Update app.py

app.py

@@ -21,7 +21,9 @@ warnings.filterwarnings('ignore')
 
 st.title("Predict Unrolled Values")
 st.sidebar.header('Enter the Details here')
-st.write("""Regression Model""")
+st.write("""This Random Forest Regressor model helps to forecast unrolled values with impressive accuracy. 
+            Leveraging the strength of the Random Forest technique, we can now make reliable predictions that 
+            enable us to plan and strategize effectively in the fast-paced media landscape.""")
 
 # load the saved model using pickle
 with open('aajTak_model.pkl', 'rb') as file:
@@ -47,13 +49,17 @@ def user_report():
   # Share = round(float(st.sidebar.slider('Share', 0.000000, 100.000000, 0.611246, step=0.000001)), 6)
   # AMA = round(float(st.sidebar.slider('AMA', 0.000000, 45.000000, 4.196084, step=0.000001)), 6)
   # rate = round(float(st.sidebar.slider('rate', 0.000000, 1.500000, 0.018516, step=0.000001)), 6)  
+  # daily_reach = round(float(st.sidebar.slider('daily reach', 0.000000, 300.000000, 36.23)), 6) 
+  # cume_reach = round(float(st.sidebar.slider('cume reach', 0.000000, 300.000000, 36.231006)), 6)
 
+    
   Share = round(float(st.sidebar.number_input('Share', 0.0, 100.0, 0.611246, step=0.000001)), 6)
   AMA = round(float(st.sidebar.number_input('AMA', 0.0, 45.0, 4.196084, step=0.000001)), 6)
   rate = round(float(st.sidebar.number_input('rate', 0.0, 1.5, 0.018516, step=0.000001)), 6)
+  daily_reach = round(float(st.sidebar.number_input('daily reach', 0.0, 300.0, 36.23, step=0.000001)), 6)
+  cume_reach = round(float(st.sidebar.number_input('cume reach', 0.0, 300.0, 36.231006, step=0.000001)), 6)
 
-  daily_reach = round(float(st.sidebar.slider('daily reach', 0.000000, 300.000000, 36.23)), 6) 
-  cume_reach = round(float(st.sidebar.slider('cume reach', 0.000000, 300.000000, 36.231006)), 6)
+  
 
   # Share = st.sidebar.slider('Share', 0, 100, 0)
   # AMA = st.sidebar.slider('AMA', 0, 45, 4)
@@ -123,12 +129,23 @@ def predict_unrolled_value(user_data):
     # make the prediction using the loaded model and input data
     predicted_unrolled_value = model.predict(user_data)
     
-    # return the predicted max number of repairs as output
-    return np.round(predicted_unrolled_value[0])
+    # return the predict_unrolled_value as output
+    return predicted_unrolled_value[0]
 
     
 # Function calling
 y_pred = int(predict_unrolled_value(user_data))
-st.write("Click here to see the Predictions")
-if st.button("Predict"):
+
+# CSS code for changing color of the button
+st.markdown("""
+    <style>
+    .stButton button {
+        background-color: #668f45;                        
+        color: white;
+    }
+    </style>
+    """, unsafe_allow_html=True)
+
+# st.write("Click here to see the Predictions")
+if st.button("Click here for Predictions"):
     st.subheader(f"Predicted Unrolled Value: {y_pred} ")