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Delete App_Env&CL.ipynb

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  1. App_Env&CL.ipynb +0 -309
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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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- " Stored in directory: /root/.cache/pip/wheels/bd/65/9a/671fc6dcde07d4418df0c592f8df512b26d7a0029c2a23dd81\n",
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- "Successfully built ffmpy\n",
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- "Installing collected packages: pydub, ffmpy, websockets, uvloop, tomlkit, semantic-version, ruff, python-multipart, python-dotenv, orjson, httptools, h11, dnspython, aiofiles, watchfiles, uvicorn, starlette, httpcore, email_validator, httpx, gradio-client, fastapi-cli, altair, fastapi, gradio\n",
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- " Attempting uninstall: altair\n",
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- " Found existing installation: altair 4.2.2\n",
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- " Uninstalling altair-4.2.2:\n",
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- " Successfully uninstalled altair-4.2.2\n",
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- "Successfully installed aiofiles-23.2.1 altair-5.3.0 dnspython-2.6.1 email_validator-2.2.0 fastapi-0.111.1 fastapi-cli-0.0.4 ffmpy-0.3.2 gradio-4.38.1 gradio-client-1.1.0 h11-0.14.0 httpcore-1.0.5 httptools-0.6.1 httpx-0.27.0 orjson-3.10.6 pydub-0.25.1 python-dotenv-1.0.1 python-multipart-0.0.9 ruff-0.5.3 semantic-version-2.10.0 starlette-0.37.2 tomlkit-0.12.0 uvicorn-0.30.1 uvloop-0.19.0 watchfiles-0.22.0 websockets-11.0.3\n"
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- ]
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- }
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- ]
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- },
164
- {
165
- "cell_type": "code",
166
- "execution_count": null,
167
- "metadata": {
168
- "colab": {
169
- "base_uri": "https://localhost:8080/"
170
- },
171
- "id": "P3FcIUqkzVTW",
172
- "outputId": "58ce4065-4295-4648-f111-42b02261c6b4"
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- },
174
- "outputs": [
175
- {
176
- "output_type": "stream",
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- "name": "stdout",
178
- "text": [
179
- "Downloading scaler_X.pkl...\n",
180
- "Downloaded scaler_X.pkl.\n",
181
- "Downloading rf_model.pkl...\n",
182
- "Downloaded rf_model.pkl.\n",
183
- "Downloading mlp_model.h5...\n",
184
- "Downloaded mlp_model.h5.\n",
185
- "Downloading meta_model.pkl...\n",
186
- "Downloaded meta_model.pkl.\n",
187
- "Error loading models or scaler: 60\n"
188
- ]
189
- }
190
- ],
191
- "source": [
192
- "\n",
193
- "import os\n",
194
- "import requests\n",
195
- "import gradio as gr\n",
196
- "import numpy as np\n",
197
- "import joblib\n",
198
- "from tensorflow.keras.models import load_model\n",
199
- "\n",
200
- "# URLs to your saved models and scaler\n",
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",
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",
205
- "\n",
206
- "# Paths to save the downloaded models\n",
207
- "scaler_path = 'scaler_X.pkl'\n",
208
- "rf_model_path = 'rf_model.pkl'\n",
209
- "mlp_model_path = 'mlp_model.h5'\n",
210
- "meta_model_path = 'meta_model.pkl'\n",
211
- "\n",
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
- }