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Create app.py
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app.py
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import spaces
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import gradio as gr
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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# Load the tokenizer and model
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model_id = "CohereForAI/c4ai-command-r-v01"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id)
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@spaces.GPU
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def generate_response(user_input, max_new_tokens, temperature):
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# Format message with the command-r chat template
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messages = [{"role": "user", "content": user_input}]
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input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
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# Generate tokens
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gen_tokens = model.generate(
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input_ids['input_ids'],
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max_length=max_new_tokens + input_ids['input_ids'].shape[1], # Adjusting max_length to account for input length
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do_sample=True,
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temperature=temperature,
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)
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# Decode tokens to string
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gen_text = tokenizer.decode(gen_tokens[0])
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return gen_text
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# Define the Gradio interface
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iface = gr.Interface(
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fn=generate_response,
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inputs=[
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gr.inputs.Textbox(lines=2, label="Your Message"),
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gr.inputs.Slider(minimum=10, maximum=100, default=50, label="Max New Tokens"),
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gr.inputs.Slider(minimum=0.1, maximum=1.0, step=0.1, default=0.3, label="Temperature")
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],
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outputs=gr.outputs.Textbox(label="Model Response"),
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title="Text Generation Model Interface",
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description="This is a Gradio interface for a text generation model. Enter your message and adjust the parameters to generate a response."
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)
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# Launch the application
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iface.launch()
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