Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import joblib
+import numpy as np
+import zipfile
+import os
+from huggingface_hub import hf_hub_download
+
+# Hugging Face repository ID
+repo_id = "CaxtonEmeraldS/CholesterolConcentrationPredictor"  # Replace with your actual repo name
+
+# Unzip models only once
+unzip_dir = "unzipped_models"
+if not os.path.exists(unzip_dir):
+    print("Downloading and extracting model zip file...")
+    zip_path = hf_hub_download(repo_id=repo_id, filename="Models.zip")  # Replace with your actual uploaded ZIP filename
+    with zipfile.ZipFile(zip_path, 'r') as zip_ref:
+        zip_ref.extractall(unzip_dir)
+    print("Extraction complete.")
+
+# 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)
+
+def predict(r, g, b, activation, seed, neurons):
+    try:
+        X = np.array([[r, g, b]])
+        # grey = 0.2989 * r + 0.5870 * g + 0.1140 * b
+
+        # # Linear predictions
+        # lin_pred_rgb = linear_rgb.predict(X)[0]
+        # lin_pred_grey = linear_grey.predict([[grey]])[0]
+
+        # Load corresponding ANN model
+        keras_path = os.path.join(unzip_dir, f"{activation}/seed_{seed}/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, lin_pred_grey
+    
+    except Exception as e:
+        return f"Error: {str(e)}", "", ""
+
+iface = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Number(label="R"), 
+        gr.Number(label="G"), 
+        gr.Number(label="B"),
+        gr.Textbox(label="Activation (folder name)"),
+        gr.Number(label="Seed (folder name)"),
+        gr.Number(label="Neurons (model number)")
+    ],
+    outputs=[
+        gr.Text(label="ANN Model Prediction"),
+        gr.Text(label="Linear RGB Prediction"),
+        gr.Text(label="Linear Grey Prediction"),
+    ],
+    title="ANN vs Linear Model Predictor",
+    description="Dynamically load models from Hugging Face repo and predict."
+)
+
+if __name__ == "__main__":
+    iface.launch()