Update app.py

app.py

@@ -1,27 +1,41 @@
-import pickle
-import pandas as pd
+import gradio as gr
 import numpy as np
-import streamlit as st
+import pickle
 import sklearn
 
+# Load your trained model
 model_file = "model-3.pkl"
 try:
-    with open(model_file,'rb') as file:
+    with open(model_file, 'rb') as file:
         model = pickle.load(file)
 except FileNotFoundError:
-    st.error("The file was not found in the directory")
+    raise FileNotFoundError("Model file 'model-3.pkl' not found in the directory.")
 
-st.title("FLower Classification using Streamlit on IRIS DATASET")
-st.header("Enter your flower features to get the classification prediction")
+# Define prediction function
+def predict_flower(sepal_length, sepal_width, petal_length, petal_width):
+    features = np.array([[sepal_length, sepal_width, petal_length, petal_width]])
+    prediction = model.predict(features)[0]
+    return f"The predicted flower class is: {prediction}"
 
-sepal_length = st.number_input("Enter yuour sepal length") 
-sepal_width = st.number_input("Enter yuour sepal width")
-petal_length = st.number_input("Enter yuour petal length")
-petal_width = st.number_input("Enter yuour petal width")
+# Define input and output components
+inputs = [
+    gr.Number(label="Sepal Length"),
+    gr.Number(label="Sepal Width"),
+    gr.Number(label="Petal Length"),
+    gr.Number(label="Petal Width")
+]
 
-if st.button("PREDICT"):
-    features = np.array([[sepal_length,sepal_width,petal_length,petal_width]])
-    prediction = model.predict(features)[0]
+output = gr.Textbox(label="Prediction Result")
+
+# Create the Gradio interface
+demo = gr.Interface(
+    fn=predict_flower,
+    inputs=inputs,
+    outputs=output,
+    title="🌸 Flower Classification on IRIS Dataset",
+    description="Enter the flower's sepal and petal measurements to predict its species using a trained ML model."
+)
 
-    st.subheader("Prediction has been made")
-    st.write("Theprediction for your features is",predicton)
+# Launch the Gradio app
+if __name__ == "__main__":
+    demo.launch()