Update space
Browse files- app.py +29 -18
- requirements.txt +4 -1
app.py
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import gradio as gr
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from
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"""
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""
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def respond(
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messages.append({"role": "user", "content": message})
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for message in client.chat_completion(
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messages,
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temperature=temperature,
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top_p=top_p
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)
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response += token
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yield response
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"""
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For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
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"""
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demo = gr.ChatInterface(
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respond,
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additional_inputs=[
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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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device = "cuda" if torch.cuda.is_available() else "cpu"
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model_path = "Hack337/WavGPT-1.0" # Replace with the actual model path
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model = AutoModelForCausalLM.from_pretrained(
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model_path,
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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def respond(
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messages.append({"role": "user", "content": message})
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(device)
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generated_ids = model.generate(
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model_inputs.input_ids,
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max_new_tokens=max_tokens,
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pad_token_id=tokenizer.eos_token_id,
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temperature=temperature,
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top_p=top_p
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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return response
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demo = gr.ChatInterface(
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respond,
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additional_inputs=[
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requirements.txt
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huggingface_hub==0.22.2
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minijinja
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huggingface_hub==0.22.2
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minijinja
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torch
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transformers
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gradio
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