Fix: Readme

streamlit_app.py

@@ -6,6 +6,8 @@ import streamlit as st
 from datetime import datetime, timedelta
 import pandas as pd
 from sklearn.preprocessing import MinMaxScaler
+import os
+from dotenv import load_dotenv
 
 from src.agri_predict import (
     fetch_and_process_data,
@@ -20,6 +22,10 @@ from src.agri_predict.constants import state_market_dict
 from src.agri_predict.utils import authenticate_user
 from src.agri_predict.config import get_collections
 
+# Load environment variables
+load_dotenv()
+IS_PROD = os.getenv("PROD", "False").lower() == "true"
+
 
 st.set_page_config(layout="wide")
 
@@ -138,7 +144,7 @@ if st.session_state.authenticated:
             states = ["Karnataka", "Madhya Pradesh", "Gujarat", "Uttar Pradesh", "Telangana"]
             selected_state = st.selectbox("Select State for Model Training", states)
             filter_key = f"state_{selected_state}"
-            if st.button("Train and Forecast"):
+            if not IS_PROD and st.button("Train and Forecast"):
                 query_filter = {"State Name": selected_state}
                 df = fetch_and_process_data(query_filter)
                 if sub_timeline == "14 days":
@@ -160,7 +166,7 @@ if st.session_state.authenticated:
             market_options = ["Rajkot", "Gondal", "Kalburgi", "Amreli"]
             selected_market = st.selectbox("Select Market for Model Training", market_options)
             filter_key = f"market_{selected_market}"
-            if st.button("Train and Forecast"):
+            if not IS_PROD and st.button("Train and Forecast"):
                 query_filter = {"Market Name": selected_market}
                 df = fetch_and_process_data(query_filter)
                 if sub_timeline == "14 days":
@@ -180,7 +186,7 @@ if st.session_state.authenticated:
                     forecast(df, filter_key, 90)
         elif sub_option == "India":
             df = collection_to_dataframe(impExp)
-            if st.button("Train and Forecast"):
+            if not IS_PROD and st.button("Train and Forecast"):
                 query_filter = {}
                 df = fetch_and_process_data(query_filter)
                 if sub_timeline == "14 days":