Create app.py

app.py

@@ -0,0 +1,35 @@
+import gradio as gr
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+import torch
+import os
+
+# Load token from environment variable
+token = os.getenv('ACCESS_SECRET')
+
+# Specify the repository ID
+model_repo_id = "Ajay12345678980/QA_Chatbot"
+
+# 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
+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
+
+# Set up Gradio interface
+interface = gr.Interface(
+    fn=predict, 
+    inputs="text", 
+    outputs="text", 
+    title="GPT-2 Text Generation",
+    description="Enter some text and see what the model generates!"
+)
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    interface.launch()