Create app.py

app.py

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+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
+from sklearn.preprocessing import LabelEncoder
+import gradio as gr
+
+# Load dataset
+data = pd.read_csv("cpdata.csv")
+
+# Split features and target
+y = data["label"]
+x = data.drop("label", axis=1)
+
+# Encode target labels
+encoder = LabelEncoder()
+y_encoded = encoder.fit_transform(y)
+
+# Train-test split
+xtrain, xtest, ytrain, ytest = train_test_split(x, y_encoded, test_size=0.2, random_state=0)
+
+# Train model
+classifier = RandomForestClassifier(n_estimators=100, random_state=0)
+classifier.fit(xtrain, ytrain)
+
+# Evaluate
+output = classifier.predict(xtest)
+accuracy = accuracy_score(ytest, output)
+precision = precision_score(ytest, output, average="weighted")
+recall = recall_score(ytest, output, average="weighted")
+f1 = f1_score(ytest, output, average="weighted")
+
+print("Model Performance:")
+print(f"Accuracy: {accuracy:.2f}")
+print(f"Precision: {precision:.2f}")
+print(f"Recall: {recall:.2f}")
+print(f"F1-score: {f1:.2f}")
+
+# Gradio prediction function
+def predict(temp, humi, ph, rain, N, P, K):
+    new_data = [[temp, humi, ph, rain, N, P, K]]
+    pred = classifier.predict(new_data)
+    plant = encoder.inverse_transform(pred)[0]
+    return f"Predicted plant for given condition: {plant}"
+
+# Gradio UI
+demo = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Number(label="Temperature"),
+        gr.Number(label="Humidity"),
+        gr.Number(label="pH"),
+        gr.Number(label="Rainfall"),
+        gr.Number(label="Nitrogen (N)"),
+        gr.Number(label="Phosphorus (P)"),
+        gr.Number(label="Potassium (K)")
+    ],
+    outputs=gr.Textbox(label="Prediction"),
+    title="Crop Prediction App",
+    description="Enter soil and climate conditions to predict the best plant."
+)
+
+if __name__ == "__main__":
+    demo.launch()