Update app.py

app.py

@@ -1,37 +1,30 @@
 # app.py
 import gradio as gr
 import pandas as pd
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.model_selection import train_test_split
-from sklearn.metrics import accuracy_score
+import joblib
 
-# Load the dataset
+# Load pre-trained model and dataset
 data = pd.read_csv('Iris.csv')
+data.drop(columns=['Id'], inplace=True)
 
-# Prepare features and target
-X = data.iloc[:, 1:-1]  # Assuming the first column is an ID and last is the target
-y = data.iloc[:, -1]
-
-# Train a model
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
-model = RandomForestClassifier(random_state=42)
-model.fit(X_train, y_train)
+# Load the saved model
+model = joblib.load('best_random_forest_model.pkl')
 
 def classify_iris(sepal_length, sepal_width, petal_length, petal_width):
-    """Function to classify iris species based on input features."""
+    """Classify iris species based on input features."""
     input_features = [[sepal_length, sepal_width, petal_length, petal_width]]
     prediction = model.predict(input_features)[0]
     return prediction
 
 # Define the Gradio interface
 inputs = [
-    gr.inputs.Number(label="Sepal Length (cm)"),
-    gr.inputs.Number(label="Sepal Width (cm)"),
-    gr.inputs.Number(label="Petal Length (cm)"),
-    gr.inputs.Number(label="Petal Width (cm)")
+    gr.Number(label="Sepal Length (cm)"),
+    gr.Number(label="Sepal Width (cm)"),
+    gr.Number(label="Petal Length (cm)"),
+    gr.Number(label="Petal Width (cm)")
 ]
 
-outputs = gr.outputs.Textbox(label="Predicted Iris Species")
+outputs = gr.Textbox(label="Predicted Iris Species")
 
 description = "This app classifies iris species (Setosa, Versicolor, Virginica) based on the given features."