Delete App_Env&CL.py

App_Env&CL.py

@@ -1,113 +0,0 @@
-import os
-import requests
-import gradio as gr
-import numpy as np
-import joblib
-from tensorflow.keras.models import load_model
-
-# URLs to your saved models and scaler
-scaler_url = 'https://huggingface.co/spaces/saba000farahani/Env_ContaminationLevel/blob/main/scaler_X.pkl'
-rf_model_url = 'https://huggingface.co/spaces/saba000farahani/Env_ContaminationLevel/blob/main/rf_model.pkl'
-mlp_model_url = 'https://huggingface.co/spaces/saba000farahani/Env_ContaminationLevel/blob/main/mlp_model.h5'
-meta_model_url = 'https://huggingface.co/spaces/saba000farahani/Env_ContaminationLevel/blob/main/meta_model.pkl'
-
-# Paths to save the downloaded models
-scaler_path = 'scaler_X.pkl'
-rf_model_path = 'rf_model.pkl'
-mlp_model_path = 'mlp_model.h5'
-meta_model_path = 'meta_model.pkl'
-
-def download_model(url, path):
-    if not os.path.exists(path):
-        print(f"Downloading {path}...")
-        response = requests.get(url)
-        with open(path, 'wb') as f:
-            f.write(response.content)
-        print(f"Downloaded {path}.")
-    else:
-        print(f"{path} already exists.")
-
-# Download the models and scaler if they don't exist locally
-download_model(scaler_url, scaler_path)
-download_model(rf_model_url, rf_model_path)
-download_model(mlp_model_url, mlp_model_path)
-download_model(meta_model_url, meta_model_path)
-
-try:
-    # Load the scaler and models
-    scaler_X = joblib.load(scaler_path)
-    loaded_rf_model = joblib.load(rf_model_path)
-    loaded_mlp_model = load_model(mlp_model_path)
-    loaded_meta_model = joblib.load(meta_model_path)
-    print("Models and scaler loaded successfully.")
-except Exception as e:
-    print(f"Error loading models or scaler: {e}")
-
-def predict_new_values(new_input_data):
-    try:
-        # Ensure the new input data is in the correct format
-        print(f"Raw Input Data: {new_input_data}")
-        new_input_data = np.array(new_input_data).reshape(1, -1)
-        
-        # Scale the new input data
-        new_input_scaled = scaler_X.transform(new_input_data)
-        print(f"Scaled Input Data: {new_input_scaled}")
-        
-        # Make predictions with both base models
-        mlp_predictions_new = loaded_mlp_model.predict(new_input_scaled)
-        rf_predictions_new = loaded_rf_model.predict(new_input_scaled)
-        
-        # Combine the predictions
-        combined_features_new = np.concatenate([mlp_predictions_new, rf_predictions_new], axis=1)
-        print(f"Combined Features: {combined_features_new}")
-        
-        # Use the loaded meta model to make predictions on the new data
-        loaded_meta_predictions_new = loaded_meta_model.predict(combined_features_new)
-        print(f"Meta Model Predictions: {loaded_meta_predictions_new}")
-        
-        return loaded_meta_predictions_new[0]
-    except Exception as e:
-        print(f"Error in prediction: {e}")
-        return ["Error", "Error", "Error", "Error", "Error", "Error"]
-
-def gradio_interface(velocity, temperature, precipitation, humidity):
-    try:
-        input_data = [velocity, temperature, precipitation, humidity]
-        print(f"Input Data: {input_data}")
-        predictions = predict_new_values(input_data)
-        print(f"Predictions: {predictions}")
-        return [
-            f"{predictions[0] * 100:.2f}%" if predictions[0] != "Error" else "Error",
-            f"{predictions[1] * 100:.2f}%" if predictions[1] != "Error" else "Error",
-            f"{predictions[2] * 100:.2f}%" if predictions[2] != "Error" else "Error",
-            f"{predictions[3] * 100:.2f}%" if predictions[3] != "Error" else "Error",
-            f"{predictions[4] * 100:.2f}%" if predictions[4] != "Error" else "Error",
-            f"{predictions[5] * 100:.2f}%" if predictions[5] != "Error" else "Error"
-        ]
-    except Exception as e:
-        print(f"Error in Gradio interface: {e}")
-        return ["Error", "Error", "Error", "Error", "Error", "Error"]
-
-inputs = [
-    gr.Slider(minimum=0, maximum=100, value=50,step=0.5, label="Velocity (mph)"),
-    gr.Slider(minimum=-30, maximum=50, value=0,step=0.5, label="Temperature (°C)"),
-    gr.Slider(minimum=0, maximum=10, value=0,step=0.01, label="Precipitation (inch)"),
-    gr.Slider(minimum=0, maximum=100, value=50, label="Humidity (%)")
-]
-
-outputs = [
-    gr.Textbox(label="Front Left"),
-    gr.Textbox(label="Front Right"),
-    gr.Textbox(label="Left"),
-    gr.Textbox(label="Right"),
-    gr.Textbox(label="Roof"),
-    gr.Textbox(label="Rear")
-]
-
-gr.Interface(
-    fn=gradio_interface, 
-    inputs=inputs, 
-    outputs=outputs, 
-    title="Environmental Factor-Based Contamination Level Prediction",
-    description="Enter the environmental factors to get the contamination levels for Front Left, Front Right, Left, Right, Roof, and Rear LiDARs."
-).launch()