Update app.py

app.py

@@ -1,6 +1,3 @@
-
-!pip install gradio scikit-learn joblib
-
 import numpy as np
 import pandas as pd
 import gradio as gr
@@ -9,56 +6,74 @@ 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
+import os
+
+# ---------------------------
+# 1. Train or Load Model
+# ---------------------------
+MODEL_FILE = "turgor_model.pkl"
+
+if not os.path.exists(MODEL_FILE):
+    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
+    )
 
-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)
-
+    pipeline = Pipeline(
+        [
+            ("scaler", StandardScaler()),
+            ("select", SelectKBest(score_func=f_regression, k=3)),  # top 3 features
+            ("regressor", RandomForestRegressor(n_estimators=100, random_state=42)),
+        ]
+    )
 
-y_pred = pipeline.predict(X_test)
-print("MSE:", mean_squared_error(y_test, y_pred))
-print("R2 Score:", r2_score(y_test, y_pred))
+    pipeline.fit(X_train, y_train)
 
-# Save model
-joblib.dump(pipeline, "turgor_model.pkl")
+    y_pred = pipeline.predict(X_test)
+    print("MSE:", mean_squared_error(y_test, y_pred))
+    print("R2 Score:", r2_score(y_test, y_pred))
 
+    joblib.dump(pipeline, MODEL_FILE)
 
-model = joblib.load("turgor_model.pkl")
+# Load trained model
+model = joblib.load(MODEL_FILE)
 
+# ---------------------------
+# 2. Prediction Function
+# ---------------------------
 def predict_turgor(leaf_thickness, water_content, osmotic_pressure, temperature):
-    input_data = np.array([[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"
 
+# ---------------------------
+# 3. Gradio UI
+# ---------------------------
 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 (%)")
@@ -71,8 +86,7 @@ with gr.Blocks() as demo:
     predict_btn.click(
         fn=predict_turgor,
         inputs=[leaf_thickness, water_content, osmotic_pressure, temperature],
-        outputs=output
+        outputs=output,
     )
 
 demo.launch()
-