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Update app.py
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app.py
CHANGED
@@ -11,19 +11,26 @@ import os
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load_dotenv() # load environment variables from .env file
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api_key = os.getenv("OPENAI_API_KEY") # access the value of the OPENAI_API_KEY environment variable
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def
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title = "ChatSherman"
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description = "This is an AI chatbot powered by ShermanAI. Enter your question below to get started."
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@@ -32,7 +39,4 @@ examples = [
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["Is my personal information and data safe when I use the ChatSherman chatbot?", []],
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["What are some common applications of deep learning in engineering?", []]
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]
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outputs = ["chatbot", "state"]
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interface = gr.Interface(fn=chatbot, inputs=inputs, outputs=outputs, title=title, description=description, examples=examples)
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interface.launch(debug=True)
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load_dotenv() # load environment variables from .env file
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api_key = os.getenv("OPENAI_API_KEY") # access the value of the OPENAI_API_KEY environment variable
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def predict(message, history):
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prompt = "I'm an AI chatbot named ChatSherman designed by a student named ShermanAI at the Department of Electronic and Information Engineering at The Hong Kong Polytechnic University to help you with your engineering questions. Also, I can assist with a wide range of topics and questions."
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history = [(prompt, '')] + history
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history_openai_format = []
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for human, assistant in history:
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history_openai_format.append({"role": "user", "content": human })
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history_openai_format.append({"role": "assistant", "content": assistant})
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history_openai_format.append({"role": "user", "content": message})
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response = openai.ChatCompletion.create(
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model='gpt-3.5-turbo',
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messages= history_openai_format,
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temperature=1.0,
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stream=True
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)
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partial_message = ""
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for chunk in response:
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if len(chunk['choices'][0]['delta']) != 0:
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partial_message = partial_message + chunk['choices'][0]['delta']['content']
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yield partial_message
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title = "ChatSherman"
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description = "This is an AI chatbot powered by ShermanAI. Enter your question below to get started."
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["Is my personal information and data safe when I use the ChatSherman chatbot?", []],
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["What are some common applications of deep learning in engineering?", []]
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]
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gr.ChatInterface(predict, title=title, description=description, examples=examples).queue().launch(debug=True)
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