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uploaded the py and requirements
Browse files- app.py +66 -0
- requirements.txt +3 -0
app.py
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#completed
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import gradio as gr
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_name="microsoft/DialoGPT-medium"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name)
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def user(message, history):
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return "", history + [[message, None]]
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def bot(history,temperature, max_length, top_p,top_k):
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user_message = history[-1][0]
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new_user_input_ids = tokenizer.encode(
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user_message + tokenizer.eos_token, return_tensors="pt"
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)
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# append the new user input tokens to the chat history
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bot_input_ids = torch.cat([torch.LongTensor([]), new_user_input_ids], dim=-1)
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# generate a response
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response = model.generate(
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bot_input_ids,
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pad_token_id=tokenizer.eos_token_id,
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temperature = float(temperature),
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max_length=max_length,
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top_p=float(top_p),
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top_k=top_k,
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do_sample=True
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).tolist()
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# convert the tokens to text, and then split the responses into lines
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response = tokenizer.decode(response[0]).split("<|endoftext|>")
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response = [
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(response[i], response[i + 1]) for i in range(0, len(response) - 1, 2)
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] # convert to tuples of list
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history[-1] = response[0]
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return history
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with gr.Blocks() as demo:
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temperature = gr.Slider(0, 5, value=0.8, step=0.1, label='Temperature')
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max_length = gr.Slider(0, 8192, value=256, step=1, label='Max Length')
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top_p = gr.Slider(0, 1, value=0.8, step=0.1, label='Top P')
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top_k = gr.Slider(0, 50, value=50, step=1, label='Top K')
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chatbot = gr.Chatbot()
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msg = gr.Textbox()
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submit = gr.Button("Submit")
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clear = gr.Button("Clear")
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examples = gr.Examples(examples=["Why is a raven like a writing desk?", "What comes once in a minute, twice in a moment, but never in a thousand years?",
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"What can be touched but can't be seen?"],inputs=[msg])
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#submit.click(bot,[msg,chatbot,temperature, max_length, top_p,top_k],chatbot)
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submit.click(user, [msg, chatbot], [msg, chatbot], queue=False).then(
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bot, [chatbot,temperature,max_length,top_p,top_k], chatbot
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)
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clear.click(lambda: None, None, chatbot, queue=False)
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demo.launch()
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requirements.txt
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gradio
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transformers
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torch
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