Create app.py

app.py

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+import gradio as gr
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+
+# Load the model and tokenizer from Hugging Face model hub
+model_name = "gpt2"  # You can replace "gpt2" with your fine-tuned model if you have one
+model = GPT2LMHeadModel.from_pretrained(model_name)
+tokenizer = GPT2Tokenizer.from_pretrained(model_name)
+
+def chat_with_me(input_text, history=[]):
+    # Encode the new input with history
+    new_input_ids = tokenizer.encode(input_text + tokenizer.eos_token, return_tensors="pt")
+
+    # Append the new user input to the chat history
+    bot_input_ids = torch.cat([torch.tensor(history), new_input_ids], dim=-1) if history else new_input_ids
+
+    # Generate the model's response
+    history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
+
+    # Decode the response and append to history
+    response = tokenizer.decode(history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
+    history += new_input_ids.tolist()
+
+    return response, history
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        chat = gr.Chatbot()
+        user_input = gr.Textbox(placeholder="Ask me anything...")
+    with gr.Row():
+        clear = gr.Button("Clear")
+
+    # Define interactions
+    user_input.submit(chat_with_me, [user_input, chat], [chat, user_input])
+    clear.click(lambda: None, None, chat)
+
+demo.launch()