change fast api to gradio

app.py

@@ -1,33 +1,36 @@
-from fastapi import FastAPI
-from pydantic import BaseModel
+import gradio as gr
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
 
-app = FastAPI()
-
-# Load the Mongolian Llama model and tokenizer
+# Load model and tokenizer
 model_name = "Dorjzodovsuren/Mongolian_Llama3-v0.1"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(model_name)
 
-class UserInput(BaseModel):
-    text: str
-
-@app.post("/generate/")
-def generate_response(user_input: UserInput):
+def generate_response(input_text):
     # Tokenize the input text
-    inputs = tokenizer(user_input.text, return_tensors="pt")
+    inputs = tokenizer(input_text, return_tensors="pt")
     
     # Generate response
     with torch.no_grad():
         outputs = model.generate(
             **inputs,
             max_length=100,  # Adjust for desired response length
-            num_return_sequences=1,
             temperature=0.7,  # Adjust for creativity
             top_p=0.9        # Adjust for response diversity
         )
-    
     # Decode the generated text
-    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return {"response": response}
+    response_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response_text
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=generate_response,
+    inputs="text",
+    outputs="text",
+    title="Mongolian Llama3 Chatbot",
+    description="Ask anything in Mongolian!"
+)
+
+# Launch the Gradio app
+iface.launch()

requirements.txt

@@ -1,4 +1,3 @@
-fastapi
-uvicorn
+gradio
 transformers
-torch
+torch