mask=False)
+    outputs = model.generate(**inputs, max_length=length)
+    return tokenizer.batch_decode(outputs)[0]
+
+demo = gr.Interface(fn=generate, inputs=["text", "number"], outputs="text")
+
+if __name__ == "__main__":
+   demo.launch(show_api=False)