init

app.py

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+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+import gradio as gr
+
+
+# Load a conversational model
+model_name = "microsoft/DialoGPT-medium"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+
+def chatbot(input_text, history=[]):
+    # Tokenize input and history
+    inputs = tokenizer.encode(input_text + tokenizer.eos_token, return_tensors="pt")
+    
+    # Append the conversation history
+    bot_input_ids = torch.cat([torch.tensor(history, dtype=torch.long), inputs], dim=-1) if history else inputs
+    
+    # Generate response
+    history = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
+    response = tokenizer.decode(history[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
+    return response, history.tolist()
+
+
+
+# Define the interface
+with gr.Blocks() as demo:
+    chatbot_widget = gr.Chatbot()
+    user_input = gr.Textbox(label="Type your message here")
+    submit_button = gr.Button("Send")
+    
+    # Function to handle chat interactions
+    def respond(message, history=[]):
+        response, history = chatbot(message, history)
+        history.append((message, response))  # Append to chat history
+        return history, history
+    
+    # Bind the button click to the chatbot response function
+    submit_button.click(respond, [user_input, chatbot_widget], [chatbot_widget, chatbot_widget])
+
+# Launch the app
+demo.launch()