Upload App_Env&CL.ipynb
Browse files- App_Env&CL.ipynb +309 -0
App_Env&CL.ipynb
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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"provenance": []
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"language_info": {
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"name": "python"
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}
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},
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"cells": [
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{
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"cell_type": "code",
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"source": [
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"pip install gradio"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "bk0XfjBP09aJ",
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"outputId": "4134f0d7-5fe6-4cac-c092-04d128a9fb17"
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},
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"execution_count": 4,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Collecting gradio\n",
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" Downloading gradio-4.38.1-py3-none-any.whl (12.4 MB)\n",
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"\u001b[?25hCollecting aiofiles<24.0,>=22.0 (from gradio)\n",
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" Downloading aiofiles-23.2.1-py3-none-any.whl (15 kB)\n",
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"Collecting altair<6.0,>=5.0 (from gradio)\n",
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"\u001b[?25hCollecting fastapi (from gradio)\n",
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" Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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"\u001b[?25hCollecting httpx>=0.24.1 (from gradio)\n",
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"\u001b[?25hCollecting semantic-version~=2.0 (from gradio)\n",
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{
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "P3FcIUqkzVTW",
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"outputId": "58ce4065-4295-4648-f111-42b02261c6b4"
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Downloading scaler_X.pkl...\n",
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"Downloaded scaler_X.pkl.\n",
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"Downloading rf_model.pkl...\n",
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"Downloaded rf_model.pkl.\n",
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"Downloading mlp_model.h5...\n",
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"Downloaded mlp_model.h5.\n",
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"Downloading meta_model.pkl...\n",
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"Downloaded meta_model.pkl.\n",
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"Error loading models or scaler: 60\n"
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]
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}
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],
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"source": [
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"\n",
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| 193 |
+
"import os\n",
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| 194 |
+
"import requests\n",
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| 195 |
+
"import gradio as gr\n",
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| 196 |
+
"import numpy as np\n",
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| 197 |
+
"import joblib\n",
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| 198 |
+
"from tensorflow.keras.models import load_model\n",
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| 199 |
+
"\n",
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| 200 |
+
"# URLs to your saved models and scaler\n",
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| 201 |
+
"scaler_url = 'https://huggingface.co/spaces/saba000farahani/Env_ContaminationLevel/blob/main/scaler_X.pkl'\n",
|
| 202 |
+
"rf_model_url = 'https://huggingface.co/spaces/saba000farahani/Env_ContaminationLevel/blob/main/rf_model.pkl'\n",
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| 203 |
+
"mlp_model_url = 'https://huggingface.co/spaces/saba000farahani/Env_ContaminationLevel/blob/main/mlp_model.h5'\n",
|
| 204 |
+
"meta_model_url = 'https://huggingface.co/spaces/saba000farahani/Env_ContaminationLevel/blob/main/meta_model.pkl'\n",
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+
"\n",
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| 206 |
+
"# Paths to save the downloaded models\n",
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| 207 |
+
"scaler_path = 'scaler_X.pkl'\n",
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| 208 |
+
"rf_model_path = 'rf_model.pkl'\n",
|
| 209 |
+
"mlp_model_path = 'mlp_model.h5'\n",
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| 210 |
+
"meta_model_path = 'meta_model.pkl'\n",
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| 211 |
+
"\n",
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| 212 |
+
"def download_model(url, path):\n",
|
| 213 |
+
" if not os.path.exists(path):\n",
|
| 214 |
+
" print(f\"Downloading {path}...\")\n",
|
| 215 |
+
" response = requests.get(url)\n",
|
| 216 |
+
" with open(path, 'wb') as f:\n",
|
| 217 |
+
" f.write(response.content)\n",
|
| 218 |
+
" print(f\"Downloaded {path}.\")\n",
|
| 219 |
+
" else:\n",
|
| 220 |
+
" print(f\"{path} already exists.\")\n",
|
| 221 |
+
"\n",
|
| 222 |
+
"# Download the models and scaler if they don't exist locally\n",
|
| 223 |
+
"download_model(scaler_url, scaler_path)\n",
|
| 224 |
+
"download_model(rf_model_url, rf_model_path)\n",
|
| 225 |
+
"download_model(mlp_model_url, mlp_model_path)\n",
|
| 226 |
+
"download_model(meta_model_url, meta_model_path)\n",
|
| 227 |
+
"\n",
|
| 228 |
+
"try:\n",
|
| 229 |
+
" # Load the scaler and models\n",
|
| 230 |
+
" scaler_X = joblib.load(scaler_path)\n",
|
| 231 |
+
" loaded_rf_model = joblib.load(rf_model_path)\n",
|
| 232 |
+
" loaded_mlp_model = load_model(mlp_model_path)\n",
|
| 233 |
+
" loaded_meta_model = joblib.load(meta_model_path)\n",
|
| 234 |
+
" print(\"Models and scaler loaded successfully.\")\n",
|
| 235 |
+
"except Exception as e:\n",
|
| 236 |
+
" print(f\"Error loading models or scaler: {e}\")\n",
|
| 237 |
+
"\n",
|
| 238 |
+
"def predict_new_values(new_input_data):\n",
|
| 239 |
+
" try:\n",
|
| 240 |
+
" # Ensure the new input data is in the correct format\n",
|
| 241 |
+
" print(f\"Raw Input Data: {new_input_data}\")\n",
|
| 242 |
+
" new_input_data = np.array(new_input_data).reshape(1, -1)\n",
|
| 243 |
+
"\n",
|
| 244 |
+
" # Scale the new input data\n",
|
| 245 |
+
" new_input_scaled = scaler_X.transform(new_input_data)\n",
|
| 246 |
+
" print(f\"Scaled Input Data: {new_input_scaled}\")\n",
|
| 247 |
+
"\n",
|
| 248 |
+
" # Make predictions with both base models\n",
|
| 249 |
+
" mlp_predictions_new = loaded_mlp_model.predict(new_input_scaled)\n",
|
| 250 |
+
" rf_predictions_new = loaded_rf_model.predict(new_input_scaled)\n",
|
| 251 |
+
"\n",
|
| 252 |
+
" # Combine the predictions\n",
|
| 253 |
+
" combined_features_new = np.concatenate([mlp_predictions_new, rf_predictions_new], axis=1)\n",
|
| 254 |
+
" print(f\"Combined Features: {combined_features_new}\")\n",
|
| 255 |
+
"\n",
|
| 256 |
+
" # Use the loaded meta model to make predictions on the new data\n",
|
| 257 |
+
" loaded_meta_predictions_new = loaded_meta_model.predict(combined_features_new)\n",
|
| 258 |
+
" print(f\"Meta Model Predictions: {loaded_meta_predictions_new}\")\n",
|
| 259 |
+
"\n",
|
| 260 |
+
" return loaded_meta_predictions_new[0]\n",
|
| 261 |
+
" except Exception as e:\n",
|
| 262 |
+
" print(f\"Error in prediction: {e}\")\n",
|
| 263 |
+
" return [\"Error\", \"Error\", \"Error\", \"Error\", \"Error\", \"Error\"]\n",
|
| 264 |
+
"\n",
|
| 265 |
+
"def gradio_interface(velocity, temperature, precipitation, humidity):\n",
|
| 266 |
+
" try:\n",
|
| 267 |
+
" input_data = [velocity, temperature, precipitation, humidity]\n",
|
| 268 |
+
" print(f\"Input Data: {input_data}\")\n",
|
| 269 |
+
" predictions = predict_new_values(input_data)\n",
|
| 270 |
+
" print(f\"Predictions: {predictions}\")\n",
|
| 271 |
+
" return [\n",
|
| 272 |
+
" f\"{predictions[0] * 100:.2f}%\" if predictions[0] != \"Error\" else \"Error\",\n",
|
| 273 |
+
" f\"{predictions[1] * 100:.2f}%\" if predictions[1] != \"Error\" else \"Error\",\n",
|
| 274 |
+
" f\"{predictions[2] * 100:.2f}%\" if predictions[2] != \"Error\" else \"Error\",\n",
|
| 275 |
+
" f\"{predictions[3] * 100:.2f}%\" if predictions[3] != \"Error\" else \"Error\",\n",
|
| 276 |
+
" f\"{predictions[4] * 100:.2f}%\" if predictions[4] != \"Error\" else \"Error\",\n",
|
| 277 |
+
" f\"{predictions[5] * 100:.2f}%\" if predictions[5] != \"Error\" else \"Error\"\n",
|
| 278 |
+
" ]\n",
|
| 279 |
+
" except Exception as e:\n",
|
| 280 |
+
" print(f\"Error in Gradio interface: {e}\")\n",
|
| 281 |
+
" return [\"Error\", \"Error\", \"Error\", \"Error\", \"Error\", \"Error\"]\n",
|
| 282 |
+
"\n",
|
| 283 |
+
"inputs = [\n",
|
| 284 |
+
" gr.Slider(minimum=0, maximum=100, value=50,step=0.5, label=\"Velocity (mph)\"),\n",
|
| 285 |
+
" gr.Slider(minimum=-30, maximum=50, value=0,step=0.5, label=\"Temperature (°C)\"),\n",
|
| 286 |
+
" gr.Slider(minimum=0, maximum=10, value=0,step=0.01, label=\"Precipitation (inch)\"),\n",
|
| 287 |
+
" gr.Slider(minimum=0, maximum=100, value=50, label=\"Humidity (%)\")\n",
|
| 288 |
+
"]\n",
|
| 289 |
+
"\n",
|
| 290 |
+
"outputs = [\n",
|
| 291 |
+
" gr.Textbox(label=\"Front Left\"),\n",
|
| 292 |
+
" gr.Textbox(label=\"Front Right\"),\n",
|
| 293 |
+
" gr.Textbox(label=\"Left\"),\n",
|
| 294 |
+
" gr.Textbox(label=\"Right\"),\n",
|
| 295 |
+
" gr.Textbox(label=\"Roof\"),\n",
|
| 296 |
+
" gr.Textbox(label=\"Rear\")\n",
|
| 297 |
+
"]\n",
|
| 298 |
+
"\n",
|
| 299 |
+
"gr.Interface(\n",
|
| 300 |
+
" fn=gradio_interface,\n",
|
| 301 |
+
" inputs=inputs,\n",
|
| 302 |
+
" outputs=outputs,\n",
|
| 303 |
+
" title=\"Environmental Factor-Based Contamination Level Prediction\",\n",
|
| 304 |
+
" description=\"Enter the environmental factors to get the contamination levels for Front Left, Front Right, Left, Right, Roof, and Rear LiDARs.\"\n",
|
| 305 |
+
").launch()\n"
|
| 306 |
+
]
|
| 307 |
+
}
|
| 308 |
+
]
|
| 309 |
+
}
|