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Create app.py
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app.py
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import gradio as gr
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import random
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import time
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from transformers import AutoTokenizer, AutoModelForCausalLM
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# Initialize the model and tokenizer
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tokenizer = AutoTokenizer.from_pretrained("upstage/llama-30b-instruct-2048")
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model = AutoModelForCausalLM.from_pretrained("upstage/llama-30b-instruct-2048")
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# Define the bot function that will generate the responses
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def bot(history):
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user_message = history[-1][0]
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input_ids = tokenizer.encode(user_message + tokenizer.eos_token, return_tensors='pt')
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response = model.generate(input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
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bot_message = tokenizer.decode(response[:, input_ids.shape[-1]:][0], skip_special_tokens=True)
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history[-1][1] = ""
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for character in bot_message:
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history[-1][1] += character
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time.sleep(0.05)
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yield history
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# Define the functions to add text and files to the chat history
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def add_text(history, text):
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history = history + [(text, None)]
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return history, gr.update(value="", interactive=False)
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def add_file(history, file):
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history = history + [((file.name,), None)]
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return history
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# Initialize the Gradio interface
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with gr.Blocks() as demo:
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chatbot = gr.Chatbot([], elem_id="chatbot").style(height=750)
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with gr.Row():
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with gr.Column(scale=0.85):
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txt = gr.Textbox(show_label=False, placeholder="Enter text and press enter, or upload an image").style(container=False)
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with gr.Column(scale=0.15, min_width=0):
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btn = gr.UploadButton("📁", file_types=["image", "video", "audio"])
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txt_msg = txt.submit(add_text, [chatbot, txt], [chatbot, txt], queue=False).then(bot, chatbot, chatbot)
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txt_msg.then(lambda: gr.update(interactive=True), None, [txt], queue=False)
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file_msg = btn.upload(add_file, [chatbot, btn], [chatbot], queue=False).then(bot, chatbot, chatbot)
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# Run the chatbot
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demo.queue()
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demo.launch()
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