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+{"cells":[{"cell_type":"code","execution_count":1,"id":"5a748a06-f7b7-4862-8e53-1ad85de04892","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5a748a06-f7b7-4862-8e53-1ad85de04892","executionInfo":{"status":"ok","timestamp":1736724106593,"user_tz":0,"elapsed":14009,"user":{"displayName":"Cheikh mansour Sow","userId":"06761263153221193241"}},"outputId":"5cd35523-1f22-40c1-8386-ac3dcda753f4"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting gradio\n","  Downloading gradio-5.12.0-py3-none-any.whl.metadata (16 kB)\n","Collecting aiofiles<24.0,>=22.0 (from gradio)\n","  Downloading aiofiles-23.2.1-py3-none-any.whl.metadata (9.7 kB)\n","Requirement already satisfied: anyio<5.0,>=3.0 in /usr/local/lib/python3.10/dist-packages (from gradio) (3.7.1)\n","Collecting fastapi<1.0,>=0.115.2 (from gradio)\n","  Downloading fastapi-0.115.6-py3-none-any.whl.metadata (27 kB)\n","Collecting ffmpy (from gradio)\n","  Downloading ffmpy-0.5.0-py3-none-any.whl.metadata 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uvicorn-0.34.0\n"]}],"source":["pip install gradio"]},{"cell_type":"code","source":["import gradio as gr\n","import pandas as pd\n","import joblib\n","\n","# Charger le modèle, le scaler et les encodeurs\n","model = joblib.load(\"/content/drive/MyDrive/ML 2/linear_regression_model.pkl\")\n","scaler = joblib.load(\"/content/drive/MyDrive/ML 2/scaler.pkl\")\n","encoders = joblib.load(\"/content/drive/MyDrive/ML 2/encoders.pkl\")\n","\n","def predict_single(Kms_Driven, Present_Price, Fuel_Type, Seller_Type, Transmission, Age):\n","    \"\"\"\n","    Fonction pour la prédiction d'un seul véhicule.\n","    \"\"\"\n","    try:\n","        # Préparer les données d'entrée\n","        input_data = pd.DataFrame({\n","            \"Kms_Driven\": [Kms_Driven],\n","            \"Present_Price\": [Present_Price],\n","            \"Fuel_Type\": [Fuel_Type],\n","            \"Seller_Type\": [Seller_Type],\n","            \"Transmission\": [Transmission],\n","            \"Age\": [Age],\n","        })\n","\n","        # Encoder les variables catégorielles\n","        for col, encoder in encoders.items():\n","            input_data[col] = encoder.transform(input_data[col])\n","\n","        # Normaliser les données\n","        input_scaled = scaler.transform(input_data)\n","\n","        # Faire la prédiction\n","        prediction = model.predict(input_scaled)[0]\n","\n","        return round(prediction, 2)  # Retourne la prédiction arrondie\n","\n","    except Exception as e:\n","        return f\"Erreur : {e}\"\n","\n","\n","def predict_multiple(file):\n","    \"\"\"\n","    Fonction pour la prédiction de multiples véhicules à partir d'un fichier CSV.\n","    \"\"\"\n","    try:\n","        # Lire le fichier CSV\n","        data = pd.read_csv(file)\n","\n","        # Vérifier la structure du fichier\n","        required_columns = [\"Kms_Driven\", \"Present_Price\", \"Fuel_Type\", \"Seller_Type\", \"Transmission\", \"Age\"]\n","        for col in required_columns:\n","            if col not in data.columns:\n","                return f\"Le fichier doit contenir les colonnes suivantes : {required_columns}\"\n","\n","        # Encoder les colonnes catégoriques\n","        for col, encoder in encoders.items():\n","            data[col] = encoder.transform(data[col])\n","\n","        # Normaliser les données\n","        data_scaled = scaler.transform(data)\n","\n","        # Faire les prédictions\n","        predictions = model.predict(data_scaled)\n","        data[\"Predicted_Selling_Price\"] = predictions\n","\n","        return data\n","\n","    except Exception as e:\n","        return f\"Erreur : {e}\"\n","\n","\n","# Interface Gradio\n","with gr.Blocks() as app:\n","    gr.Markdown(\"# Application de Prédiction du Prix de Vente\")\n","\n","    gr.Markdown(\"## Instructions pour les colonnes catégoriques :\")\n","    gr.Markdown(\"\"\"\n","    - **Type de carburant (Fuel_Type)** :\n","        - Petrol = 0\n","        - Diesel = 1\n","        - CNG = 2\n","    - **Type de vendeur (Seller_Type)** :\n","        - Dealer = 0\n","        - Individual = 1\n","    - **Transmission** :\n","        - Manual = 0\n","        - Automatic = 1\n","    \"\"\")\n","\n","    # Section Prédiction Simple\n","    gr.Markdown(\"## Prédiction Simple\")\n","    with gr.Row():\n","        Kms_Driven = gr.Number(label=\"Kilomètres parcourus\")\n","        Present_Price = gr.Number(label=\"Prix actuel (en Lakhs)\")\n","        Fuel_Type = gr.Textbox(label=\"Type de carburant (Petrol, Diesel, CNG)\")\n","        Seller_Type = gr.Textbox(label=\"Type de vendeur (Dealer, Individual)\")\n","        Transmission = gr.Textbox(label=\"Transmission (Manual, Automatic)\")\n","        Age = gr.Number(label=\"Âge du véhicule (en années)\")\n","    predict_btn = gr.Button(\"Prédire\")\n","    output_simple = gr.Textbox(label=\"Prix de vente prédit\")\n","\n","    predict_btn.click(\n","        predict_single,\n","        inputs=[Kms_Driven, Present_Price, Fuel_Type, Seller_Type, Transmission, Age],\n","        outputs=output_simple,\n","    )\n","\n","    # Section Prédiction Multiple\n","    gr.Markdown(\"## Prédiction Multiple\")\n","    gr.Markdown(\"\"\"\n","    Chargez un fichier CSV contenant les colonnes suivantes :\n","    - Kms_Driven, Present_Price, Fuel_Type, Seller_Type, Transmission, Age\n","    \"\"\")\n","\n","    file_input = gr.File(label=\"Uploader un fichier CSV\")\n","    output_multiple = gr.Dataframe(label=\"Résultats avec Prédictions\")\n","\n","    file_input.change(\n","        predict_multiple,\n","        inputs=file_input,\n","        outputs=output_multiple,\n","    )\n","\n","app.launch()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":648},"id":"eOa3lAIu4XxB","executionInfo":{"status":"ok","timestamp":1736724407945,"user_tz":0,"elapsed":7451,"user":{"displayName":"Cheikh mansour Sow","userId":"06761263153221193241"}},"outputId":"bce9dacd-043c-4e8d-e64b-2420cc6917cc"},"id":"eOa3lAIu4XxB","execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["Running Gradio in a Colab notebook requires sharing enabled. Automatically setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n","\n","Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n","* Running on public URL: https://45049290bb1b4bcbd2.gradio.live\n","\n","This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["<div><iframe src=\"https://45049290bb1b4bcbd2.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":[]},"metadata":{},"execution_count":4}]},{"cell_type":"code","source":[],"metadata":{"id":"_PYNGDF10zUY"},"id":"_PYNGDF10zUY","execution_count":null,"outputs":[]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kN8nlvdV03iG","executionInfo":{"status":"ok","timestamp":1736724197537,"user_tz":0,"elapsed":36893,"user":{"displayName":"Cheikh mansour Sow","userId":"06761263153221193241"}},"outputId":"a8ba8770-705b-489a-a236-5a34737183cf"},"id":"kN8nlvdV03iG","execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["import gradio as gr\n","import pandas as pd\n","import joblib\n","\n","# Charger le modèle, le scaler et les encodeurs\n","model = joblib.load(\"/content/drive/MyDrive/ML 2/linear_regression_model.pkl\")\n","scaler = joblib.load(\"/content/drive/MyDrive/ML 2/scaler.pkl\")\n","encoders = joblib.load(\"/content/drive/MyDrive/ML 2/encoders.pkl\")\n","\n","\n","def validate_inputs(Kms_Driven, Present_Price, Age):\n","    \"\"\"Valider les entrées numériques pour éviter des erreurs.\"\"\"\n","    errors = []\n","    if Kms_Driven < 0:\n","        errors.append(\"Les kilomètres parcourus (Kms_Driven) ne peuvent pas être négatifs.\")\n","    if Present_Price < 0:\n","        errors.append(\"Le prix actuel (Present_Price) ne peut pas être négatif.\")\n","    if Age < 0:\n","        errors.append(\"L'âge du véhicule (Age) ne peut pas être négatif.\")\n","    return errors\n","\n","\n","def predict_single(Kms_Driven, Present_Price, Fuel_Type, Seller_Type, Transmission, Age):\n","    try:\n","        # Valider les entrées\n","        errors = validate_inputs(Kms_Driven, Present_Price, Age)\n","        if errors:\n","            return \"\\n\".join(errors)\n","\n","        # Préparer les données d'entrée\n","        input_data = pd.DataFrame({\n","            \"Kms_Driven\": [Kms_Driven],\n","            \"Present_Price\": [Present_Price],\n","            \"Fuel_Type\": [Fuel_Type],\n","            \"Seller_Type\": [Seller_Type],\n","            \"Transmission\": [Transmission],\n","            \"Age\": [Age],\n","        })\n","\n","        # Encoder les variables catégoriques\n","        for col, encoder in encoders.items():\n","            input_data[col] = encoder.transform(input_data[col])\n","\n","        # Normaliser les données\n","        input_scaled = scaler.transform(input_data)\n","\n","        # Faire la prédiction\n","        prediction = model.predict(input_scaled)[0]\n","        return f\"Prix de vente prédit : {round(prediction, 2)} A\"\n","    except Exception as e:\n","        return f\"Erreur : {e}\"\n","\n","\n","def predict_multiple(file):\n","    try:\n","        # Lire le fichier CSV\n","        data = pd.read_csv(file)\n","\n","        # Vérifier la structure du fichier\n","        required_columns = [\"Kms_Driven\", \"Present_Price\", \"Fuel_Type\", \"Seller_Type\", \"Transmission\", \"Age\"]\n","        for col in required_columns:\n","            if col not in data.columns:\n","                return f\"Le fichier doit contenir les colonnes suivantes : {required_columns}\"\n","\n","        # Encoder les colonnes catégoriques\n","        for col, encoder in encoders.items():\n","            data[col] = encoder.transform(data[col])\n","\n","        # Normaliser les données\n","        data_scaled = scaler.transform(data)\n","\n","        # Faire les prédictions\n","        predictions = model.predict(data_scaled)\n","        data[\"Predict_Price\"] = predictions\n","\n","        return data\n","    except Exception as e:\n","        return f\"Erreur : {e}\"\n","\n","\n","# Interface Gradio\n","with gr.Blocks(theme=gr.themes.Monochrome()) as app:\n","    gr.Markdown(\n","        \"\"\"\n","        # 🏎️ Application de Prédiction du Prix de Vente\n","        ## Cheikh Mansour Sow\n","\n","        Prédisez le prix de vente de votre véhicule d'occasion rapidement et facilement !\n","        \"\"\"\n","    )\n","\n","    # Section Prédiction Simple\n","    with gr.Tab(\"Prédiction Simple\"):\n","        gr.Markdown(\"## Remplissez les informations de votre véhicule :\")\n","        with gr.Row():\n","            Kms_Driven = gr.Number(label=\"Kilomètres parcourus\")\n","            Present_Price = gr.Number(label=\"Prix actuel (en Lakhs)\")\n","            Fuel_Type = gr.Dropdown(\n","                label=\"Type de carburant\",\n","                choices=[\"Petrol\", \"Diesel\", \"CNG\"]\n","            )\n","            Seller_Type = gr.Dropdown(\n","                label=\"Type de vendeur\",\n","                choices=[\"Dealer\", \"Individual\"]\n","            )\n","            Transmission = gr.Dropdown(\n","                label=\"Transmission\",\n","                choices=[\"Manual\", \"Automatic\"]\n","            )\n","            Age = gr.Number(label=\"Âge du véhicule\")\n","\n","        predict_btn = gr.Button(\"Prédire\", variant=\"primary\")\n","        output_simple = gr.Textbox(label=\"Résultat\")\n","\n","        predict_btn.click(\n","            predict_single,\n","            inputs=[Kms_Driven, Present_Price, Fuel_Type, Seller_Type, Transmission, Age],\n","            outputs=output_simple,\n","        )\n","\n","    # Section Prédiction Multiple\n","    with gr.Tab(\"Prédiction Multiple\"):\n","        gr.Markdown(\"## Uploadez un fichier CSV avec les colonnes suivantes :\")\n","        gr.Markdown(\"- `Kms_Driven`, `Present_Price`, `Fuel_Type`, `Seller_Type`, `Transmission`, `Age`\")\n","\n","        file_input = gr.File(label=\"Uploader un fichier CSV\")\n","        output_multiple = gr.Dataframe(label=\"Résultats avec Prédictions\")\n","\n","        file_input.change(\n","            predict_multiple,\n","            inputs=file_input,\n","            outputs=output_multiple,\n","        )\n","\n","# Lancer l'application\n","app.launch()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":648},"id":"BUTIH41-057A","executionInfo":{"status":"ok","timestamp":1736724997631,"user_tz":0,"elapsed":4193,"user":{"displayName":"Cheikh mansour Sow","userId":"06761263153221193241"}},"outputId":"57cb964d-a8c9-463f-8a6f-d5400e09461e"},"id":"BUTIH41-057A","execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["Running Gradio in a Colab notebook requires sharing enabled. Automatically setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n","\n","Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n","* Running on public URL: https://93dabdc570dcdbfabe.gradio.live\n","\n","This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["<div><iframe src=\"https://93dabdc570dcdbfabe.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":[]},"metadata":{},"execution_count":5}]},{"cell_type":"code","source":["import gradio as gr\n","import pandas as pd\n","import joblib\n","\n","# Charger le modèle, le scaler et les encodeurs\n","model = joblib.load(\"/content/drive/MyDrive/ML 2/linear_regression_model.pkl\")\n","scaler = joblib.load(\"/content/drive/MyDrive/ML 2/scaler.pkl\")\n","encoders = joblib.load(\"/content/drive/MyDrive/ML 2/encoders.pkl\")\n","\n","\n","def validate_inputs(Kms_Driven, Present_Price, Age):\n","    \"\"\"Valider les entrées numériques pour éviter des erreurs.\"\"\"\n","    errors = []\n","    if Kms_Driven < 0:\n","        errors.append(\"⚠️ Les kilomètres parcourus (Kms_Driven) ne peuvent pas être négatifs.\")\n","    if Present_Price < 0:\n","        errors.append(\"⚠️ Le prix actuel (Present_Price) ne peut pas être négatif.\")\n","    if Age < 0:\n","        errors.append(\"⚠️ L'âge du véhicule (Age) ne peut pas être négatif.\")\n","    return errors\n","\n","\n","def predict_single(Kms_Driven, Present_Price, Fuel_Type, Seller_Type, Transmission, Age):\n","    try:\n","        # Valider les entrées\n","        errors = validate_inputs(Kms_Driven, Present_Price, Age)\n","        if errors:\n","            return \"\\n\".join(errors)  # Affiche les erreurs si elles existent\n","\n","        # Préparer les données d'entrée\n","        input_data = pd.DataFrame({\n","            \"Kms_Driven\": [Kms_Driven],\n","            \"Present_Price\": [Present_Price],\n","            \"Fuel_Type\": [Fuel_Type],\n","            \"Seller_Type\": [Seller_Type],\n","            \"Transmission\": [Transmission],\n","            \"Age\": [Age],\n","        })\n","\n","        # Encoder les variables catégoriques\n","        for col, encoder in encoders.items():\n","            input_data[col] = encoder.transform(input_data[col])\n","\n","        # Normaliser les données\n","        input_scaled = scaler.transform(input_data)\n","\n","        # Faire la prédiction\n","        prediction = model.predict(input_scaled)[0]\n","        return f\"✅ Prix de vente prédit : {round(prediction, 2)} \"\n","    except Exception as e:\n","        return f\"Erreur : {e}\"\n","\n","\n","def predict_multiple(file):\n","    try:\n","        # Lire le fichier CSV\n","        data = pd.read_csv(file)\n","\n","        # Vérifier la structure du fichier\n","        required_columns = [\"Kms_Driven\", \"Present_Price\", \"Fuel_Type\", \"Seller_Type\", \"Transmission\", \"Age\"]\n","        for col in required_columns:\n","            if col not in data.columns:\n","                return f\"Le fichier doit contenir les colonnes suivantes : {required_columns}\"\n","\n","        # Encoder les colonnes catégoriques\n","        for col, encoder in encoders.items():\n","            data[col] = encoder.transform(data[col])\n","\n","        # Normaliser les données\n","        data_scaled = scaler.transform(data)\n","\n","        # Faire les prédictions\n","        predictions = model.predict(data_scaled)\n","        data[\"Predict_Price\"] = predictions\n","\n","        return data\n","    except Exception as e:\n","        return f\"Erreur : {e}\"\n","\n","\n","# Interface Gradio\n","with gr.Blocks(theme=gr.themes.Monochrome()) as app:\n","    gr.Markdown(\n","        \"\"\"\n","        # 🏎️ Application de Prédiction du Prix de Vente\n","        ## Cheikh Mansour Sow\n","\n","        Prédisez le prix de vente de votre véhicule d'occasion rapidement et facilement !\n","        \"\"\"\n","    )\n","\n","    # Section Prédiction Simple\n","    with gr.Tab(\"Prédiction Simple\"):\n","        gr.Markdown(\"## Remplissez les informations de votre véhicule :\")\n","        with gr.Row():\n","            Kms_Driven = gr.Number(label=\"Kilomètres parcourus\")\n","            Present_Price = gr.Number(label=\"Prix actuel \")\n","            Fuel_Type = gr.Dropdown(\n","                label=\"Type de carburant\",\n","                choices=[\"Petrol\", \"Diesel\", \"CNG\"]\n","            )\n","            Seller_Type = gr.Dropdown(\n","                label=\"Type de vendeur\",\n","                choices=[\"Dealer\", \"Individual\"]\n","            )\n","            Transmission = gr.Dropdown(\n","                label=\"Transmission\",\n","                choices=[\"Manual\", \"Automatic\"]\n","            )\n","            Age = gr.Number(label=\"Âge du véhicule\")\n","\n","        predict_btn = gr.Button(\"Prédire\", variant=\"primary\")\n","        output_simple = gr.Textbox(label=\"Résultat\")\n","\n","        predict_btn.click(\n","            predict_single,\n","            inputs=[Kms_Driven, Present_Price, Fuel_Type, Seller_Type, Transmission, Age],\n","            outputs=output_simple,\n","        )\n","\n","    # Section Prédiction Multiple\n","    with gr.Tab(\"Prédiction Multiple\"):\n","        gr.Markdown(\"## Uploadez un fichier CSV avec les colonnes suivantes :\")\n","        gr.Markdown(\"- `Kms_Driven` - `Present_Price` - `Fuel_Type` - `Seller_Type` - `Transmission` - `Age`\")\n","\n","        file_input = gr.File(label=\"Uploader un fichier CSV\")\n","        output_multiple = gr.Dataframe(label=\"Résultats avec Prédictions\")\n","\n","        file_input.change(\n","            predict_multiple,\n","            inputs=file_input,\n","            outputs=output_multiple,\n","        )\n","\n","# Lancer l'application\n","app.launch()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":648},"id":"RdW7Yu8T4FOm","executionInfo":{"status":"ok","timestamp":1736725716419,"user_tz":0,"elapsed":2831,"user":{"displayName":"Cheikh mansour Sow","userId":"06761263153221193241"}},"outputId":"9a248741-9ec8-4270-8ac6-873d5098d29d"},"id":"RdW7Yu8T4FOm","execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["Running Gradio in a Colab notebook requires sharing enabled. Automatically setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n","\n","Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n","* Running on public URL: https://53e4a64c6e25377d50.gradio.live\n","\n","This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["<div><iframe src=\"https://53e4a64c6e25377d50.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":[]},"metadata":{},"execution_count":8}]},{"cell_type":"code","source":[],"metadata":{"id":"R-Lppvwn6Fuk"},"id":"R-Lppvwn6Fuk","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.5"},"colab":{"provenance":[]}},"nbformat":4,"nbformat_minor":5}

encoders.pkl

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encoders_lr.pkl

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linear_regression_model.pkl

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scaler.pkl

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scaler_lr.pkl

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test.ipynb

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+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"mount_file_id":"1OTe5fLFOcKMl35flt1IZryI-TYh_Xfp5","authorship_tag":"ABX9TyMtd1Z/o84vVHvQ++o/uWbV"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","source":["pip install gradio"],"metadata":{"id":"5nu3RwfcmEx4"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from sklearn.linear_model import LinearRegression\n","from sklearn.model_selection import train_test_split\n","from sklearn.preprocessing import LabelEncoder, StandardScaler\n","import pandas as pd\n","import joblib\n","\n","# Charger les données d'entraînement\n","data = pd.read_csv(\"/content/drive/MyDrive/ML 2/Car_dataset.csv\")  # Adaptez le chemin\n","\n","# Encoder les variables catégorielles\n","encoders = {\n","    \"Fuel_Type\": LabelEncoder(),\n","    \"Seller_Type\": LabelEncoder(),\n","    \"Transmission\": LabelEncoder()\n","}\n","\n","for col, encoder in encoders.items():\n","    data[col] = encoder.fit_transform(data[col])\n","\n","# Séparer les caractéristiques et la cible\n","X = data[[\"Kms_Driven\", \"Present_Price\", \"Fuel_Type\", \"Seller_Type\", \"Transmission\", \"Age\"]]\n","y = data[\"Selling_Price\"]\n","\n","# Normaliser les données\n","scaler = StandardScaler()\n","X_scaled = scaler.fit_transform(X)\n","\n","# Diviser les données en jeu d'entraînement et de test\n","X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)\n","\n","# Entraîner un modèle de régression linéaire\n","model = LinearRegression()\n","model.fit(X_train, y_train)\n","\n","# Sauvegarder le modèle, le scaler et les encodeurs\n","joblib.dump(model, \"/content/drive/MyDrive/ML 2/linear_regression_model.pkl\")\n","joblib.dump(scaler, \"/content/drive/MyDrive/ML 2/scaler_lr.pkl\")\n","joblib.dump(encoders, \"/content/drive/MyDrive/ML 2/encoders_lr.pkl\")\n","\n","print(\"Modèle et preprocessors sauvegardés avec succès !\")\n","\n","# Tester les prédictions avec des valeurs extrêmes\n","test_cases = [\n","    {\"Kms_Driven\": 1000, \"Present_Price\": 20, \"Fuel_Type\": \"Petrol\", \"Seller_Type\": \"Dealer\", \"Transmission\": \"Manual\", \"Age\": 1},\n","    {\"Kms_Driven\": 200000, \"Present_Price\": 1, \"Fuel_Type\": \"Diesel\", \"Seller_Type\": \"Individual\", \"Transmission\": \"Automatic\", \"Age\": 15},\n","    {\"Kms_Driven\": 50000, \"Present_Price\": 10, \"Fuel_Type\": \"CNG\", \"Seller_Type\": \"Dealer\", \"Transmission\": \"Manual\", \"Age\": 5},\n","]\n","\n","# Charger les encodeurs et le scaler pour transformer les données d'entrée\n","scaler = joblib.load(\"/content/drive/MyDrive/ML 2/scaler_lr.pkl\")\n","encoders = joblib.load(\"/content/drive/MyDrive/ML 2/encoders_lr.pkl\")\n","model = joblib.load(\"/content/drive/MyDrive/ML 2/linear_regression_model.pkl\")\n","\n","# Préparer les données de test\n","input_df = pd.DataFrame(test_cases)\n","\n","# Encoder les variables catégorielles\n","for col, encoder in encoders.items():\n","    input_df[col] = encoder.transform(input_df[col])\n","\n","# Normaliser les données\n","input_scaled = scaler.transform(input_df)\n","\n","# Faire des prédictions\n","predictions = model.predict(input_scaled)\n","input_df[\"Predicted_Selling_Price\"] = predictions\n","\n","print(\"\\nRésultats avec prédictions :\")\n","print(input_df)\n"],"metadata":{"id":"bTF694rSjL-S"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import gradio as gr\n","import pandas as pd\n","import joblib\n","\n","# Charger le modèle, le scaler et les encodeurs\n","model = joblib.load(\"/content/drive/MyDrive/ML 2/linear_regression_model.pkl\")\n","scaler = joblib.load(\"/content/drive/MyDrive/ML 2/scaler.pkl\")\n","encoders = joblib.load(\"/content/drive/MyDrive/ML 2/encoders.pkl\")\n","\n","def predict_single(Kms_Driven, Present_Price, Fuel_Type, Seller_Type, Transmission, Age):\n","    \"\"\"\n","    Fonction pour la prédiction d'un seul véhicule.\n","    \"\"\"\n","    try:\n","        # Préparer les données d'entrée\n","        input_data = pd.DataFrame({\n","            \"Kms_Driven\": [Kms_Driven],\n","            \"Present_Price\": [Present_Price],\n","            \"Fuel_Type\": [Fuel_Type],\n","            \"Seller_Type\": [Seller_Type],\n","            \"Transmission\": [Transmission],\n","            \"Age\": [Age],\n","        })\n","\n","        # Encoder les variables catégorielles\n","        for col, encoder in encoders.items():\n","            input_data[col] = encoder.transform(input_data[col])\n","\n","        # Normaliser les données\n","        input_scaled = scaler.transform(input_data)\n","\n","        # Faire la prédiction\n","        prediction = model.predict(input_scaled)[0]\n","\n","        return round(prediction, 2)  # Retourne la prédiction arrondie\n","\n","    except Exception as e:\n","        return f\"Erreur : {e}\"\n","\n","\n","def predict_multiple(file):\n","    \"\"\"\n","    Fonction pour la prédiction de multiples véhicules à partir d'un fichier CSV.\n","    \"\"\"\n","    try:\n","        # Lire le fichier CSV\n","        data = pd.read_csv(file)\n","\n","        # Vérifier la structure du fichier\n","        required_columns = [\"Kms_Driven\", \"Present_Price\", \"Fuel_Type\", \"Seller_Type\", \"Transmission\", \"Age\"]\n","        for col in required_columns:\n","            if col not in data.columns:\n","                return f\"Le fichier doit contenir les colonnes suivantes : {required_columns}\"\n","\n","        # Encoder les colonnes catégoriques\n","        for col, encoder in encoders.items():\n","            data[col] = encoder.transform(data[col])\n","\n","        # Normaliser les données\n","        data_scaled = scaler.transform(data)\n","\n","        # Faire les prédictions\n","        predictions = model.predict(data_scaled)\n","        data[\"Predicted_Selling_Price\"] = predictions\n","\n","        return data\n","\n","    except Exception as e:\n","        return f\"Erreur : {e}\"\n","\n","\n","# Interface Gradio\n","with gr.Blocks() as app:\n","    gr.Markdown(\"# Application de Prédiction du Prix de Vente\")\n","\n","    gr.Markdown(\"## Instructions pour les colonnes catégoriques :\")\n","    gr.Markdown(\"\"\"\n","    - **Type de carburant (Fuel_Type)** :\n","        - Petrol = 0\n","        - Diesel = 1\n","        - CNG = 2\n","    - **Type de vendeur (Seller_Type)** :\n","        - Dealer = 0\n","        - Individual = 1\n","    - **Transmission** :\n","        - Manual = 0\n","        - Automatic = 1\n","    \"\"\")\n","\n","    # Section Prédiction Simple\n","    gr.Markdown(\"## Prédiction Simple\")\n","    with gr.Row():\n","        Kms_Driven = gr.Number(label=\"Kilomètres parcourus\")\n","        Present_Price = gr.Number(label=\"Prix actuel (en Lakhs)\")\n","        Fuel_Type = gr.Textbox(label=\"Type de carburant (Petrol, Diesel, CNG)\")\n","        Seller_Type = gr.Textbox(label=\"Type de vendeur (Dealer, Individual)\")\n","        Transmission = gr.Textbox(label=\"Transmission (Manual, Automatic)\")\n","        Age = gr.Number(label=\"Âge du véhicule (en années)\")\n","    predict_btn = gr.Button(\"Prédire\")\n","    output_simple = gr.Textbox(label=\"Prix de vente prédit\")\n","\n","    predict_btn.click(\n","        predict_single,\n","        inputs=[Kms_Driven, Present_Price, Fuel_Type, Seller_Type, Transmission, Age],\n","        outputs=output_simple,\n","    )\n","\n","    # Section Prédiction Multiple\n","    gr.Markdown(\"## Prédiction Multiple\")\n","    gr.Markdown(\"\"\"\n","    Chargez un fichier CSV contenant les colonnes suivantes :\n","    - Kms_Driven, Present_Price, Fuel_Type, Seller_Type, Transmission, Age\n","    \"\"\")\n","\n","    file_input = gr.File(label=\"Uploader un fichier CSV\")\n","    output_multiple = gr.Dataframe(label=\"Résultats avec Prédictions\")\n","\n","    file_input.change(\n","        predict_multiple,\n","        inputs=file_input,\n","        outputs=output_multiple,\n","    )\n","\n","app.launch()\n"],"metadata":{"id":"ip7H-tSczcDr"},"execution_count":null,"outputs":[]}]}