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Update app.py
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app.py
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@@ -2,6 +2,7 @@ import gradio as gr
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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import spaces
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# Initialize the Qwen model and tokenizer
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@@ -12,7 +13,7 @@ tokenizer = AutoTokenizer.from_pretrained(model_name)
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# Function to generate README and documentation
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@spaces.GPU
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def generate_documentation(code_input):
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prompt = f"Generate README
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messages = [
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{"role": "system", "content": "You are CodeDocify, a highly efficient and intelligent assistant designed to analyze code and generate comprehensive, clear, and concise documentation. Your purpose is to help developers by producing well-structured README files and detailed explanations of their code. You aim to simplify complex code into easily understandable documentation, ensuring that your responses are accurate, professional, and easy to follow."},
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@@ -34,7 +35,11 @@ def generate_documentation(code_input):
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# Gradio interface
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def process_code(code_input):
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documentation = generate_documentation(code_input)
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# Set up the Gradio app with Bootstrap, icons, and smiley
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with gr.Blocks(css=".container { font-family: 'Roboto', sans-serif; } .btn-primary { background-color: #007bff; } .icon { margin-right: 10px; }") as app:
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@@ -51,10 +56,11 @@ with gr.Blocks(css=".container { font-family: 'Roboto', sans-serif; } .btn-prima
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generate_button = gr.Button("Generate Documentation", elem_classes="btn btn-primary")
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with gr.Row():
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output_text = gr.Markdown()
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# Bind function to button click
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generate_button.click(process_code, inputs=code_input, outputs=output_text)
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# Launch the Gradio app
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app.launch()
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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import spaces
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import io
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# Initialize the Qwen model and tokenizer
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# Function to generate README and documentation
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@spaces.GPU
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def generate_documentation(code_input):
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prompt = f"Generate README for the following code:\n\n{code_input}"
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messages = [
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{"role": "system", "content": "You are CodeDocify, a highly efficient and intelligent assistant designed to analyze code and generate comprehensive, clear, and concise documentation. Your purpose is to help developers by producing well-structured README files and detailed explanations of their code. You aim to simplify complex code into easily understandable documentation, ensuring that your responses are accurate, professional, and easy to follow."},
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# Gradio interface
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def process_code(code_input):
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documentation = generate_documentation(code_input)
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readme_file = io.BytesIO()
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readme_file.write(documentation.encode('utf-8'))
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readme_file.seek(0)
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return documentation, readme_file
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# Set up the Gradio app with Bootstrap, icons, and smiley
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with gr.Blocks(css=".container { font-family: 'Roboto', sans-serif; } .btn-primary { background-color: #007bff; } .icon { margin-right: 10px; }") as app:
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generate_button = gr.Button("Generate Documentation", elem_classes="btn btn-primary")
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with gr.Row():
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output_text = gr.Markdown(label="Generated Documentation")
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download_button = gr.File(label="Download README.md")
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# Bind function to button click
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generate_button.click(process_code, inputs=code_input, outputs=[output_text, download_button])
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# Launch the Gradio app
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app.launch()
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