Update app.py
Browse files
app.py
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import gradio as gr
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from transformers import AutoTokenizer,
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from auto_gptq import
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import torch
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import os
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#
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MODEL_NAME = "TheBloke/deepseek-coder-1.3b-instruct-GPTQ"
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os.makedirs(cache_dir, exist_ok=True)
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tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, use_fast=True, cache_dir=cache_dir)
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model = AutoGPTQForCausalLM.from_quantized(
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MODEL_NAME,
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low_cpu_mem_usage=True,
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cache_dir=cache_dir
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)
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def generate_text(prompt, max_length=512, temperature=0.7):
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"""Generate text with safety checks and context awareness"""
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full_prompt = f"Instruct: {prompt}\nOutput:"
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with torch.inference_mode():
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response = generator(
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full_prompt,
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max_new_tokens=max_length,
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temperature=temperature,
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do_sample=True,
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pad_token_id=tokenizer.eos_token_id
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)[0]["generated_text"]
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# Remove prompt from output
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return response.split("Output:")[-1].strip()
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# Gradio interface with enhanced UX
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with gr.Blocks(theme="soft", css=".gradio-container {max-width: 800px; margin: auto;}") as demo:
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gr.Markdown("""
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# 🧠 DeepSeek Coder 1.3B Instruct (GPTQ)
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*Text-to-Code Generation App*
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Enter a programming instruction below and adjust parameters for optimal output.
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""")
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with gr.Row():
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prompt = gr.Textbox(
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label="Enter your instruction",
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placeholder="Write a Python function to calculate Fibonacci numbers...",
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lines=4
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)
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submit.click(
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fn=generate_text,
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outputs=output
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)
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gr.
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import gradio as gr
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from auto_gptq import BaseQuantizeConfig
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import torch
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# Initialize model and tokenizer
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MODEL_NAME = "TheBloke/deepseek-coder-1.3b-instruct-GPTQ"
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tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, use_fast=True)
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model = AutoModelForCausalLM.from_pretrained(
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MODEL_NAME,
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device_map="cpu", # Optimized for CPU
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quantization_config=BaseQuantizeConfig(), # Required for GPTQ models
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torch_dtype=torch.float32, # Better CPU compatibility
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low_cpu_mem_usage=True
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def generate_text(prompt, max_length=100, temperature=0.7):
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inputs = tokenizer(prompt, return_tensors="pt").to("cpu")
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with torch.no_grad():
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outputs = model.generate(
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**inputs,
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max_length=max_length,
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temperature=temperature,
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pad_token_id=tokenizer.eos_token_id
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)
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return tokenizer.decode(outputs[0], skip_special_tokens=True)
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# Gradio UI
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with gr.Blocks(theme="soft") as demo:
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gr.Markdown("# 🧠 DeepSeek Coder 1.3B Text Generator\nOptimized for CPU execution on HuggingFace Spaces")
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with gr.Row():
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with gr.Column():
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prompt = gr.Textbox(
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label="Input Prompt",
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placeholder="Enter your programming/code-related question...",
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lines=5
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)
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max_length = gr.Slider(50, 500, value=150, label="Max Output Length")
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temperature = gr.Slider(0.1, 1.0, value=0.7, label="Creativity Level")
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submit = gr.Button("Generate Code", variant="primary")
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output = gr.Textbox(label="Generated Output", lines=10)
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submit.click(
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fn=generate_text,
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outputs=output
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gr.Examples(
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examples=[
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["Write a Python function to calculate Fibonacci numbers"],
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["Explain the difference between list and tuples in Python"],
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["Create a simple Flask API endpoint for user registration"]
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],
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fn=generate_text,
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inputs=[prompt, max_length, temperature],
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outputs=output,
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cache_examples=False # Save memory
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)
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if __name__ == "__main__":
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demo.launch()
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