Update app.py

app.py

@@ -29,7 +29,7 @@ import pydeck as pdk
 import math
 from sklearn.linear_model import LinearRegression
 
-# Page configuration with custom theme
+# Page configuration
 st.set_page_config(
     page_title="سامانه هوشمند پایش مزارع نیشکر دهخدا",
     page_icon="🌿",
@@ -37,7 +37,7 @@ st.set_page_config(
     initial_sidebar_state="expanded"
 )
 
-# Custom CSS with modern design and animations
+# Custom CSS with cascading panels and eye-catching reflexes
 st.markdown("""
 <style>
     @import url('https://fonts.googleapis.com/css2?family=Vazirmatn:wght@100;200;300;400;500;600;700;800;900&display=swap');
@@ -48,39 +48,38 @@ st.markdown("""
     
     /* Main container styling */
     .main {
-        background: linear-gradient(135deg, #f5f7fa 0%, #e4e9f2 100%);
+        background: linear-gradient(135deg, #f0f4f8 0%, #d9e2ec 100%);
     }
     
     /* Header styling */
     .main-header {
         background: linear-gradient(90deg, #2ecc71 0%, #27ae60 100%);
-        padding: 1.5rem;
-        border-radius: 12px;
-        box-shadow: 0 8px 32px rgba(0, 0, 0, 0.1);
+        padding: 2rem;
+        border-radius: 15px;
+        box-shadow: 0 10px 30px rgba(0, 0, 0, 0.2);
         margin-bottom: 2rem;
         position: relative;
         overflow: hidden;
-        animation: header-glow 3s infinite alternate;
+        animation: headerPulse 3s infinite ease-in-out;
     }
     
-    @keyframes header-glow {
-        0% { box-shadow: 0 8px 32px rgba(46, 204, 113, 0.1); }
-        100% { box-shadow: 0 8px 32px rgba(46, 204, 113, 0.3); }
+    @keyframes headerPulse {
+        0% { box-shadow: 0 10px 30px rgba(46, 204, 113, 0.2); }
+        50% { box-shadow: 0 15px 40px rgba(46, 204, 113, 0.4); }
+        100% { box-shadow: 0 10px 30px rgba(46, 204, 113, 0.2); }
     }
     
     .main-header h1 {
         color: white;
         font-weight: 700;
         margin: 0;
-        position: relative;
-        z-index: 1;
+        text-shadow: 2px 2px 5px rgba(0, 0, 0, 0.2);
     }
     
     .main-header p {
-        color: rgba(255, 255, 255, 0.8);
+        color: rgba(255, 255, 255, 0.9);
         margin: 0;
-        position: relative;
-        z-index: 1;
+        font-size: 1.1rem;
     }
     
     /* Metric card styling */
@@ -88,29 +87,66 @@ st.markdown("""
         display: flex;
         justify-content: space-around;
         flex-wrap: wrap;
-        gap: 20px;
+        gap: 25px;
         padding: 20px;
     }
     
     .metric-card {
         background: linear-gradient(135deg, #3498db 0%, #2980b9 100%);
-        border-radius: 15px;
-        padding: 20px;
-        width: 220px;
+        border-radius: 20px;
+        padding: 25px;
+        width: 240px;
         text-align: center;
         color: white;
-        box-shadow: 0 10px 20px rgba(0,0,0,0.2);
-        transition: transform 0.3s ease, box-shadow 0.3s ease;
+        box-shadow: 0 10px 25px rgba(0, 0, 0, 0.15);
+        transition: all 0.4s ease;
+        position: relative;
+        overflow: hidden;
     }
     
     .metric-card:hover {
-        transform: translateY(-10px);
-        box-shadow: 0 15px 30px rgba(0,0,0,0.3);
+        transform: translateY(-10px) scale(1.05);
+        box-shadow: 0 15px 35px rgba(0, 0, 0, 0.25);
+    }
+    
+    .metric-card::before {
+        content: '';
+        position: absolute;
+        top: -50%;
+        left: -50%;
+        width: 200%;
+        height: 200%;
+        background: rgba(255, 255, 255, 0.1);
+        transform: rotate(30deg);
+        transition: all 0.4s ease;
+    }
+    
+    .metric-card:hover::before {
+        top: 100%;
+        left: 100%;
+    }
+    
+    .metric-icon { font-size: 2.8rem; margin-bottom: 15px; animation: iconBounce 2s infinite; }
+    .metric-value { font-size: 2.2rem; font-weight: 700; text-shadow: 1px 1px 3px rgba(0, 0, 0, 0.2); }
+    .metric-label { font-size: 1.1rem; opacity: 0.9; }
+    
+    @keyframes iconBounce {
+        0%, 100% { transform: translateY(0); }
+        50% { transform: translateY(-10px); }
     }
     
-    .metric-icon { font-size: 2.5rem; margin-bottom: 10px; }
-    .metric-value { font-size: 2rem; font-weight: 700; }
-    .metric-label { font-size: 1rem; opacity: 0.9; }
+    /* Dropdown menu styling */
+    .stSelectbox {
+        background: white;
+        border-radius: 12px;
+        padding: 10px;
+        box-shadow: 0 5px 15px rgba(0, 0, 0, 0.1);
+        transition: all 0.3s ease;
+    }
+    
+    .stSelectbox:hover {
+        box-shadow: 0 8px 20px rgba(0, 0, 0, 0.15);
+    }
     
     /* Navigation menu styling */
     .st-emotion-cache-1lcbz7b {
@@ -119,38 +155,59 @@ st.markdown("""
         margin-bottom: 20px !important;
     }
     
-    .st-emotion-cache-1lcbz7b .st-emotion-cache-1j7d69d {
+    .st-emotion-cache-1j7d69d {
         --hover-color: #e9f7ef !important;
-        border-radius: 10px !important;
+        border-radius: 12px !important;
         font-size: 16px !important;
         text-align: center !important;
         margin: 0 !important;
+        transition: all 0.3s ease;
     }
     
-    .st-emotion-cache-1lcbz7b .st-emotion-cache-1j7d69d:hover {
+    .st-emotion-cache-1j7d69d:hover {
         background-color: #e9f7ef !important;
+        transform: translateY(-2px);
     }
     
-    .st-emotion-cache-1lcbz7b .st-emotion-cache-1j7d69d[data-selected="true"] {
+    .st-emotion-cache-1j7d69d[data-selected="true"] {
         background-color: #2ecc71 !important;
         color: white !important;
         font-weight: 600 !important;
+        box-shadow: 0 5px 15px rgba(46, 204, 113, 0.3);
     }
     
     /* Button styling */
     .stButton>button {
         border-radius: 50px;
-        padding: 0.5rem 1.5rem;
+        padding: 0.7rem 2rem;
         font-weight: 600;
-        transition: all 0.3s ease;
-        border: none;
         background: linear-gradient(90deg, #2ecc71 0%, #27ae60 100%);
         color: white;
+        border: none;
+        transition: all 0.3s ease;
     }
     
     .stButton>button:hover {
-        transform: translateY(-2px);
-        box-shadow: 0 4px 12px rgba(0, 0, 0, 0.1);
+        transform: translateY(-3px);
+        box-shadow: 0 8px 20px rgba(46, 204, 113, 0.3);
+    }
+    
+    /* Tabs styling */
+    .stTabs [data-baseweb="tab-list"] {
+        gap: 10px;
+    }
+    
+    .stTabs [data-baseweb="tab"] {
+        border-radius: 8px 8px 0 0;
+        padding: 12px 20px;
+        background-color: #f8f9fa;
+        transition: all 0.3s ease;
+    }
+    
+    .stTabs [aria-selected="true"] {
+        background-color: #2ecc71 !important;
+        color: white !important;
+        box-shadow: 0 5px 15px rgba(46, 204, 113, 0.2);
     }
     
     /* Footer styling */
@@ -159,10 +216,11 @@ st.markdown("""
         left: 0;
         bottom: 0;
         width: 100%;
-        background-color: #2ecc71;
+        background: linear-gradient(90deg, #2ecc71 0%, #27ae60 100%);
         color: white;
         text-align: center;
-        padding: 10px 0;
+        padding: 15px 0;
+        box-shadow: 0 -5px 15px rgba(0, 0, 0, 0.1);
     }
 </style>
 """, unsafe_allow_html=True)
@@ -186,7 +244,6 @@ def load_farm_data():
             'رطوبت استاندارد قبلی': 'PreviousStandardMoisture', 'چاهک 1': 'Well1', 'تاریخ قرائت': 'Well1Date',
             'چاهک 2': 'Well2', 'تاریخ قرائت.1': 'Well2Date'
         }, inplace=True)
-        # Convert numeric columns to float and handle NaN
         numeric_cols = ['Area', 'CurrentHeight', 'PreviousHeight', 'CurrentGrowth', 'PreviousGrowth', 
                         'CurrentNitrogen', 'PreviousNitrogen', 'CurrentMoisture', 'PreviousMoisture', 
                         'Station1', 'Station2', 'Station3', 'Station4', 'Station5', 'Well1', 'Well2']
@@ -278,6 +335,15 @@ def create_ee_map(farm_id, date_str, layer_type="NDVI"):
         elif layer_type == "NDMI":
             index = s2.normalizedDifference(['B8', 'B11']).rename('NDMI')
             viz_params = {'min': -0.5, 'max': 0.5, 'palette': ['#8b0000', '#ff8c00', '#00ced1', '#00b7eb', '#00008b']}
+        elif layer_type == "EVI":
+            nir = s2.select('B8')
+            red = s2.select('B4')
+            blue = s2.select('B2')
+            index = nir.subtract(red).multiply(2.5).divide(nir.add(red.multiply(6)).subtract(blue.multiply(7.5)).add(1)).rename('EVI')
+            viz_params = {'min': 0, 'max': 1, 'palette': ['#d73027', '#f46d43', '#fdae61', '#fee08b', '#4caf50']}
+        elif layer_type == "NDWI":
+            index = s2.normalizedDifference(['B3', 'B8']).rename('NDWI')
+            viz_params = {'min': -0.5, 'max': 0.5, 'palette': ['#00008b', '#00b7eb', '#add8e6', '#fdae61', '#d73027']}
         map_id_dict = ee.Image(index).getMapId(viz_params)
         folium.TileLayer(
             tiles=map_id_dict['tile_fetcher'].url_format,
@@ -301,9 +367,30 @@ def generate_real_growth_data(selected_variety="all", selected_age="all"):
     if selected_age != "all":
         filtered_farms = filtered_farms[filtered_farms['Age'] == selected_age]
     
+    farm_growth_data = []
     weeks = filtered_farms['Week'].unique()
-    avg_heights = [filtered_farms[filtered_farms['Week'] == week]['CurrentHeight'].mean() for week in weeks]
-    return {'weeks': weeks, 'heights': avg_heights}
+    for farm_id in filtered_farms['Farm_ID'].unique():
+        farm_data = filtered_farms[filtered_farms['Farm_ID'] == farm_id]
+        growth_data = {
+            'farm_id': farm_id,
+            'variety': farm_data['Variety'].iloc[0] if not farm_data.empty else 'Unknown',
+            'age': farm_data['Age'].iloc[0] if not farm_data.empty else 'Unknown',
+            'weeks': weeks,
+            'heights': [farm_data[farm_data['Week'] == week]['CurrentHeight'].mean() if not farm_data[farm_data['Week'] == week].empty else 0 for week in weeks]
+        }
+        farm_growth_data.append(growth_data)
+    
+    if farm_growth_data:
+        avg_heights = []
+        for week in weeks:
+            week_heights = [farm['heights'][list(weeks).index(week)] for farm in farm_growth_data if farm['heights'][list(weeks).index(week)] > 0]
+            avg_heights.append(round(sum(week_heights) / len(week_heights)) if week_heights else 0)
+        avg_growth_data = {'farm_id': 'میانگین', 'variety': 'همه', 'age': 'همه', 'weeks': weeks, 'heights': avg_heights}
+        return {'individual': farm_growth_data, 'average': avg_growth_data}
+    return {
+        'individual': [],
+        'average': {'farm_id': 'میانگین', 'variety': 'همه', 'age': 'همه', 'weeks': weeks, 'heights': [0] * len(weeks)}
+    }
 
 # Initialize Earth Engine and load data
 ee_initialized = initialize_earth_engine()
@@ -323,7 +410,7 @@ if 'heights_df' not in st.session_state:
 # Main header
 st.markdown('<div class="main-header">', unsafe_allow_html=True)
 st.markdown('<h1>سامانه هوشمند پایش مزارع نیشکر دهخدا</h1>', unsafe_allow_html=True)
-st.markdown('<p>پلتفرم جامع مدیریت، پایش و تحلیل داده‌های مزارع نیشکر</p>', unsafe_allow_html=True)
+st.markdown('<p>پلتفرم جامع مدیریت، پایش و تحلیل داده‌های مزارع نیشکر با فناوری پیشرفته</p>', unsafe_allow_html=True)
 st.markdown('</div>', unsafe_allow_html=True)
 
 # Navigation menu
@@ -337,7 +424,7 @@ selected = option_menu(
     styles={
         "container": {"padding": "0!important", "background-color": "transparent", "margin-bottom": "20px"},
         "icon": {"color": "#2ecc71", "font-size": "18px"}, 
-        "nav-link": {"font-size": "16px", "text-align": "center", "margin":"0px", "--hover-color": "#e9f7ef", "border-radius": "10px"},
+        "nav-link": {"font-size": "16px", "text-align": "center", "margin":"0px", "--hover-color": "#e9f7ef", "border-radius": "12px"},
         "nav-link-selected": {"background-color": "#2ecc71", "color": "white", "font-weight": "600"},
     }
 )
@@ -380,25 +467,61 @@ if selected == "داشبورد":
     
     st.markdown('</div>', unsafe_allow_html=True)
     
-    st.markdown("### نمودار رشد هفتگی")
-    growth_data = generate_real_growth_data()
-    fig = px.line(x=growth_data['weeks'], y=growth_data['heights'], title="رشد هفتگی", labels={'x': 'هفته', 'y': 'ارتفاع (سانتی‌متر)'})
-    st.plotly_chart(fig, use_container_width=True)
+    tab1, tab2, tab3, tab4 = st.tabs(["نمای کلی", "نقشه مزارع", "نمودارها", "داده‌ها"])
+    
+    with tab1:
+        st.subheader("توزیع واریته‌ها و سن محصول")
+        col1, col2 = st.columns(2)
+        with col1:
+            fig = px.pie(farm_df['Variety'].value_counts().reset_index(), values='count', names='Variety', title='توزیع واریته‌ها')
+            st.plotly_chart(fig, use_container_width=True)
+        with col2:
+            fig = px.pie(farm_df['Age'].value_counts().reset_index(), values='count', names='Age', title='توزیع سن محصول')
+            st.plotly_chart(fig, use_container_width=True)
+        st_lottie(lottie_farm, height=200, key="farm_animation")
+    
+    with tab2:
+        if not coordinates_df.empty:
+            m = folium.Map(location=[31.45, 48.72], zoom_start=12)
+            for _, farm in coordinates_df.iterrows():
+                folium.Marker([farm['Latitude'], farm['Longitude']], popup=f'مزرعه {farm["Farm_ID"]}').add_to(m)
+            folium_static(m, width=1000, height=600)
+    
+    with tab3:
+        st.subheader("نمودار رشد")
+        col1, col2 = st.columns(2)
+        with col1:
+            variety = st.selectbox("انتخاب واریته", ["all"] + list(farm_df['Variety'].unique()), key="variety_chart")
+        with col2:
+            age = st.selectbox("انتخاب سن", ["all"] + list(farm_df['Age'].unique()), key="age_chart")
+        growth_data = generate_real_growth_data(variety, age)
+        fig = go.Figure()
+        for farm_data in growth_data['individual'][:5]:
+            fig.add_trace(go.Scatter(x=farm_data['weeks'], y=farm_data['heights'], mode='lines+markers', name=f"مزرعه {farm_data['farm_id']}"))
+        fig.update_layout(title='رشد هفتگی مزارع', xaxis_title='هفته', yaxis_title='ارتفاع (سانتی‌متر)')
+        st.plotly_chart(fig, use_container_width=True)
+    
+    with tab4:
+        st.subheader("داده‌های مزارع")
+        st.dataframe(farm_df, use_container_width=True)
 
 # Map Page
 elif selected == "نقشه مزارع":
     st.markdown("## نقشه مزارع با شاخص‌های ماهواره‌ای")
-    farm_id = st.selectbox("انتخاب مزرعه", coordinates_df['Farm_ID'].tolist())
-    date = st.date_input("انتخاب تاریخ", datetime.now())
-    layer_type = st.selectbox("انتخاب شاخص", ["NDVI", "NDMI"])
-    if st.button("تولید نقشه"):
-        with st.spinner('در حال تولید نقشه...'):
-            m = create_ee_map(farm_id, date.strftime('%Y-%m-%d'), layer_type)
-            if m:
-                folium_static(m, width=800, height=600)
-                st.success(f"نقشه {layer_type} برای مزرعه {farm_id} تولید شد.")
-            else:
-                st.error("خطا در تولید نقشه.")
+    col1, col2 = st.columns([1, 3])
+    with col1:
+        farm_id = st.selectbox("انتخاب مزرعه", coordinates_df['Farm_ID'].tolist())
+        date = st.date_input("انتخاب تاریخ", datetime.now())
+        layer_type = st.selectbox("انتخاب شاخص", ["NDVI", "NDMI", "EVI", "NDWI"])
+        if st.button("تولید نقشه"):
+            with st.spinner('در حال تولید نقشه...'):
+                m = create_ee_map(farm_id, date.strftime('%Y-%m-%d'), layer_type)
+                if m:
+                    st.session_state['last_map'] = m
+                    st.success(f"نقشه {layer_type} تولید شد.")
+    with col2:
+        if 'last_map' in st.session_state:
+            folium_static(st.session_state['last_map'], width=800, height=600)
 
 # Data Entry Page
 elif selected == "ورود اطلاعات":
@@ -406,14 +529,22 @@ elif selected == "ورود اطلاعات":
     tab1, tab2 = st.tabs(["ورود دستی", "آپلود فایل"])
     
     with tab1:
-        week = st.selectbox("انتخاب هفته", [str(i) for i in range(1, 23)])
-        day = st.selectbox("انتخاب روز", day_df['Day'].unique())
+        col1, col2 = st.columns(2)
+        with col1:
+            week = st.selectbox("انتخاب هفته", [str(i) for i in range(1, 23)], key="week_entry")
+        with col2:
+            day = st.selectbox("انتخاب روز", day_df['Day'].unique(), key="day_entry")
         filtered_farms = farm_df[farm_df['Week'] == int(week)]
         if not filtered_farms.empty:
             data_key = f"data_{week}_{day}"
             if data_key not in st.session_state:
                 st.session_state[data_key] = pd.DataFrame({
                     'Farm_ID': filtered_farms['Farm_ID'],
+                    'Station1': [0] * len(filtered_farms),
+                    'Station2': [0] * len(filtered_farms),
+                    'Station3': [0] * len(filtered_farms),
+                    'Station4': [0] * len(filtered_farms),
+                    'Station5': [0] * len(filtered_farms),
                     'CurrentHeight': [0.0] * len(filtered_farms),
                     'CurrentMoisture': [0.0] * len(filtered_farms)
                 })
@@ -427,7 +558,7 @@ elif selected == "ورود اطلاعات":
         if uploaded_file:
             try:
                 df = pd.read_csv(uploaded_file) if uploaded_file.name.endswith('.csv') else pd.read_excel(uploaded_file)
-                numeric_cols = ['CurrentHeight', 'CurrentMoisture']
+                numeric_cols = ['CurrentHeight', 'CurrentMoisture', 'Station1', 'Station2', 'Station3', 'Station4', 'Station5']
                 for col in numeric_cols:
                     if col in df.columns:
                         df[col] = pd.to_numeric(df[col], errors='coerce').fillna(0)
@@ -441,17 +572,48 @@ elif selected == "ورود اطلاعات":
 # Data Analysis Page
 elif selected == "تحلیل داده‌ها":
     st.markdown("## تحلیل هوشمند داده‌ها")
-    growth_data = generate_real_growth_data()
-    fig = go.Figure()
-    fig.add_trace(go.Scatter(x=growth_data['weeks'], y=growth_data['heights'], mode='lines+markers', name='رشد'))
-    fig.update_layout(title='رشد هفتگی', xaxis_title='هفته', yaxis_title='ارتفاع (سانتی‌متر)')
-    st.plotly_chart(fig, use_container_width=True)
+    tab1, tab2, tab3, tab4 = st.tabs(["تحلیل رشد", "مقایسه واریته‌ها", "تحلیل رطوبت", "پیش‌بینی"])
+    
+    with tab1:
+        col1, col2 = st.columns(2)
+        with col1:
+            variety = st.selectbox("انتخاب واریته", ["all"] + list(farm_df['Variety'].unique()), key="variety_analysis")
+        with col2:
+            age = st.selectbox("انتخاب سن", ["all"] + list(farm_df['Age'].unique()), key="age_analysis")
+        growth_data = generate_real_growth_data(variety, age)
+        fig = go.Figure()
+        for farm_data in growth_data['individual'][:5]:
+            fig.add_trace(go.Scatter(x=farm_data['weeks'], y=farm_data['heights'], mode='lines+markers', name=f"مزرعه {farm_data['farm_id']}"))
+        fig.update_layout(title='رشد هفتگی', xaxis_title='هفته', yaxis_title='ارتفاع (سانتی‌متر)')
+        st.plotly_chart(fig, use_container_width=True)
+    
+    with tab2:
+        fig = px.box(farm_df, x='Variety', y='CurrentHeight', title='مقایسه ارتفاع بر اساس واریته')
+        st.plotly_chart(fig, use_container_width=True)
+    
+    with tab3:
+        fig = px.scatter(farm_df, x='CurrentMoisture', y='CurrentHeight', color='Farm_ID', title='همبستگی رطوبت و ارتفاع')
+        st.plotly_chart(fig, use_container_width=True)
+    
+    with tab4:
+        farm_id = st.selectbox("انتخاب مزرعه", farm_df['Farm_ID'].tolist(), key="predict_farm")
+        farm_data = farm_df[farm_df['Farm_ID'] == farm_id]
+        if len(farm_data) > 1:
+            model = LinearRegression()
+            model.fit(farm_data['Week'].values.reshape(-1, 1), farm_data['CurrentHeight'].values)
+            future_weeks = np.array(range(max(farm_data['Week']) + 1, 30)).reshape(-1, 1)
+            future_heights = model.predict(future_weeks)
+            fig = go.Figure()
+            fig.add_trace(go.Scatter(x=farm_data['Week'], y=farm_data['CurrentHeight'], mode='lines+markers', name='داده‌های واقعی'))
+            fig.add_trace(go.Scatter(x=future_weeks.flatten(), y=future_heights, mode='lines', name='پیش‌بینی'))
+            fig.update_layout(title=f'پیش‌بینی رشد مزرعه {farm_id}', xaxis_title='هفته', yaxis_title='ارتفاع (سانتی‌متر)')
+            st.plotly_chart(fig, use_container_width=True)
 
 # Report Generation Page
 elif selected == "گزارش‌گیری":
     st.markdown("## گزارش‌گیری")
-    report_week = st.selectbox("انتخاب هفته", [str(i) for i in range(1, 23)])
-    report_day = st.selectbox("انتخاب روز", day_df['Day'].unique())
+    report_week = st.selectbox("انتخاب هفته", [str(i) for i in range(1, 23)], key="report_week")
+    report_day = st.selectbox("انتخاب روز", day_df['Day'].unique(), key="report_day")
     report_df = st.session_state.heights_df[
         (st.session_state.heights_df['Week'] == int(report_week)) & 
         (st.session_state.heights_df['Farm_ID'].isin(day_df[day_df['Day'] == report_day]['Farm_ID']))
@@ -459,9 +621,10 @@ elif selected == "گزارش‌گیری":
     if not report_df.empty:
         st.dataframe(report_df)
         csv = report_df.to_csv(index=False).encode('utf-8')
-        st.download_button(label="دانلود گزارش", data=csv, file_name=f"report_week_{report_week}.csv")
+        st.download_button(label="دانلود گزارش", data=csv, file_name=f"report_week_{report_week}_day_{report_day}.csv")
     else:
-        st.warning(f"داده‌ای برای هفته {report_week} یافت نشد.")
+        st.warning(f"داده‌ای برای هفته {report_week} و روز {report_day} یافت نشد.")
+    st_lottie(lottie_report, height=200, key="report_animation")
 
 # Settings Page
 elif selected == "تنظیمات":
@@ -469,10 +632,20 @@ elif selected == "تنظیمات":
     if st.button("بارگذاری مجدد داده‌ها"):
         st.session_state.heights_df = load_farm_data()
         st.success("داده‌ها به‌روزرسانی شدند.")
+    theme = st.selectbox("انتخاب تم", ["سبز (پیش‌فرض)", "آبی"], key="theme_select")
+    if theme == "آبی":
+        st.markdown("""
+        <style>
+            .main-header {background: linear-gradient(90deg, #3498db 0%, #2980b9 100%);}
+            .metric-card {background: linear-gradient(135deg, #3498db 0%, #2980b9 100%);}
+            .stButton>button {background: linear-gradient(90deg, #3498db 0%, #2980b9 100%);}
+            footer {background: linear-gradient(90deg, #3498db 0%, #2980b9 100%);}
+        </style>
+        """, unsafe_allow_html=True)
 
 # Footer
 st.markdown("""
 <footer>
-    <p>&copy; 2025 سامانه هوشمند پایش مزارع نیشکر دهخدا. تمامی حقوق محفوظ است.</p>
+    <p>© 2025 سامانه هوشمند پایش مزارع نیشکر دهخدا. تمامی حقوق محفوظ است.</p>
 </footer>
 """, unsafe_allow_html=True)