Create app.py

app.py

@@ -0,0 +1,59 @@
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Load model and tokenizer
+model_name = "microsoft/DialoGPT-small"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+def respond(message, chat_history, chat_history_ids):
+    # Encode user input
+    new_input_ids = tokenizer.encode(message + tokenizer.eos_token, return_tensors="pt")
+    
+    # Append to chat history
+    if chat_history_ids is not None:
+        input_ids = torch.cat([chat_history_ids, new_input_ids], dim=-1)
+    else:
+        input_ids = new_input_ids
+    
+    # Generate response
+    chat_history_ids = model.generate(
+        input_ids,
+        max_length=1000,
+        pad_token_id=tokenizer.eos_token_id,
+        no_repeat_ngram_size=3,
+        do_sample=True,
+        top_k=50,
+        top_p=0.95,
+        temperature=0.8
+    )
+    
+    # Decode response
+    response = tokenizer.decode(
+        chat_history_ids[:, input_ids.shape[-1]:][0], 
+        skip_special_tokens=True
+    )
+    
+    # Update conversation history
+    chat_history.append((message, response))
+    
+    return "", chat_history, chat_history_ids
+
+with gr.Blocks() as demo:
+    # Store model's conversation history
+    state = gr.State()
+    
+    gr.Markdown("## DialoGPT Chatbot")
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox(label="Your Message")
+    clear = gr.Button("Clear History")
+
+    msg.submit(
+        respond,
+        [msg, chatbot, state],
+        [msg, chatbot, state]
+    )
+    clear.click(lambda: (None, None), outputs=[chatbot, state], queue=False)
+
+demo.launch()