deployment

app.py

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+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+from sklearn.svm import SVR
+from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
+from sklearn.preprocessing import LabelEncoder
+import gradio as gr
+
+def load_data():
+    url = 'https://raw.githubusercontent.com/NarutoOp/Crop-Recommendation/master/cropdata.csv'
+    data = pd.read_csv(url)
+    return data
+
+data = load_data()
+
+label_encoders = {}
+for column in ['STATE', 'CROP']:
+    le = LabelEncoder()
+    data[column] = le.fit_transform(data[column])
+    label_encoders[column] = le
+
+X = data[['YEAR', 'STATE', 'CROP', 'YEILD']]  # Feature columns
+y = data['PROFIT']  # Target column
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+models = {
+    'Linear Regression': LinearRegression(),
+    'SVR': SVR(),
+    'Random Forest': RandomForestRegressor(),
+    'Gradient Boosting': GradientBoostingRegressor()
+}
+
+for name, model in models.items():
+    model.fit(X_train, y_train)
+
+def predict(model_name, year, state, crop, yield_):
+    if model_name in models:
+        model = models[model_name]
+        state_encoded = label_encoders['STATE'].transform([state])[0]
+        crop_encoded = label_encoders['CROP'].transform([crop])[0]
+        prediction = model.predict([[year, state_encoded, crop_encoded, yield_]])[0]
+        return prediction
+    else:
+        return "Model not found"
+
+inputs = [
+    gr.Dropdown(choices=list(models.keys()), label='Model'),
+    gr.Number(label='Year'),
+    gr.Textbox(label='State'),
+    gr.Textbox(label='Crop'),
+    gr.Number(label='Yield')
+]
+outputs = gr.Textbox(label='Predicted Profit')
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=inputs,
+    outputs=outputs,
+    title="Profit Prediction using various ML models",
+    theme=gr.themes.Soft()
+)
+
+demo.launch()

requirements.txt

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+pandas
+scikit-learn
+gradio