Update app.py

app.py

@@ -3,21 +3,21 @@ import pandas as pd
 from sklearn.linear_model import LinearRegression
 import matplotlib.pyplot as plt
 
-# Page config
+# Page configuration
 st.set_page_config(page_title="Crime Rate Prediction", layout="wide")
 st.title("📊 Crime Rate Prediction Based on Past Data")
 
-# CSV path (Make sure this file is uploaded in Streamlit cloud if deployed)
+# CSV path (ensure the file is accessible or uploaded in cloud deployment)
 csv_path = "https://huggingface.co/spaces/MLDeveloper/crime_rate_predicition/resolve/main/RS_Session_255_AS_116.1%20(2).csv"
 
 try:
-    # Load the dataset
+    # Load dataset
     df = pd.read_csv(csv_path)
 
     st.subheader("📄 Raw Dataset")
     st.dataframe(df)
 
-    # Preprocess
+    # Preprocessing
     data = df[[
         'State/UT',
         'Number of Cases Registered - 2018-19',
@@ -27,16 +27,16 @@ try:
     ]].copy()
     data.columns = ['State/UT', '2018', '2019', '2020', '2021']
 
-    # Convert string numbers to integers (if needed)
+    # Convert to numeric
     for col in ['2018', '2019', '2020', '2021']:
         data[col] = pd.to_numeric(data[col], errors='coerce').fillna(0).astype(int)
 
-    # Sidebar for user input
+    # Sidebar input
     st.sidebar.header("🔍 Predict Future Crime")
     selected_state = st.sidebar.selectbox("Select a State/UT", data['State/UT'].unique())
-    start_year = st.sidebar.slider("Select starting year for prediction", 2022, 2026, 2022)
+    start_year = st.sidebar.slider("Select a year to predict", 2022, 2027, 2022)
 
-    # Perform prediction for selected state
+    # Filter and train model
     selected_row = data[data['State/UT'] == selected_state].iloc[0]
     years = [2018, 2019, 2020, 2021]
     X_train = pd.DataFrame({'Year': years})
@@ -45,25 +45,31 @@ try:
     model = LinearRegression()
     model.fit(X_train, y_train)
 
-    future_years = list(range(start_year, 2028))
+    # Predict future crime rates
+    future_years = list(range(2022, 2028))
     predictions = model.predict(pd.DataFrame({'Year': future_years}))
-
-    # Prepare result DataFrame
     result_df = pd.DataFrame({
         'Year': future_years,
         'Predicted Crime Cases': [max(0, int(pred)) for pred in predictions]
     })
 
-    st.subheader(f"📈 Predicted Crime Rate in {selected_state} ({start_year}–2027)")
+    # Display single year result
+    selected_year_prediction = result_df[result_df['Year'] == start_year]['Predicted Crime Cases'].values[0]
+    st.success(f"📌 **Predicted Crime Rate in {selected_state} for the year {start_year}: {selected_year_prediction} cases**")
+
+    # Show full table
+    st.subheader(f"📈 Predicted Crime Rate in {selected_state} (2022–2027)")
     st.dataframe(result_df)
 
-    # Plotting
-    fig2, ax2 = plt.subplots()
-    ax2.plot(result_df['Year'], result_df['Predicted Crime Cases'], marker='o', linestyle='--', color='teal')
-    ax2.set_xlabel("Year")
-    ax2.set_ylabel("Predicted Crime Cases")
-    ax2.set_title(f"Crime Trend Prediction for {selected_state}")
-    st.pyplot(fig2)
+    # Line chart
+    fig, ax = plt.subplots()
+    ax.plot(result_df['Year'], result_df['Predicted Crime Cases'], marker='o', linestyle='--', color='teal')
+    ax.set_xlabel("Year")
+    ax.set_ylabel("Predicted Crime Cases")
+    ax.set_title(f"Crime Trend Prediction for {selected_state}")
+    st.pyplot(fig)
 
 except FileNotFoundError:
     st.error(f"❌ File not found at path: {csv_path}. Please check the path.")
+except Exception as e:
+    st.error(f"⚠️ An error occurred: {e}")