Update app.py

app.py

@@ -3,21 +3,16 @@ import pandas as pd
 from sklearn.linear_model import LinearRegression
 import matplotlib.pyplot as plt
 
-# Page config
-st.set_page_config(page_title="Crime Rate Prediction", layout="wide")
-st.title("📊 Crime Rate Prediction Based on Past Data")
+# Page configuration
+st.set_page_config(page_title="Crime Rate Predictor", layout="centered")
+st.title("🔮 Crime Rate Prediction for Indian States/UTs")
 
-# CSV path (Make sure this file is uploaded in Streamlit cloud if deployed)
-csv_path = "crime_data.csv"
+# CSV path (Hosted online)
+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 and preprocess data
     df = pd.read_csv(csv_path)
-
-    st.subheader("📄 Raw Dataset")
-    st.dataframe(df)
-
-    # Preprocess
     data = df[[
         'State/UT',
         'Number of Cases Registered - 2018-19',
@@ -26,44 +21,51 @@ try:
         'Number of Cases Registered - 2021-22 (up to 31.10.2021)'
     ]].copy()
     data.columns = ['State/UT', '2018', '2019', '2020', '2021']
-
-    # Convert string numbers to integers (if needed)
     for col in ['2018', '2019', '2020', '2021']:
         data[col] = pd.to_numeric(data[col], errors='coerce').fillna(0).astype(int)
 
-    # Sidebar for user 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)
+    # --- User Inputs ---
+    st.subheader("📝 Enter Details to Predict Future Crime Rates")
+    
+    # Dropdown for State selection
+    state_input = st.selectbox("Select State/UT", sorted(data['State/UT'].unique()))
+    
+    # Slider for year selection
+    year_input = st.slider("Select Starting Year", 2022, 2026, 2022)
 
-    # Perform prediction for selected state
-    selected_row = data[data['State/UT'] == selected_state].iloc[0]
-    years = [2018, 2019, 2020, 2021]
-    X_train = pd.DataFrame({'Year': years})
-    y_train = selected_row[['2018', '2019', '2020', '2021']].values
+    if state_input:
+        if state_input in data['State/UT'].values:
+            selected_row = data[data['State/UT'] == state_input].iloc[0]
+            X_train = pd.DataFrame({'Year': [2018, 2019, 2020, 2021]})
+            y_train = selected_row[['2018', '2019', '2020', '2021']].values
 
-    model = LinearRegression()
-    model.fit(X_train, y_train)
+            # Train model and predict
+            model = LinearRegression()
+            model.fit(X_train, y_train)
 
-    future_years = list(range(start_year, 2028))
-    predictions = model.predict(pd.DataFrame({'Year': future_years}))
+            future_years = list(range(year_input, 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]
-    })
+            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)")
-    st.dataframe(result_df)
+            # Show predictions
+            st.subheader(f"📈 Predicted Crime Rate for {state_input} ({year_input} to 2027)")
+            st.dataframe(result_df, use_container_width=True)
 
-    # 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)
+            # Plot
+            fig, ax = plt.subplots()
+            ax.plot(result_df['Year'], result_df['Predicted Crime Cases'], marker='o', linestyle='--', color='orangered')
+            ax.set_xlabel("Year")
+            ax.set_ylabel("Predicted Crime Cases")
+            ax.set_title(f"{state_input} Crime Rate Prediction")
+            st.pyplot(fig)
+        else:
+            st.warning("⚠️ Please enter a valid State/UT name from the dataset.")
+    else:
+        st.info("👈 Please enter a State/UT name to begin prediction.")
 
 except FileNotFoundError:
     st.error(f"❌ File not found at path: {csv_path}. Please check the path.")