Update app.py

app.py

@@ -1,9 +1,30 @@
-from transformers import pipeline
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
 
-generator = pipeline("text-generation", model="distilgpt2")
-generator(
-    "In this course, we will teach you how to",
-    max_length=30,
-    num_return_sequences=2,
+#set up the model (large version of DialoGPT)
+tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-large")
+model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-large")
 
-)
+#Defining a predict function
+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 = [(response[i], response[i+1]) for i in range(0, len(response)-1, 2)]  # convert to tuples of list
+    return response, history
+
+#creating a gradio interface
+demo = gr.Interface(fn=predict,
+             inputs=["text", "state"],
+             outputs=["chatbot", "state"])
+
+demo.launch()