Create Python

Python

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+# chatbot/app.py
+
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import gradio as gr
+
+# Load pretrained DialoGPT model and tokenizer
+checkpoint = "microsoft/DialoGPT-medium"
+tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+model = AutoModelForCausalLM.from_pretrained(checkpoint)
+
+# Chat history for session
+chat_history_ids = None
+
+def respond(user_input, history=[]):
+    global chat_history_ids
+    
+    # Encode user input and append to chat history
+    new_input_ids = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors='pt')
+    
+    if chat_history_ids is not None:
+        bot_input_ids = torch.cat([chat_history_ids, new_input_ids], dim=-1)
+    else:
+        bot_input_ids = new_input_ids
+
+    chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
+
+    output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
+
+    history.append((user_input, output))
+    return history, history
+
+# Gradio UI
+with gr.Blocks() as demo:
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox(label="Type your message here")
+    clear = gr.Button("Clear Chat")
+
+    state = gr.State([])
+
+    msg.submit(respond, [msg, state], [chatbot, state])
+    clear.click(lambda: ([], []), None, [chatbot, state])
+
+if __name__ == "__main__":
+    demo.launch()