Upload app.py

app.py

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+# ========================
+# IMPORTS
+# ========================
+import os
+import pandas as pd
+import requests
+import numpy as np
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.metrics import mean_absolute_error, mean_squared_error
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import LSTM, Dense
+import streamlit as st
+from prophet import Prophet
+import plotly.graph_objects as go
+import math
+
+# ========================
+# CONFIGURATION
+# ========================
+API_KEY = "579b464db66ec23bdd000001cff99e32ded74c1e60ee264b550dd5c6"
+RESOURCE_ID = "35985678-0d79-46b4-9ed6-6f13308a1d24"
+TRAIN_EPOCHS = 10
+FORECAST_HORIZON = 30
+LOOK_BACK = 7
+
+FORECAST_DIR = "forecasts"
+os.makedirs(FORECAST_DIR, exist_ok=True)
+
+# ========================
+# PAGE SETUP
+# ========================
+st.set_page_config(
+    page_title="🌾 Commodity Price Predictor", 
+    page_icon="🌱", 
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+st.markdown("""
+    <style>
+    /* Main container styling */
+    .main { 
+        background: linear-gradient(135deg, #f5f7fa 0%, #e8f5e9 100%);
+        padding: 2rem; 
+    }
+    
+    /* Header styling */
+    h1 { 
+        text-align: center; 
+        color: #1b5e20;
+        font-size: 3rem;
+        font-weight: 700;
+        margin-bottom: 0.5rem;
+        text-shadow: 2px 2px 4px rgba(0,0,0,0.1);
+    }
+    
+    h2 {
+        color: #2e7d32;
+        border-left: 4px solid #4caf50;
+        padding-left: 1rem;
+        margin-top: 2rem;
+    }
+    
+    h3 {
+        color: #388e3c;
+    }
+    
+    /* Subtitle */
+    .subtitle {
+        text-align: center;
+        color: #558b2f;
+        font-size: 1.2rem;
+        margin-bottom: 2rem;
+        font-weight: 400;
+    }
+    
+    /* Button styling */
+    .stButton>button { 
+        background: linear-gradient(135deg, #2e7d32 0%, #4caf50 100%);
+        color: white; 
+        border-radius: 12px; 
+        border: none; 
+        padding: 12px 32px;
+        font-size: 1.1rem;
+        font-weight: 600;
+        box-shadow: 0 4px 12px rgba(46, 125, 50, 0.3);
+        transition: all 0.3s ease;
+        width: 100%;
+    }
+    .stButton>button:hover { 
+        background: linear-gradient(135deg, #1b5e20 0%, #2e7d32 100%);
+        transform: translateY(-2px);
+        box-shadow: 0 6px 16px rgba(46, 125, 50, 0.4);
+    }
+    
+    /* Metric box styling */
+    .metric-box { 
+        background: linear-gradient(135deg, #ffffff 0%, #f1f8e9 100%);
+        border-radius: 16px; 
+        padding: 24px; 
+        box-shadow: 0px 4px 12px rgba(0,0,0,0.08);
+        text-align: center;
+        border: 2px solid #c5e1a5;
+        transition: transform 0.3s ease;
+    }
+    .metric-box:hover {
+        transform: translateY(-5px);
+        box-shadow: 0px 8px 20px rgba(0,0,0,0.12);
+    }
+    .metric-box h4 {
+        color: #2e7d32;
+        margin-bottom: 1rem;
+        font-size: 1.3rem;
+    }
+    .metric-value {
+        font-size: 1.8rem;
+        font-weight: 700;
+        color: #1b5e20;
+        margin: 0.5rem 0;
+    }
+    
+    /* Info boxes */
+    .info-card {
+        background: white;
+        border-radius: 12px;
+        padding: 20px;
+        box-shadow: 0 2px 8px rgba(0,0,0,0.06);
+        margin: 1rem 0;
+        border-left: 4px solid #4caf50;
+    }
+    
+    /* Select box styling */
+    .stSelectbox > div > div {
+        background-color: white;
+        border-radius: 10px;
+        border: 2px solid #c5e1a5;
+    }
+    
+    /* Success message */
+    .success-banner {
+        background: linear-gradient(135deg, #4caf50 0%, #81c784 100%);
+        color: white;
+        padding: 1.5rem;
+        border-radius: 12px;
+        text-align: center;
+        font-size: 1.3rem;
+        font-weight: 600;
+        box-shadow: 0 4px 12px rgba(76, 175, 80, 0.3);
+        margin: 2rem 0;
+    }
+    
+    /* DataFrame styling */
+    .dataframe {
+        border-radius: 10px;
+        overflow: hidden;
+        box-shadow: 0 2px 8px rgba(0,0,0,0.08);
+    }
+    
+    /* Section divider */
+    .divider {
+        height: 2px;
+        background: linear-gradient(90deg, transparent, #4caf50, transparent);
+        margin: 2rem 0;
+    }
+    </style>
+""", unsafe_allow_html=True)
+
+# ========================
+# LOAD DATA
+# ========================
+@st.cache_data
+def load_data():
+    url = f"https://api.data.gov.in/resource/{RESOURCE_ID}?api-key={API_KEY}&format=json&limit=10000"
+    response = requests.get(url)
+    data = response.json()
+    df = pd.DataFrame(data["records"])
+    df.columns = [c.replace(" ", "_") for c in df.columns]
+    df["Arrival_Date"] = pd.to_datetime(df["Arrival_Date"], dayfirst=True)
+    df["avg_price"] = (
+        df[["Min_Price", "Max_Price", "Modal_Price"]].astype(float).mean(axis=1)
+    )
+    df = df.dropna(subset=["Commodity_Code", "avg_price", "Commodity"])
+    return df
+
+df = load_data()
+
+# ========================
+# HELPERS
+# ========================
+def prepare_series(df, code):
+    temp = df[df["Commodity_Code"] == code][["Arrival_Date", "avg_price"]]
+    temp = temp.groupby("Arrival_Date").mean().reset_index()
+    temp = temp.sort_values("Arrival_Date")
+    temp = temp.rename(columns={"Arrival_Date": "timestamp", "avg_price": "value"})
+    return temp
+
+def fill_dates(series_df):
+    all_days = pd.date_range(series_df["timestamp"].min(), series_df["timestamp"].max(), freq="D")
+    series_df = series_df.set_index("timestamp").reindex(all_days).interpolate().reset_index()
+    series_df.columns = ["timestamp", "value"]
+    return series_df
+
+def create_dataset(series, look_back=10):
+    X, y = [], []
+    for i in range(len(series) - look_back):
+        X.append(series[i : i + look_back])
+        y.append(series[i + look_back])
+    return np.array(X), np.array(y)
+
+# ========================
+# UI HEADER
+# ========================
+st.title("🌾 Commodity Price Predictor")
+st.markdown('<p class="subtitle">Predict future commodity prices using Deep Learning (LSTM), Facebook Prophet, and hybrid ensemble modeling</p>', unsafe_allow_html=True)
+
+# ========================
+# SIDEBAR
+# ========================
+with st.sidebar:
+    st.header("📊 Forecast Settings")
+    st.markdown("---")
+    
+    commodity_list = df["Commodity"].unique()
+    selected_commodity = st.selectbox(
+        "🌾 Select Commodity:", 
+        sorted(commodity_list),
+        help="Choose a commodity to forecast"
+    )
+    
+    st.markdown("---")
+    st.subheader("⚙️ Model Configuration")
+    st.info(f"""
+    **Current Settings:**
+    - Forecast Horizon: {FORECAST_HORIZON} days
+    - Look Back Period: {LOOK_BACK} days
+    - Training Epochs: {TRAIN_EPOCHS}
+    - Hybrid Weight: 60% LSTM, 40% Prophet
+    """)
+    
+    st.markdown("---")
+    st.subheader("📈 About the Models")
+    with st.expander("LSTM Neural Network"):
+        st.write("Deep learning model that learns temporal patterns in price data")
+    with st.expander("Prophet"):
+        st.write("Facebook's time series forecasting tool optimized for business data")
+    with st.expander("Hybrid Ensemble"):
+        st.write("Combines both models for improved accuracy and stability")
+
+# ========================
+# MAIN CONTENT
+# ========================
+col1, col2, col3 = st.columns([1, 2, 1])
+with col2:
+    forecast_button = st.button("🔮 Generate Forecast", use_container_width=True)
+
+if forecast_button:
+    code = df[df["Commodity"] == selected_commodity]["Commodity_Code"].iloc[0]
+    
+    # Info card
+    st.markdown(f'<div class="info-card">🔄 Processing <b>{selected_commodity}</b> (Code: {code})...</div>', unsafe_allow_html=True)
+
+    # Prepare and clean data
+    series = fill_dates(prepare_series(df, code))
+    values = series["value"].values.reshape(-1, 1)
+    scaler = MinMaxScaler()
+    scaled = scaler.fit_transform(values)
+    X, y = create_dataset(scaled, LOOK_BACK)
+    X = np.reshape(X, (X.shape[0], X.shape[1], 1))
+
+    # ========================
+    # PROGRESS TRACKING
+    # ========================
+    progress_bar = st.progress(0)
+    status_text = st.empty()
+
+    # ========================
+    # LSTM MODEL
+    # ========================
+    status_text.text("🤖 Training LSTM model...")
+    progress_bar.progress(20)
+    
+    model = Sequential([
+        LSTM(32, input_shape=(LOOK_BACK, 1), activation="tanh"),
+        Dense(1)
+    ])
+    model.compile(optimizer="adam", loss="mse")
+    model.fit(X, y, epochs=TRAIN_EPOCHS, batch_size=16, verbose=0)
+    
+    progress_bar.progress(40)
+    status_text.text("✅ LSTM training complete")
+
+    # Forecast with LSTM
+    last_seq = scaled[-LOOK_BACK:]
+    preds = []
+    for _ in range(FORECAST_HORIZON):
+        pred = model.predict(last_seq.reshape(1, LOOK_BACK, 1), verbose=0)
+        preds.append(pred[0][0])
+        last_seq = np.append(last_seq[1:], pred, axis=0)
+    forecast_lstm = scaler.inverse_transform(np.array(preds).reshape(-1, 1)).flatten()
+
+    # ========================
+    # PROPHET MODEL
+    # ========================
+    status_text.text("📈 Training Prophet model...")
+    progress_bar.progress(60)
+    
+    prophet_df = series.rename(columns={"timestamp": "ds", "value": "y"})
+    model_prophet = Prophet()
+    model_prophet.fit(prophet_df)
+    future = model_prophet.make_future_dataframe(periods=FORECAST_HORIZON)
+    forecast_prophet = model_prophet.predict(future)
+    prophet_values = forecast_prophet["yhat"].tail(FORECAST_HORIZON).values
+    
+    progress_bar.progress(80)
+    status_text.text("✅ Prophet training complete")
+
+    # ========================
+    # HYBRID (FINAL) FORECAST
+    # ========================
+    status_text.text("🌿 Generating hybrid forecast...")
+    progress_bar.progress(90)
+    
+    final_pred = 0.6 * forecast_lstm + 0.4 * prophet_values
+    
+    progress_bar.progress(100)
+    status_text.text("✅ Forecast complete!")
+    
+    st.markdown('<div class="divider"></div>', unsafe_allow_html=True)
+
+    # ========================
+    # VISUALIZATION
+    # ========================
+    st.subheader("📊 Forecast Visualization")
+    
+    # Create tabs for different views
+    tab1, tab2, tab3 = st.tabs(["📈 Hybrid Forecast", "🔍 Model Comparison", "📉 Historical Context"])
+    
+    with tab1:
+        fig_final = go.Figure()
+        fig_final.add_trace(go.Scatter(
+            y=final_pred, 
+            mode="lines+markers", 
+            name="Hybrid Forecast", 
+            line=dict(color="#2e7d32", width=3),
+            marker=dict(size=6),
+            fill='tozeroy',
+            fillcolor='rgba(46, 125, 50, 0.1)'
+        ))
+        fig_final.update_layout(
+            title=f"🌾 30-Day Hybrid Forecast for {selected_commodity}",
+            xaxis_title="Days Ahead", 
+            yaxis_title="Predicted Price (₹)",
+            plot_bgcolor="white", 
+            hovermode="x unified",
+            height=500,
+            font=dict(size=12)
+        )
+        st.plotly_chart(fig_final, use_container_width=True)
+    
+    with tab2:
+        fig_compare = go.Figure()
+        fig_compare.add_trace(go.Scatter(
+            y=forecast_lstm, 
+            mode="lines", 
+            name="LSTM", 
+            line=dict(color="#1976d2", dash="dot")
+        ))
+        fig_compare.add_trace(go.Scatter(
+            y=prophet_values, 
+            mode="lines", 
+            name="Prophet", 
+            line=dict(color="#f57c00", dash="dash")
+        ))
+        fig_compare.add_trace(go.Scatter(
+            y=final_pred, 
+            mode="lines+markers", 
+            name="Hybrid Final", 
+            line=dict(color="#2e7d32", width=3)
+        ))
+        fig_compare.update_layout(
+            title="Model Comparison",
+            xaxis_title="Days Ahead", 
+            yaxis_title="Predicted Price (₹)",
+            plot_bgcolor="white", 
+            hovermode="x unified",
+            height=500
+        )
+        st.plotly_chart(fig_compare, use_container_width=True)
+    
+    with tab3:
+        # Historical + Forecast
+        fig_hist = go.Figure()
+        hist_days = min(90, len(values))
+        fig_hist.add_trace(go.Scatter(
+            y=values[-hist_days:].flatten(),
+            mode="lines",
+            name="Historical Prices",
+            line=dict(color="#616161")
+        ))
+        fig_hist.add_trace(go.Scatter(
+            y=final_pred,
+            mode="lines",
+            name="Forecast",
+            line=dict(color="#2e7d32", width=2)
+        ))
+        fig_hist.update_layout(
+            title=f"Historical Prices (Last {hist_days} days) + Forecast",
+            xaxis_title="Time Period",
+            yaxis_title="Price (₹)",
+            plot_bgcolor="white",
+            hovermode="x unified",
+            height=500
+        )
+        st.plotly_chart(fig_hist, use_container_width=True)
+
+    st.markdown('<div class="divider"></div>', unsafe_allow_html=True)
+
+    # ========================
+    # METRICS
+    # ========================
+    st.subheader("📊 Model Performance Metrics")
+    
+    # Calculate metrics (comparing last 30 days if available)
+    if len(values) >= FORECAST_HORIZON:
+        mae_lstm = mean_absolute_error(values[-FORECAST_HORIZON:], forecast_lstm[-FORECAST_HORIZON:])
+        rmse_lstm = math.sqrt(mean_squared_error(values[-FORECAST_HORIZON:], forecast_lstm[-FORECAST_HORIZON:]))
+        mae_prophet = mean_absolute_error(values[-FORECAST_HORIZON:], prophet_values)
+        rmse_prophet = math.sqrt(mean_squared_error(values[-FORECAST_HORIZON:], prophet_values))
+    else:
+        mae_lstm = rmse_lstm = mae_prophet = rmse_prophet = 0
+
+    col1, col2, col3 = st.columns(3)
+    
+    with col1:
+        st.markdown(f'''
+        <div class="metric-box">
+            <h4>🤖 LSTM Model</h4>
+            <div class="metric-value">MAE: ₹{mae_lstm:.2f}</div>
+            <div>RMSE: ₹{rmse_lstm:.2f}</div>
+        </div>
+        ''', unsafe_allow_html=True)
+    
+    with col2:
+        st.markdown(f'''
+        <div class="metric-box">
+            <h4>📈 Prophet Model</h4>
+            <div class="metric-value">MAE: ₹{mae_prophet:.2f}</div>
+            <div>RMSE: ₹{rmse_prophet:.2f}</div>
+        </div>
+        ''', unsafe_allow_html=True)
+    
+    with col3:
+        avg_pred = np.mean(final_pred)
+        min_pred = np.min(final_pred)
+        max_pred = np.max(final_pred)
+        st.markdown(f'''
+        <div class="metric-box">
+            <h4>🌿 Hybrid Forecast</h4>
+            <div class="metric-value">Avg: ₹{avg_pred:.2f}</div>
+            <div>Range: ₹{min_pred:.2f} - ₹{max_pred:.2f}</div>
+        </div>
+        ''', unsafe_allow_html=True)
+
+    st.markdown('<div class="divider"></div>', unsafe_allow_html=True)
+
+    # ========================
+    # FORECAST TABLE
+    # ========================
+    st.subheader("📅 Detailed Forecast Data")
+    
+    forecast_df = pd.DataFrame({
+        "Day": np.arange(1, FORECAST_HORIZON + 1),
+        "LSTM_Forecast": forecast_lstm,
+        "Prophet_Forecast": prophet_values,
+        "Final_Hybrid": final_pred
+    })
+
+    st.dataframe(
+        forecast_df.style.format({
+            "LSTM_Forecast": "₹{:.2f}",
+            "Prophet_Forecast": "₹{:.2f}",
+            "Final_Hybrid": "₹{:.2f}"
+        }).background_gradient(subset=["Final_Hybrid"], cmap="Greens"),
+        use_container_width=True,
+        height=400
+    )
+
+    # ========================
+    # SUMMARY & DOWNLOAD
+    # ========================
+    st.markdown(f'''
+    <div class="success-banner">
+        🎯 Average Predicted Price (Hybrid): ₹{avg_pred:.2f}
+    </div>
+    ''', unsafe_allow_html=True)
+
+    filename = f"{selected_commodity.replace(' ', '_')}_{code}_forecast.csv"
+    forecast_df.to_csv(os.path.join(FORECAST_DIR, filename), index=False)
+    
+    col1, col2, col3 = st.columns([1, 1, 1])
+    with col2:
+        st.download_button(
+            "📥 Download Forecast CSV", 
+            data=forecast_df.to_csv(index=False), 
+            file_name=filename,
+            use_container_width=True
+        )
+
+# ========================
+# FOOTER
+# ========================
+st.markdown('<div class="divider"></div>', unsafe_allow_html=True)
+st.markdown("""
+<div style="text-align: center; color: #558b2f; padding: 2rem;">
+    <p>🌱 Powered by LSTM Neural Networks & Facebook Prophet | Data from Government of India API</p>
+</div>
+""", unsafe_allow_html=True)