Update app.py

app.py

@@ -1,25 +1,13 @@
 import gradio as gr
-from transformers import GPT2LMHeadModel, GPT2Tokenizer
-import torch
-import os
+from transformers import pipeline
 
-# Load token from environment variable
-token = os.getenv('ACCESS_SECRET')
+# Load the model using the Hugging Face pipeline
+model = pipeline("text-generation", model="Ajay12345678980/QA_bot")
 
-# Specify the repository ID
-model_repo_id = "Ajay12345678980/QA_bot"
-
-# Load model and tokenizer
-model = GPT2LMHeadModel.from_pretrained(model_repo_id, use_auth_token=token)
-tokenizer = GPT2Tokenizer.from_pretrained(model_repo_id, use_auth_token=token)
-
-# Define prediction function
+# Define the prediction function
 def predict(text):
-    inputs = tokenizer.encode(text, return_tensors="pt")
-    with torch.no_grad():
-        outputs = model.generate(inputs, max_length=50, do_sample=True)
-    prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return prediction
+    outputs = model(text, max_length=50, do_sample=True)
+    return outputs[0]["generated_text"]
 
 # Set up Gradio interface
 interface = gr.Interface(