Create app.py

app.py

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+from transformers import AutoModelForCausalLM, AutoTokenizer
+import gradio as gr
+
+# Load the model and tokenizer
+model_name = "microsoft/DialoGPT-small"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Function to generate a response
+def dialoGPT_response(user_input, history):
+    # Encode the new user input, with the history
+    new_user_input_ids = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors='pt')
+
+    # Append the new user input tokens to the chat history
+    bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1) if history else new_user_input_ids
+
+    # Generate a response
+    chat_history_ids = model.generate(
+        bot_input_ids, 
+        max_length=1000,
+        pad_token_id=tokenizer.eos_token_id
+    )
+
+    # Decode the response
+    response = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
+    return response
+
+# Gradio interface
+iface = gr.Interface(
+    fn=dialoGPT_response,
+    inputs=[gr.Textbox(placeholder="Enter your message..."), "state"],
+    outputs="text",
+    title="DialoGPT Chat",
+    description="Chat with DialoGPT-small model."
+)
+
+iface.launch()