Create app.py

app.py

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+
+!pip install gradio scikit-learn joblib
+
+import numpy as np
+import pandas as pd
+import gradio as gr
+from sklearn.model_selection import train_test_split
+from sklearn.feature_selection import SelectKBest, f_regression
+from sklearn.preprocessing import StandardScaler
+from sklearn.pipeline import Pipeline
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.svm import SVR
+from sklearn.metrics import mean_squared_error, r2_score
+import joblib
+
+
+np.random.seed(42)
+n_samples = 500
+
+X = pd.DataFrame({
+    "leaf_thickness": np.random.uniform(0.2, 1.0, n_samples),
+    "water_content": np.random.uniform(60, 95, n_samples),
+    "osmotic_pressure": np.random.uniform(0.5, 2.5, n_samples),
+    "temperature": np.random.uniform(15, 40, n_samples),
+})
+
+
+y = 0.3*X["leaf_thickness"] + 0.5*X["water_content"] - 0.2*X["temperature"] + \
+    0.8*X["osmotic_pressure"] + np.random.normal(0, 2, n_samples)
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+
+pipeline = Pipeline([
+    ("scaler", StandardScaler()),
+    ("select", SelectKBest(score_func=f_regression, k=3)),   # Select top 3 features
+    ("regressor", RandomForestRegressor(n_estimators=100, random_state=42))
+])
+
+pipeline.fit(X_train, y_train)
+
+
+y_pred = pipeline.predict(X_test)
+print("MSE:", mean_squared_error(y_test, y_pred))
+print("R2 Score:", r2_score(y_test, y_pred))
+
+# Save model
+joblib.dump(pipeline, "turgor_model.pkl")
+
+
+model = joblib.load("turgor_model.pkl")
+
+def predict_turgor(leaf_thickness, water_content, osmotic_pressure, temperature):
+    input_data = np.array([[leaf_thickness, water_content, osmotic_pressure, temperature]])
+    pred = model.predict(input_data)[0]
+    return f"Predicted Turgor Pressure: {pred:.2f} units"
+
+with gr.Blocks() as demo:
+    gr.Markdown("# 🌱 Turgor Pressure Detection")
+    gr.Markdown("Predict plant turgor pressure using physiological and environmental features.")
+    
+    with gr.Row():
+        leaf_thickness = gr.Slider(0.2, 1.0, value=0.5, step=0.01, label="Leaf Thickness (mm)")
+        water_content = gr.Slider(60, 95, value=80, step=0.1, label="Water Content (%)")
+        osmotic_pressure = gr.Slider(0.5, 2.5, value=1.2, step=0.01, label="Osmotic Pressure (MPa)")
+        temperature = gr.Slider(15, 40, value=25, step=0.5, label="Temperature (°C)")
+
+    output = gr.Textbox(label="Prediction")
+
+    predict_btn = gr.Button("Predict")
+    predict_btn.click(
+        fn=predict_turgor,
+        inputs=[leaf_thickness, water_content, osmotic_pressure, temperature],
+        outputs=output
+    )
+
+demo.launch()
+