Update app.py

app.py

@@ -1,35 +1,17 @@
 import gradio as gr
 from transformers import pipeline
-from fastapi import FastAPI
-from pydantic import BaseModel
-import uvicorn
 
 # Load the model
 model_name = "nvidia/Llama3-ChatQA-1.5-8B"
 qa_pipeline = pipeline("text-generation", model=model_name)
 
-# FastAPI app
-app = FastAPI()
-
-class Query(BaseModel):
-    inputs: str
-
-@app.post("/predict")
-async def predict(query: Query):
-    response = qa_pipeline(query.inputs, max_length=250)
-    return {"generated_text": response[0]["generated_text"]}
-
-# Gradio app
 def generate_answer(question):
+    # Generate the answer using the model
     response = qa_pipeline(question, max_length=250)
     return response[0]["generated_text"]
 
+# Create the Gradio interface
 iface = gr.Interface(fn=generate_answer, inputs="text", outputs="text", title="Llama3 ChatQA")
 
-# Mount Gradio app to FastAPI
-@app.get("/")
-async def gradio_app():
-    return gr.mount_gradio_app(app, iface)
-
-if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=7860)
+# Launch the interface and set share=True to create a public URL
+iface.launch(share=True)