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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "code",
"source": [
"pip install gradio"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "bk0XfjBP09aJ",
"outputId": "4134f0d7-5fe6-4cac-c092-04d128a9fb17"
},
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting gradio\n",
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"Building wheels for collected packages: ffmpy\n",
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" Created wheel for ffmpy: filename=ffmpy-0.3.2-py3-none-any.whl size=5584 sha256=f3f29dcbec7250e84a6a6df6492b6bd75d1cd20c812b8be3a090af573d31e368\n",
" Stored in directory: /root/.cache/pip/wheels/bd/65/9a/671fc6dcde07d4418df0c592f8df512b26d7a0029c2a23dd81\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",
" Attempting uninstall: altair\n",
" Found existing installation: altair 4.2.2\n",
" Uninstalling altair-4.2.2:\n",
" Successfully uninstalled altair-4.2.2\n",
"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"
]
}
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "P3FcIUqkzVTW",
"outputId": "58ce4065-4295-4648-f111-42b02261c6b4"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Downloading scaler_X.pkl...\n",
"Downloaded scaler_X.pkl.\n",
"Downloading rf_model.pkl...\n",
"Downloaded rf_model.pkl.\n",
"Downloading mlp_model.h5...\n",
"Downloaded mlp_model.h5.\n",
"Downloading meta_model.pkl...\n",
"Downloaded meta_model.pkl.\n",
"Error loading models or scaler: 60\n"
]
}
],
"source": [
"\n",
"import os\n",
"import requests\n",
"import gradio as gr\n",
"import numpy as np\n",
"import joblib\n",
"from tensorflow.keras.models import load_model\n",
"\n",
"# URLs to your saved models and scaler\n",
"scaler_url = 'https://huggingface.co/spaces/saba000farahani/Env_ContaminationLevel/blob/main/scaler_X.pkl'\n",
"rf_model_url = 'https://huggingface.co/spaces/saba000farahani/Env_ContaminationLevel/blob/main/rf_model.pkl'\n",
"mlp_model_url = 'https://huggingface.co/spaces/saba000farahani/Env_ContaminationLevel/blob/main/mlp_model.h5'\n",
"meta_model_url = 'https://huggingface.co/spaces/saba000farahani/Env_ContaminationLevel/blob/main/meta_model.pkl'\n",
"\n",
"# Paths to save the downloaded models\n",
"scaler_path = 'scaler_X.pkl'\n",
"rf_model_path = 'rf_model.pkl'\n",
"mlp_model_path = 'mlp_model.h5'\n",
"meta_model_path = 'meta_model.pkl'\n",
"\n",
"def download_model(url, path):\n",
" if not os.path.exists(path):\n",
" print(f\"Downloading {path}...\")\n",
" response = requests.get(url)\n",
" with open(path, 'wb') as f:\n",
" f.write(response.content)\n",
" print(f\"Downloaded {path}.\")\n",
" else:\n",
" print(f\"{path} already exists.\")\n",
"\n",
"# Download the models and scaler if they don't exist locally\n",
"download_model(scaler_url, scaler_path)\n",
"download_model(rf_model_url, rf_model_path)\n",
"download_model(mlp_model_url, mlp_model_path)\n",
"download_model(meta_model_url, meta_model_path)\n",
"\n",
"try:\n",
" # Load the scaler and models\n",
" scaler_X = joblib.load(scaler_path)\n",
" loaded_rf_model = joblib.load(rf_model_path)\n",
" loaded_mlp_model = load_model(mlp_model_path)\n",
" loaded_meta_model = joblib.load(meta_model_path)\n",
" print(\"Models and scaler loaded successfully.\")\n",
"except Exception as e:\n",
" print(f\"Error loading models or scaler: {e}\")\n",
"\n",
"def predict_new_values(new_input_data):\n",
" try:\n",
" # Ensure the new input data is in the correct format\n",
" print(f\"Raw Input Data: {new_input_data}\")\n",
" new_input_data = np.array(new_input_data).reshape(1, -1)\n",
"\n",
" # Scale the new input data\n",
" new_input_scaled = scaler_X.transform(new_input_data)\n",
" print(f\"Scaled Input Data: {new_input_scaled}\")\n",
"\n",
" # Make predictions with both base models\n",
" mlp_predictions_new = loaded_mlp_model.predict(new_input_scaled)\n",
" rf_predictions_new = loaded_rf_model.predict(new_input_scaled)\n",
"\n",
" # Combine the predictions\n",
" combined_features_new = np.concatenate([mlp_predictions_new, rf_predictions_new], axis=1)\n",
" print(f\"Combined Features: {combined_features_new}\")\n",
"\n",
" # Use the loaded meta model to make predictions on the new data\n",
" loaded_meta_predictions_new = loaded_meta_model.predict(combined_features_new)\n",
" print(f\"Meta Model Predictions: {loaded_meta_predictions_new}\")\n",
"\n",
" return loaded_meta_predictions_new[0]\n",
" except Exception as e:\n",
" print(f\"Error in prediction: {e}\")\n",
" return [\"Error\", \"Error\", \"Error\", \"Error\", \"Error\", \"Error\"]\n",
"\n",
"def gradio_interface(velocity, temperature, precipitation, humidity):\n",
" try:\n",
" input_data = [velocity, temperature, precipitation, humidity]\n",
" print(f\"Input Data: {input_data}\")\n",
" predictions = predict_new_values(input_data)\n",
" print(f\"Predictions: {predictions}\")\n",
" return [\n",
" f\"{predictions[0] * 100:.2f}%\" if predictions[0] != \"Error\" else \"Error\",\n",
" f\"{predictions[1] * 100:.2f}%\" if predictions[1] != \"Error\" else \"Error\",\n",
" f\"{predictions[2] * 100:.2f}%\" if predictions[2] != \"Error\" else \"Error\",\n",
" f\"{predictions[3] * 100:.2f}%\" if predictions[3] != \"Error\" else \"Error\",\n",
" f\"{predictions[4] * 100:.2f}%\" if predictions[4] != \"Error\" else \"Error\",\n",
" f\"{predictions[5] * 100:.2f}%\" if predictions[5] != \"Error\" else \"Error\"\n",
" ]\n",
" except Exception as e:\n",
" print(f\"Error in Gradio interface: {e}\")\n",
" return [\"Error\", \"Error\", \"Error\", \"Error\", \"Error\", \"Error\"]\n",
"\n",
"inputs = [\n",
" gr.Slider(minimum=0, maximum=100, value=50,step=0.5, label=\"Velocity (mph)\"),\n",
" gr.Slider(minimum=-30, maximum=50, value=0,step=0.5, label=\"Temperature (°C)\"),\n",
" gr.Slider(minimum=0, maximum=10, value=0,step=0.01, label=\"Precipitation (inch)\"),\n",
" gr.Slider(minimum=0, maximum=100, value=50, label=\"Humidity (%)\")\n",
"]\n",
"\n",
"outputs = [\n",
" gr.Textbox(label=\"Front Left\"),\n",
" gr.Textbox(label=\"Front Right\"),\n",
" gr.Textbox(label=\"Left\"),\n",
" gr.Textbox(label=\"Right\"),\n",
" gr.Textbox(label=\"Roof\"),\n",
" gr.Textbox(label=\"Rear\")\n",
"]\n",
"\n",
"gr.Interface(\n",
" fn=gradio_interface,\n",
" inputs=inputs,\n",
" outputs=outputs,\n",
" title=\"Environmental Factor-Based Contamination Level Prediction\",\n",
" description=\"Enter the environmental factors to get the contamination levels for Front Left, Front Right, Left, Right, Roof, and Rear LiDARs.\"\n",
").launch()\n"
]
}
]
} |