Update app.py
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app.py
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import
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import tkinter as tk
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from tkinterdnd2 import TkinterDnD, DND_FILES
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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# Load the
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model_name = "
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model = AutoModelForCausalLM.from_pretrained(model_name)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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# Function to explain Java code using the LLM model
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def explain_code(code):
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inputs = tokenizer(code, return_tensors="pt")
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outputs = model.generate(inputs['input_ids'], max_length=500)
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explanation = tokenizer.decode(outputs[0], skip_special_tokens=True)
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return explanation
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#
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def
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# Walk through the directory and find all Java files
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for filename in os.listdir(folder_path):
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if filename.endswith(".java"):
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file_path = os.path.join(folder_path, filename)
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# Read Java file contents
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with open(file_path, 'r', encoding='utf-8') as file:
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java_code = file.read()
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# Get explanation from the model
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explanation = explain_code(java_code)
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explanations.append(f"Explanation for {filename}:\n{explanation}\n")
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return explanations
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#
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output_text.delete(1.0, tk.END) # Clear previous output
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output_text.insert(tk.END, "\n\n".join(explanations)) # Display new output
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#
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root.title("Java Code Explainer")
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root.geometry("800x600")
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# Set up the text widget for displaying explanations
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output_text = tk.Text(root, wrap=tk.WORD, width=90, height=25)
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output_text.pack(padx=10, pady=10)
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# Set up the label for dragging and dropping the folder
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drop_label = tk.Label(root, text="Drag and drop a folder containing Java files here", width=80, height=2)
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drop_label.pack(padx=10, pady=10)
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# Register the drop area for folders
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root.drop_target_register(DND_FILES)
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root.dnd_bind('<<Drop>>', on_drop)
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# Start the GUI event loop
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root.mainloop()
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import gradio as gr
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from transformers import AutoModelForCausalLM, AutoTokenizer
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# Load the model and tokenizer
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model_name = "codegen-350M-mono"
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model = AutoModelForCausalLM.from_pretrained(model_name)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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def explain_code(code):
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inputs = tokenizer(code, return_tensors="pt")
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outputs = model.generate(inputs['input_ids'], max_length=500)
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explanation = tokenizer.decode(outputs[0], skip_special_tokens=True)
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return explanation
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# Gradio interface for uploading files
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def process_uploaded_file(file):
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code = file.read().decode("utf-8")
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return explain_code(code)
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# Create the Gradio interface
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iface = gr.Interface(fn=process_uploaded_file,
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inputs=gr.File(label="Upload Java File"),
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outputs="text",
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live=True)
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# Launch the interface
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iface.launch()
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