Update app.py

app.py

@@ -3,8 +3,9 @@ import numpy as np
 import tensorflow as tf
 import gradio as gr 
 import openai
+import os
 
-
+# Sentiment Analysis Pre-Trained Model
 model_path = "leadingbridge/sentiment-analysis"
 tokenizer = BertTokenizerFast.from_pretrained(model_path)
 model = TFBertForSequenceClassification.from_pretrained(model_path, id2label={0: 'negative', 1: 'positive'} )
@@ -14,27 +15,43 @@ def sentiment_analysis(text):
   result = pipe(text)
   return result
 
-def openai_chatbot(prompt):
-    # Set up the OpenAI API client
-    openai.api_key = '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'
-
-    # Set up the model and prompt
-    model_engine = "text-davinci-003"
-
-    # Generate a response
-    completion = openai.Completion.create(
-        engine=model_engine,
-        prompt=prompt,
-        max_tokens=1024,
-        n=1,
-        stop=None,
-        temperature=0.5,
+
+# Open AI Chatbot Model
+openai.api_key = "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"
+
+start_sequence = "\nAI:"
+restart_sequence = "\nHuman: "
+
+prompt = "The following is a conversation with an AI assistant. The assistant is helpful, creative, clever, and very friendly.\n\nHuman: Hello, who are you?\nAI: I am an AI created by OpenAI. How can I help you today?\nHuman: "
+
+def openai_create(prompt):
+
+    response = openai.Completion.create(
+    model="text-davinci-003",
+    prompt=prompt,
+    temperature=0.9,
+    max_tokens=150,
+    top_p=1,
+    frequency_penalty=0,
+    presence_penalty=0.6,
+    stop=[" Human:", " AI:"]
     )
 
-    response = completion.choices[0].text
+    return response.choices[0].text
+
+
 
-    return f'🤖 {response}'
+def chatgpt_clone(input, history):
+    history = history or []
+    s = list(sum(history, ()))
+    s.append(input)
+    inp = ' '.join(s)
+    output = openai_create(inp)
+    history.append((input, output))
+    return history, history
 
+
+# Gradio Output Model
 with gr.Blocks() as demo:
     gr.Markdown("Choose the Chinese NLP model you want to use.")
     with gr.Tab("Sentiment Analysis"):
@@ -42,9 +59,11 @@ with gr.Blocks() as demo:
         text_button.click(fn=sentiment_analysis,inputs=gr.Textbox(placeholder="Enter a positive or negative sentence here..."),
                  outputs=gr.Textbox(label="Sentiment Analysis"))
     with gr.Tab("General Chatbot"):
-        text_button = gr.Button("proceed")          
-        text_button.click(fn=openai_chatbot,inputs=gr.Textbox(placeholder="Enter any topic you would like to discuss in Chinese"),
-                 outputs=gr.Textbox(label="Chatbot Response"))
+        chatbot = gr.Chatbot()
+        message = gr.Textbox(placeholder=prompt)
+        state = gr.State()
+        submit = gr.Button("SEND")
+        submit.click(chatgpt_clone, inputs=[message, state], outputs=[chatbot, state])
 
 
 demo.launch(inline=False)