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app.py

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+import gradio as gr
+import pandas as pd
+import numpy as np
+import joblib
+import os
+
+# Carica il modello
+model = joblib.load('house_price_model.joblib')
+
+# Carica i nomi delle feature
+with open('feature_names.txt', 'r') as f:
+    feature_names = [line.strip() for line in f.readlines()]
+
+def predict_prices(csv_file):
+    # Leggi il file CSV
+    df = pd.read_csv(csv_file.name)
+    
+    # Verifica che il file contenga le colonne necessarie
+    missing_cols = [col for col in feature_names if col not in df.columns]
+    if missing_cols:
+        return f"Errore: Il file CSV manca delle seguenti colonne: {', '.join(missing_cols)}"
+    
+    # Seleziona solo le colonne necessarie nell'ordine corretto
+    input_data = df[feature_names]
+    
+    # Fai la previsione
+    predictions = model.predict(input_data)
+    
+    # Aggiungi le previsioni al DataFrame originale
+    df['predicted_price'] = predictions
+    
+    # Salva il risultato in un nuovo file CSV
+    result_path = "predictions_result.csv"
+    df.to_csv(result_path, index=False)
+    
+    return result_path
+
+# Crea l'interfaccia Gradio
+iface = gr.Interface(
+    fn=predict_prices,
+    inputs=gr.File(label="Carica un file CSV con le caratteristiche delle case"),
+    outputs=gr.File(label="Scarica i risultati"),
+    title="Previsione Prezzi Case",
+    description=f"Carica un file CSV contenente le seguenti colonne: {', '.join(feature_names)}. "
+                "Il modello prevederà il prezzo per ogni casa e restituirà un nuovo file CSV con i risultati."
+)
+
+# Avvia l'app
+iface.launch()

model_training.ipynb

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+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyNBjldI+sEf3alrGTO/Spin"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vBSz9RYyudoZ","executionInfo":{"status":"ok","timestamp":1740665464218,"user_tz":-60,"elapsed":11884,"user":{"displayName":"Michele Cantoni","userId":"10582761587185035813"}},"outputId":"3e26604c-7f2e-4a0d-8b61-8aef97ec87b6"},"outputs":[{"output_type":"stream","name":"stdout","text":["R² score: 0.8053\n","RMSE: 0.5051\n"]}],"source":["import numpy as np\n","import pandas as pd\n","from sklearn.model_selection import train_test_split\n","from sklearn.ensemble import RandomForestRegressor\n","from sklearn.preprocessing import StandardScaler\n","from sklearn.pipeline import Pipeline\n","from sklearn.metrics import root_mean_squared_error\n","import joblib\n","\n","# Per questo esempio, usiamo il dataset California Housing\n","from sklearn.datasets import fetch_california_housing\n","\n","# Carica i dati\n","housing = fetch_california_housing()\n","X = pd.DataFrame(housing.data, columns=housing.feature_names)\n","y = housing.target\n","\n","# Dividi in training e test set\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","# Crea una pipeline con standardizzazione e modello\n","pipeline = Pipeline([\n","    ('scaler', StandardScaler()),\n","    ('regressor', RandomForestRegressor(n_estimators=100, random_state=42))\n","])\n","\n","# Addestra il modello\n","pipeline.fit(X_train, y_train)\n","\n","# Valuta il modello\n","score = pipeline.score(X_test, y_test)\n","y_pred = pipeline.predict(X_test)\n","print(f\"R² score: {score:.4f}\")\n","print(f\"RMSE: {root_mean_squared_error(y_pred,y_test):.4f}\")\n","\n","# Salva il modello\n","joblib.dump(pipeline, 'house_price_model.joblib')\n","\n","# Salva anche una descrizione delle feature per riferimento futuro\n","feature_names = housing.feature_names\n","with open('feature_names.txt', 'w') as f:\n","    for feature in feature_names:\n","        f.write(f\"{feature}\\n\")\n","\n","with open('description.txt', 'w') as f:\n","    f.write(housing['DESCR'])"]},{"cell_type":"code","source":[],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"XLztU8FUu_sf","executionInfo":{"status":"ok","timestamp":1740665382445,"user_tz":-60,"elapsed":61,"user":{"displayName":"Michele Cantoni","userId":"10582761587185035813"}},"outputId":"d94db415-9014-4fbc-e9cf-95fcaec8f86a"},"execution_count":7,"outputs":[{"output_type":"execute_result","data":{"text/plain":["       MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n","0      8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n","1      8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n","2      7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n","3      5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n","4      3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n","...       ...       ...       ...        ...         ...       ...       ...   \n","20635  1.5603      25.0  5.045455   1.133333       845.0  2.560606     39.48   \n","20636  2.5568      18.0  6.114035   1.315789       356.0  3.122807     39.49   \n","20637  1.7000      17.0  5.205543   1.120092      1007.0  2.325635     39.43   \n","20638  1.8672      18.0  5.329513   1.171920       741.0  2.123209     39.43   \n","20639  2.3886      16.0  5.254717   1.162264      1387.0  2.616981     39.37   \n","\n","       Longitude  \n","0        -122.23  \n","1        -122.22  \n","2        -122.24  \n","3        -122.25  \n","4        -122.25  \n","...          ...  \n","20635    -121.09  \n","20636    -121.21  \n","20637    -121.22  \n","20638    -121.32  \n","20639    -121.24  \n","\n","[20640 rows x 8 columns]"],"text/html":["\n","  <div id=\"df-37d6d979-0beb-476d-85b5-18e590983116\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>MedInc</th>\n","      <th>HouseAge</th>\n","      <th>AveRooms</th>\n","      <th>AveBedrms</th>\n","      <th>Population</th>\n","      <th>AveOccup</th>\n","      <th>Latitude</th>\n","      <th>Longitude</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>8.3252</td>\n","      <td>41.0</td>\n","      <td>6.984127</td>\n","      <td>1.023810</td>\n","      <td>322.0</td>\n","      <td>2.555556</td>\n","      <td>37.88</td>\n","      <td>-122.23</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>8.3014</td>\n","      <td>21.0</td>\n","      <td>6.238137</td>\n","      <td>0.971880</td>\n","      <td>2401.0</td>\n","      <td>2.109842</td>\n","      <td>37.86</td>\n","      <td>-122.22</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>7.2574</td>\n","      <td>52.0</td>\n","      <td>8.288136</td>\n","      <td>1.073446</td>\n","      <td>496.0</td>\n","      <td>2.802260</td>\n","      <td>37.85</td>\n","      <td>-122.24</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>5.6431</td>\n","      <td>52.0</td>\n","      <td>5.817352</td>\n","      <td>1.073059</td>\n","      <td>558.0</td>\n","      <td>2.547945</td>\n","      <td>37.85</td>\n","      <td>-122.25</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>3.8462</td>\n","      <td>52.0</td>\n","      <td>6.281853</td>\n","      <td>1.081081</td>\n","      <td>565.0</td>\n","      <td>2.181467</td>\n","      <td>37.85</td>\n","      <td>-122.25</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>20635</th>\n","      <td>1.5603</td>\n","      <td>25.0</td>\n","      <td>5.045455</td>\n","      <td>1.133333</td>\n","      <td>845.0</td>\n","      <td>2.560606</td>\n","      <td>39.48</td>\n","      <td>-121.09</td>\n","    </tr>\n","    <tr>\n","      <th>20636</th>\n","      <td>2.5568</td>\n","      <td>18.0</td>\n","      <td>6.114035</td>\n","      <td>1.315789</td>\n","      <td>356.0</td>\n","      <td>3.122807</td>\n","      <td>39.49</td>\n","      <td>-121.21</td>\n","    </tr>\n","    <tr>\n","      <th>20637</th>\n","      <td>1.7000</td>\n","      <td>17.0</td>\n","      <td>5.205543</td>\n","      <td>1.120092</td>\n","      <td>1007.0</td>\n","      <td>2.325635</td>\n","      <td>39.43</td>\n","      <td>-121.22</td>\n","    </tr>\n","    <tr>\n","      <th>20638</th>\n","      <td>1.8672</td>\n","      <td>18.0</td>\n","      <td>5.329513</td>\n","      <td>1.171920</td>\n","      <td>741.0</td>\n","      <td>2.123209</td>\n","      <td>39.43</td>\n","      <td>-121.32</td>\n","    </tr>\n","    <tr>\n","      <th>20639</th>\n","      <td>2.3886</td>\n","      <td>16.0</td>\n","      <td>5.254717</td>\n","      <td>1.162264</td>\n","      <td>1387.0</td>\n","      <td>2.616981</td>\n","      <td>39.37</td>\n","      <td>-121.24</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>20640 rows × 8 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-37d6d979-0beb-476d-85b5-18e590983116')\"\n"," 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readme.md

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+# Previsione Prezzi Case
+
+Questa app utilizza un modello di machine learning addestrato con scikit-learn per prevedere i prezzi delle case.
+
+## Come utilizzare l'app
+
+1. Prepara un file CSV contenente le seguenti colonne:
+   - MedInc: reddito mediano del quartiere
+   - HouseAge: età media delle case nel quartiere
+   - AveRooms: numero medio di stanze per casa
+   - AveBedrms: numero medio di camere da letto per casa
+   - Population: popolazione nel quartiere
+   - AveOccup: numero medio di occupanti per casa
+   - Latitude: latitudine del quartiere
+   - Longitude: longitudine del quartiere
+
+2. Carica il file CSV nell'app
+3. Clicca su "Submit"
+4. Scarica il file CSV risultante con le previsioni
+
+Nota: Il modello è stato addestrato sul dataset California Housing, quindi funziona meglio con dati simili.

requirements.txt

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+scikit-learn==1.0.2
+pandas==1.4.2
+numpy==1.22.3
+gradio==3.7.0
+joblib==1.1.0