Update app.py

app.py

@@ -1,3 +1,4 @@
+'''
 import transformers
 import gradio as gr
 import torch
@@ -55,4 +56,50 @@ gr.Interface(fn=predict,
     outputs=["html", "state"],
     css=css
     ).launch()
+'''
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
+model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
+
+def predict(input, history=[]):
+    # tokenize the new input sentence
+    new_user_input_ids = tokenizer.encode(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)
+
+    # generate a response 
+    history = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id).tolist()
+
+    # convert the tokens to text, and then split the responses into lines
+    response = tokenizer.decode(history[0]).split("<|endoftext|>")
+    response.remove("")
+    
+    # write some HTML
+    html = "<div class='chatbot'>"
+    for m, msg in enumerate(response):
+        cls = "user" if m%2 == 0 else "bot"
+        html += "<div class='msg {}'> {}</div>".format(cls, msg)
+    html += "</div>"
+    
+    return html, history
+
+import gradio as gr
+
+css = """
+.chatbox {display:flex;flex-direction:column}
+.msg {padding:4px;margin-bottom:4px;border-radius:4px;width:80%}
+.msg.user {background-color:cornflowerblue;color:white}
+.msg.bot {background-color:lightgray;align-self:self-end}
+.footer {display:none !important}
+"""
+
+gr.Interface(fn=predict,
+             theme="default",
+             inputs=[gr.inputs.Textbox(placeholder="How are you?"), "state"],
+             outputs=["html", "state"],
+             css=css).launch()