Update app.py
Browse files
app.py
CHANGED
@@ -6,9 +6,8 @@ import requests
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#Streaming endpoint
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API_URL = "https://api.openai.com/v1/chat/completions" #os.getenv("API_URL") + "/generate_stream"
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#Testing with my Open AI Key
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#OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
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def predict(inputs, top_p, temperature, openai_api_key, chat_counter, chatbot=[], history=[]): #repetition_penalty, top_k
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payload = {
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@@ -94,31 +93,20 @@ def reset_textbox():
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title = """<h1 align="center"> ChatGPT API</h1>"""
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description = """Language models can be conditioned to act like dialogue agents through a conversational prompt that typically takes the form:
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User: <utterance>
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Assistant: <utterance>
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User: <utterance>
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Assistant: <utterance>
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...
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```
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In this app, you can explore the outputs of a gpt-3.5-turbo LLM.
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"""
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with gr.Blocks(css = """#col_container {width: 1000px; margin-left: auto; margin-right: auto;}
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#chatbot {height: 520px; overflow: auto;}""") as demo:
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with gr.Column(elem_id = "col_container"):
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openai_api_key = gr.Textbox(type='password', label="Enter your OpenAI API key here")
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chatbot = gr.Chatbot(elem_id='chatbot') #c
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inputs = gr.Textbox(placeholder= "
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state = gr.State([]) #s
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b1 = gr.Button()
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#inputs, top_p, temperature, top_k, repetition_penalty
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with gr.Accordion("Parameters", open=False):
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top_p = gr.Slider( minimum=-0, maximum=1.0, value=1.0, step=0.05, interactive=True, label="Top-p (nucleus sampling)",)
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temperature = gr.Slider( minimum=-0, maximum=5.0, value=1.0, step=0.1, interactive=True, label="Temperature",)
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#top_k = gr.Slider( minimum=1, maximum=50, value=4, step=1, interactive=True, label="Top-k",)
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#repetition_penalty = gr.Slider( minimum=0.1, maximum=3.0, value=1.03, step=0.01, interactive=True, label="Repetition Penalty", )
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chat_counter = gr.Number(value=0, visible=False, precision=0)
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inputs.submit( predict, [inputs, top_p, temperature, openai_api_key, chat_counter, chatbot, state], [chatbot, state, chat_counter],)
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@@ -126,6 +114,5 @@ with gr.Blocks(css = """#col_container {width: 1000px; margin-left: auto; margin
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b1.click(reset_textbox, [], [inputs])
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inputs.submit(reset_textbox, [], [inputs])
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#gr.Markdown(description)
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demo.queue().launch(debug=True)
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#Streaming endpoint
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API_URL = "https://api.openai.com/v1/chat/completions" #os.getenv("API_URL") + "/generate_stream"
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openai_api_key = "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"
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def predict(inputs, top_p, temperature, openai_api_key, chat_counter, chatbot=[], history=[]): #repetition_penalty, top_k
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payload = {
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title = """<h1 align="center"> ChatGPT API</h1>"""
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description = """Language models can be conditioned to act like dialogue agents through a conversational prompt that typically takes the form:
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with gr.Blocks(css = """#col_container {width: 1000px; margin-left: auto; margin-right: auto;}
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#chatbot {height: 520px; overflow: auto;}""") as demo:
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with gr.Column(elem_id = "col_container"):
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openai_api_key = gr.Textbox(type='password', label="Enter your OpenAI API key here")
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chatbot = gr.Chatbot(elem_id='chatbot') #c
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inputs = gr.Textbox(placeholder= "Type here!", label= "Type an input and press Enter") #t
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state = gr.State([]) #s
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b1 = gr.Button()
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with gr.Accordion("Parameters", open=False):
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top_p = gr.Slider( minimum=-0, maximum=1.0, value=1.0, step=0.05, interactive=True, label="Top-p (nucleus sampling)",)
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temperature = gr.Slider( minimum=-0, maximum=5.0, value=1.0, step=0.1, interactive=True, label="Temperature",)
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chat_counter = gr.Number(value=0, visible=False, precision=0)
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inputs.submit( predict, [inputs, top_p, temperature, openai_api_key, chat_counter, chatbot, state], [chatbot, state, chat_counter],)
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b1.click(reset_textbox, [], [inputs])
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inputs.submit(reset_textbox, [], [inputs])
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demo.queue().launch(debug=True)
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