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import streamlit as st
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
import os
# Fix protobuf compatibility issue
os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python"
# Load Dataset
df = pd.read_csv("Iris.csv")
df.rename(columns={'Species': 'species'}, inplace=True)
# Tabs Navigation
tabs = st.tabs(["Overview", "Dataset Details", "Data Visualization", "Prediction"])
with tabs[0]: # Overview
st.title("Overview")
st.write("""
This app uses machine learning to classify Iris flowers into three species:
- Setosa
- Versicolor
- Virginica
The model is trained using the **Random Forest Classifier**.
""")
with tabs[1]: # Dataset Details
st.title("Dataset Details")
st.write("### Sample Data")
st.dataframe(df.head())
st.write("### Dataset Statistics")
st.write(df.describe())
with tabs[2]: # Data Visualization
st.title("Data Visualization")
st.write("### Pairplot of Features")
fig = sns.pairplot(df, hue="species", diag_kind="kde")
st.pyplot(fig)
st.write("### Feature Correlation")
fig, ax = plt.subplots()
sns.heatmap(df.drop(columns=['species']).corr(), annot=True, cmap='coolwarm', ax=ax)
st.pyplot(fig)
with tabs[3]: # Prediction
st.title("Iris Flower Prediction")
st.sidebar.header("Enter flower measurements:")
sepal_length = st.sidebar.slider("Sepal Length (cm)", 4.0, 8.0, 5.0)
sepal_width = st.sidebar.slider("Sepal Width (cm)", 2.0, 5.0, 3.0)
petal_length = st.sidebar.slider("Petal Length (cm)", 1.0, 7.0, 4.0)
petal_width = st.sidebar.slider("Petal Width (cm)", 0.1, 2.5, 1.0)
X = df.iloc[:, 1:-1]
y = df['species']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = RandomForestClassifier()
model.fit(X_train, y_train)
input_data = [[sepal_length, sepal_width, petal_length, petal_width]]
prediction = model.predict(input_data)[0]
st.write("### Prediction Result")
st.success(f"The predicted species is: {prediction}")
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