Created app.py

app.py

@@ -0,0 +1,274 @@
+import streamlit as st
+import plotly.graph_objects as go
+from pymongo import MongoClient
+from datetime import datetime, timedelta
+import pandas as pd
+from sklearn.preprocessing import MinMaxScaler
+import certifi
+import json
+import os
+
+mongo_uri = "mongodb+srv://Agripredict:TjXSvMhOis49qH8E@cluster0.gek7n.mongodb.net/?retryWrites=true&w=majority&appName=Cluster0"
+
+if not mongo_uri:
+    st.error("MongoDB URI is not set!")
+    st.stop()
+else:
+    # Connect to MongoDB with SSL certificate validation
+    client = MongoClient(mongo_uri, tlsCAFile=certifi.where())
+    db = client["AgriPredict"]
+    collection = db["WhiteSesame"]
+
+# CSS to increase the width of the container
+st.markdown("""
+    <style>
+        /* Adjust the width of the main container */
+        .main {
+            max-width: 1200px;  /* Increase the width */
+            margin: 0 auto;  /* Center the container */
+        }
+
+        /* Main background */
+        body {
+            background-color: #f9f9f9;
+        }
+
+        /* Title styling */
+        h1 {
+            color: #4CAF50;
+            font-family: 'Arial Black', sans-serif;
+        }
+
+        /* Buttons */
+        .stButton>button {
+            background-color: #4CAF50;
+            color: white;
+            font-size: 14px;
+            border-radius: 8px;
+            padding: 10px 20px;
+            margin: 5px;
+            white-space: nowrap;
+        }
+        .stButton>button:hover {
+            background-color: #45a049;
+        }
+
+        /* Selectbox styling */
+        .stSelectbox>div {
+            padding: 10px;
+            background-color: #ffffff;
+            border: 1px solid #e6e6e6;
+            border-radius: 8px;
+        }
+
+        /* Checkbox styling */
+        .stCheckbox>label {
+            font-size: 14px;
+            color: #555;
+        }
+
+        /* Containers */
+        .stContainer {
+            border-radius: 12px;
+            padding: 20px;
+            background-color: #ffffff;
+            box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
+        }
+
+        /* Chart area */
+        .plotly-graph-div {
+            border-radius: 12px;
+            box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
+        }
+
+        /* Footer */
+        footer {
+            font-size: 12px;
+            text-align: center;
+            color: #888;
+            padding: 10px;
+        }
+    </style>
+""", unsafe_allow_html=True)
+
+st.title("🌾 AgriPredict Dashboard")
+
+# Load the state-market dictionary from the JSON file
+with open('all_state_market_dict.json', 'r') as file:
+    state_market_dict = json.load(file)
+
+# UI for Dashboard
+with st.container():
+    with st.expander("AgriPredict Dashboard", expanded=True):
+        # Adjust the columns to fit more elements within the container
+        col1, col2, col3, col4, col5, col6, col7 = st.columns([1.5, 1.5, 1.5, 1.5, 1.5, 3, 3])
+
+        # Buttons for periods
+        with col1:
+            if st.button('2W', key='2_weeks'):
+                st.session_state.selected_period = 14
+
+        with col2:
+            if st.button('1M', key='1_month'):
+                st.session_state.selected_period = 30
+
+        with col3:
+            if st.button('3M', key='3_months'):
+                st.session_state.selected_period = 90
+
+        with col4:
+            if st.button('1Y', key='1_year'):
+                st.session_state.selected_period = 365
+
+        with col5:
+            if st.button('5Y', key='5_year'):
+                st.session_state.selected_period = 1825
+
+        # Dropdown for states
+        with col6:
+            states = list(state_market_dict.keys())
+            selected_state = st.selectbox(
+                "Choose a state",
+                states,
+                key="state_selectbox",
+                index=0
+            )
+
+        # Dropdown for selecting between Price, Volume, or Both
+        with col7:
+            data_type = st.selectbox(
+                "Select Data Type",
+                ["Price", "Volume", "Both"]
+            )
+
+        # Checkbox for market-wise analysis
+        st.write("")
+        with st.container():
+            market_wise = st.checkbox("Market wise", key="market_checkbox")
+
+        if market_wise:
+            # Get markets for the selected state
+            markets = state_market_dict.get(selected_state, [])
+            selected_market = st.selectbox(
+                "Choose a market",
+                markets,
+                key="market_selectbox",
+                index=0
+            )
+            query_filter = {"state": selected_state, "Market Name": selected_market}
+        else:
+            query_filter = {"state": selected_state}
+
+        # Add date filtering based on selected period
+        if 'selected_period' in st.session_state:
+            days_period = st.session_state.selected_period
+            query_filter["Reported Date"] = {
+                "$gte": datetime.now() - timedelta(days=days_period)
+            }
+
+        # Fetch data from MongoDB
+        try:
+            cursor = collection.find(query_filter)
+            data = list(cursor)
+
+            if data:
+                # Convert MongoDB data to a DataFrame
+                df = pd.DataFrame(data)
+                df['Reported Date'] = pd.to_datetime(df['Reported Date'])
+
+                # Group by Reported Date
+                df_grouped = (
+                    df.groupby('Reported Date', as_index=False)
+                    .agg({
+                        'Arrivals (Tonnes)': 'sum',
+                        'Modal Price (Rs./Quintal)': 'mean'
+                    })
+                )
+
+                # Create a complete date range
+                date_range = pd.date_range(
+                    start=df_grouped['Reported Date'].min(),
+                    end=df_grouped['Reported Date'].max()
+                )
+                df_grouped = df_grouped.set_index('Reported Date').reindex(date_range).rename_axis('Reported Date').reset_index()
+
+                # Fill missing values
+                df_grouped['Arrivals (Tonnes)'] = df_grouped['Arrivals (Tonnes)'].fillna(
+                    method='ffill').fillna(method='bfill')
+                df_grouped['Modal Price (Rs./Quintal)'] = df_grouped['Modal Price (Rs./Quintal)'].fillna(
+                    method='ffill').fillna(method='bfill')
+
+                st.subheader(f"📈 Trend Graph for {selected_state} ({'Market: ' + selected_market if market_wise else 'State'})")
+
+                if data_type == "Both":
+                    # Min-Max Scaling
+                    scaler = MinMaxScaler()
+                    df_grouped[['Scaled Price', 'Scaled Arrivals']] = scaler.fit_transform(
+                        df_grouped[['Modal Price (Rs./Quintal)', 'Arrivals (Tonnes)']]
+                    )
+
+                    fig = go.Figure()
+
+                    # Plot Scaled Price with actual values on hover
+                    fig.add_trace(go.Scatter(
+                        x=df_grouped['Reported Date'],
+                        y=df_grouped['Scaled Price'],
+                        mode='lines',
+                        name='Scaled Price',
+                        line=dict(width=1, color='green'),
+                        text=df_grouped['Modal Price (Rs./Quintal)'],  # Actual Modal Price values
+                        hovertemplate='Date: %{x}<br>Scaled Price: %{y:.2f}<br>Actual Price: %{text:.2f}<extra></extra>'
+                    ))
+
+                    # Plot Scaled Arrivals with actual values on hover
+                    fig.add_trace(go.Scatter(
+                        x=df_grouped['Reported Date'],
+                        y=df_grouped['Scaled Arrivals'],
+                        mode='lines',
+                        name='Scaled Arrivals',
+                        line=dict(width=1, color='blue'),
+                        text=df_grouped['Arrivals (Tonnes)'],  # Actual Arrivals values
+                        hovertemplate='Date: %{x}<br>Scaled Arrivals: %{y:.2f}<br>Actual Arrivals: %{text:.2f}<extra></extra>'
+                    ))
+
+                    fig.update_layout(
+                        title="Price and Arrivals Trend",
+                        xaxis_title='Date',
+                        yaxis_title='Scaled Values',
+                        template='plotly_white'
+                    )
+                    st.plotly_chart(fig)
+
+                elif data_type == "Price":
+                    # Plot Modal Price
+                    fig = go.Figure()
+                    fig.add_trace(go.Scatter(
+                        x=df_grouped['Reported Date'],
+                        y=df_grouped['Modal Price (Rs./Quintal)'],
+                        mode='lines',
+                        name='Modal Price',
+                        line=dict(width=1, color='green')
+                    ))
+                    fig.update_layout(title="Modal Price Trend", xaxis_title='Date', yaxis_title='Price', template='plotly_white')
+                    st.plotly_chart(fig)
+
+                elif data_type == "Volume":
+                    # Plot Arrivals (Tonnes)
+                    fig = go.Figure()
+                    fig.add_trace(go.Scatter(
+                        x=df_grouped['Reported Date'],
+                        y=df_grouped['Arrivals (Tonnes)'],
+                        mode='lines',
+                        name='Arrivals',
+                        line=dict(width=1, color='blue')
+                    ))
+                    fig.update_layout(title="Arrivals Trend", xaxis_title='Date', yaxis_title='Volume', template='plotly_white')
+                    st.plotly_chart(fig)
+
+                else:
+                    st.warning("⚠️ No relevant data found for the selected options.")
+            else:
+                st.warning("⚠️ No data found for the selected filters.")
+
+        except Exception as e:
+            st.error(f"❌ Error fetching data: {e}")