Upload 3 files

app.py

@@ -0,0 +1,34 @@
+import streamlit as st
+import pickle
+import numpy as np
+
+# Load the saved model
+with open('model.pkl', 'rb') as f:
+    model = pickle.load(f)
+
+st.title("Iris Flower Prediction App")
+
+st.write("""
+Enter the **Iris flower measurements** below to predict the species. 
+""")
+
+# Create input widgets for each feature
+sepal_length = st.number_input("Sepal length (cm)", min_value=0.0, max_value=10.0, value=5.0)
+sepal_width  = st.number_input("Sepal width (cm)",  min_value=0.0, max_value=10.0, value=3.0)
+petal_length = st.number_input("Petal length (cm)", min_value=0.0, max_value=10.0, value=4.0)
+petal_width  = st.number_input("Petal width (cm)",  min_value=0.0, max_value=10.0, value=1.0)
+
+if st.button("Predict"):
+    # 1. Convert input to a numpy array
+    input_data = np.array([[sepal_length, sepal_width, petal_length, petal_width]])
+    
+    # 2. Make prediction
+    prediction = model.predict(input_data)
+    
+    # 3. Map numeric prediction to Iris class name
+    #  (Iris dataset classes -> 0: setosa, 1: versicolor, 2: virginica)
+    class_names = ["Iris-setosa", "Iris-versicolor", "Iris-virginica"]
+    predicted_class = class_names[prediction[0]]
+    
+    # 4. Display the result
+    st.write(f"**Predicted Iris Species:** {predicted_class}")

model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8d969c8137d8d3e7d96545046c1c3ee9b2342bcb18daf82c49b25415e0450ea2
+size 835

train_model.py

@@ -0,0 +1,33 @@
+import pandas as pd
+from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LogisticRegression
+import pickle
+
+# 1. Load the iris dataset
+iris = load_iris()
+X = iris.data  # features
+y = iris.target  # labels
+
+# 2. Convert to a DataFrame (optional, for illustration)
+df = pd.DataFrame(X, columns=iris.feature_names)
+df['target'] = y
+
+# 3. Train-test split
+X_train, X_test, y_train, y_test = train_test_split(X, y, 
+                                                    test_size=0.2, 
+                                                    random_state=42)
+
+# 4. Train a simple Logistic Regression model
+model = LogisticRegression(max_iter=200)
+model.fit(X_train, y_train)
+
+# 5. Evaluate the model (optional, just to see performance)
+accuracy = model.score(X_test, y_test)
+print(f"Model accuracy: {accuracy:.2f}")
+
+# 6. Save the trained model as a pickle file
+with open('model.pkl', 'wb') as f:
+    pickle.dump(model, f)
+
+print("Model has been trained and saved as model.pkl")