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import gradio as gr | |
from huggingface_hub import hf_hub_download | |
import pickle | |
import gradio as gr | |
import numpy as np | |
import subprocess | |
import shutil | |
# Define the function to process the input file and model selection | |
def process_file(file, model_name): | |
with open(file.name, 'r') as f: | |
content = f.read() | |
saved_test_dataset = "test.txt" | |
saved_test_label = "saved_test_label.txt" | |
# Save the uploaded file content to a specified location | |
shutil.copyfile(file.name, saved_test_dataset) | |
# For demonstration purposes, we'll just return the content with the selected model name | |
subprocess.run(["python", "src/test_saved_model.py"]) | |
return f"Model: {model_name}\nContent:\n{content}" | |
# List of models for the dropdown menu | |
models = ["Model A", "Model B", "Model C"] | |
# Create the Gradio interface | |
with gr.Blocks() as demo: | |
gr.Markdown("# File Processor with Model Selection") | |
gr.Markdown("Upload a .txt file and select a model from the dropdown menu.") | |
with gr.Row(): | |
file_input = gr.File(label="Upload a .txt file", file_types=['.txt']) | |
model_dropdown = gr.Dropdown(choices=models, label="Select a model") | |
output_text = gr.Textbox(label="Output") | |
btn = gr.Button("Submit") | |
btn.click(fn=process_file, inputs=[file_input, model_dropdown], outputs=output_text) | |
# Launch the app | |
demo.launch() | |