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  1. app.py +55 -0
  2. best_model.pt +3 -0
  3. requirements.txt +3 -0
app.py ADDED
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+ import gradio as gr
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+ import torch
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+ from model_utils import load_model, generate_text
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+
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+ # Initialize model
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+ try:
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+ model_path = "best_model.pt" # Local model file
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+ model = load_model(model_path)
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+ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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+ model = model.to(device)
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+ except Exception as e:
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+ print(f"Error loading model: {e}")
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+ model = None
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+
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+ def predict(prompt, max_tokens, temperature, top_k):
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+ """Wrapper function for Gradio interface"""
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+ if model is None:
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+ return "Error: Model failed to load. Please check the logs."
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+
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+ try:
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+ generated_text = generate_text(
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+ model=model,
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+ prompt=prompt,
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+ max_new_tokens=max_tokens,
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+ temperature=temperature,
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+ top_k=top_k
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+ )
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+ return generated_text
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+ except Exception as e:
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+ return f"Error during generation: {str(e)}"
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+
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+ # Create Gradio interface
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+ demo = gr.Interface(
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+ fn=predict,
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+ inputs=[
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+ gr.Textbox(label="Enter your prompt", lines=3),
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+ gr.Slider(minimum=1, maximum=200, value=50, step=1, label="Max Tokens"),
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+ gr.Slider(minimum=0.1, maximum=2.0, value=0.8, step=1, label="Temperature"),
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+ gr.Slider(minimum=1, maximum=100, value=40, step=1, label="Top-k"),
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+ ],
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+ outputs=gr.Textbox(label="Generated Text", lines=5),
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+ title="GPT Text Generation",
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+ description="Enter a text prompt and the model will generate a continuation.",
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+ examples=[
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+ ["The quick brown fox", 50, 0.8, 40],
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+ ["Once upon a time", 100, 0.9, 50],
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+ ["In the distant future", 75, 0.7, 30],
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+ ],
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+ )
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+
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+ if __name__ == "__main__":
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+ demo.launch(share=True) # You can set share=False if you don't want to share publicly
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+ else:
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+ # For Hugging Face Spaces
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+ app = demo.launch(share=False)
best_model.pt ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:a7a7c8f4600dd1463f53caa8e594dc828d421e9e9001ed42aeb6b6ee8064abf7
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+ size 921869805
requirements.txt ADDED
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+ torch
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+ tiktoken
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+ gradio