Update app.py

app.py

@@ -1,20 +1,20 @@
-import openai
-import gradio
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
 
-openai.api_key = "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"
 
-messages = [{"role": "system", "content": "You are a financial experts that specializes in real estate investment and negotiation"}]
+tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
+model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
 
-def CustomChatGPT(user_input):
-    messages.append({"role": "user", "content": user_input})
-    response = openai.ChatCompletion.create(
-        model = "gpt-3.5-turbo",
-        messages = messages
-    )
-    ChatGPT_reply = response["choices"][0]["message"]["content"]
-    messages.append({"role": "assistant", "content": ChatGPT_reply})
-    return ChatGPT_reply
+# Let's chat for 5 lines
+for step in range(5):
+    # encode the new user input, add the eos_token and return a tensor in Pytorch
+    new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
 
-demo = gradio.Interface(fn=CustomChatGPT, inputs = "text", outputs = "text", title = "Real Estate Pro")
+    # append the new user input tokens to the chat history
+    bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
 
-demo.launch(share=True)
+    # generated a response while limiting the total chat history to 1000 tokens, 
+    chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
+
+    # pretty print last ouput tokens from bot
+    print("DialoGPT: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))