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import gradio as gr
import tensorflow as tf
import joblib
import numpy as np
import zipfile
import os
import re
# Step 1: Unzip models only once
unzip_dir = "unzipped_models"
zip_file = "Models.zip" # Make sure this is the filename inside your Space repo
if not os.path.exists(unzip_dir):
print("Extracting model zip file...")
with zipfile.ZipFile(zip_file, 'r') as zip_ref:
zip_ref.extractall(unzip_dir)
print("Extraction complete.")
# Step 2: Parse folders to dynamically populate dropdowns
activations = []
seeds_dict = dict() # activation -> list of seeds
neurons_dict = dict() # (activation, seed) -> list of neuron counts
for act in os.listdir(os.path.join(unzip_dir, 'Models')):
act_path = os.path.join(os.path.join(unzip_dir, 'Models'), act)
if os.path.isdir(act_path) and not act.startswith("linear_models"):
activations.append(act)
seeds = []
for seed_folder in os.listdir(act_path):
seed_path = os.path.join(act_path, seed_folder)
if os.path.isdir(seed_path):
seeds.append(seed_folder)
neuron_list = []
for model_file in os.listdir(seed_path):
match = re.match(r"model_(\d+)\.keras", model_file)
if match:
neuron_list.append(int(match.group(1)))
neurons_dict[(act, seed_folder)] = sorted(neuron_list)
seeds_dict[act] = sorted(seeds)
activations = sorted(activations)
# Step 3: Load linear models
# linear_rgb_path = os.path.join(unzip_dir, "linear_models/linear_rgb.joblib")
# linear_grey_path = os.path.join(unzip_dir, "linear_models/linear_grey.joblib")
# linear_rgb = joblib.load(linear_rgb_path)
# linear_grey = joblib.load(linear_grey_path)
# Step 4: Prediction function
def predict(r, g, b, activation, seed, neurons):
try:
X = np.array([[r, g, b]])
# Linear predictions
lin_pred_rgb = (1.9221 * r) - (1.3817 * g) + (1.4058 * b) - 0.1318
# ANN prediction
keras_path = os.path.join(unzip_dir, activation, seed, f"model_{neurons}.keras")
if not os.path.exists(keras_path):
raise FileNotFoundError(f"Model not found: {keras_path}")
model = tf.keras.models.load_model(keras_path)
ann_pred = model.predict(X)[0][0]
return ann_pred, lin_pred_rgb
except Exception as e:
return f"Error: {str(e)}", "", ""
# Dynamic components for UI
def update_seeds(activation):
return gr.Dropdown.update(choices=seeds_dict[activation], value=seeds_dict[activation][0])
def update_neurons(activation, seed):
neurons = neurons_dict[(activation, seed)]
return gr.Dropdown.update(choices=neurons, value=neurons[0])
# Gradio Interface
with gr.Blocks() as demo:
gr.Markdown("# ANN vs Linear Model Predictor")
gr.Markdown("Dynamically select models and predict cholesterol concentration.")
with gr.Row():
r = gr.Number(label="R")
g = gr.Number(label="G")
b = gr.Number(label="B")
with gr.Row():
activation = gr.Dropdown(choices=activations, label="Activation Function")
seed = gr.Dropdown(label="Seed")
neurons = gr.Dropdown(label="Neurons")
activation.change(update_seeds, inputs=[activation], outputs=[seed])
seed.change(update_neurons, inputs=[activation, seed], outputs=[neurons])
with gr.Row():
btn = gr.Button("Predict")
with gr.Row():
ann_output = gr.Text(label="ANN Model Prediction")
lin_rgb_output = gr.Text(label="Linear RGB Prediction")
# lin_grey_output = gr.Text(label="Linear Grey Prediction")
btn.click(
fn=predict,
inputs=[r, g, b, activation, seed, neurons],
outputs=[ann_output, lin_rgb_output]
)
if __name__ == "__main__":
demo.launch()
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