File size: 21,638 Bytes
706f4dc
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
091a7e7
 
 
 
 
 
706f4dc
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
091a7e7
 
706f4dc
091a7e7
 
706f4dc
091a7e7
 
706f4dc
 
091a7e7
 
 
 
 
 
 
 
 
706f4dc
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
import streamlit as st
import tensorflow as tf
import joblib
import pandas as pd
import numpy as np
import os
import matplotlib.pyplot as plt
import pickle
import plotly.graph_objects as go
import plotly.express as px
from plotly.subplots import make_subplots

# Dark theme configuration
st.set_page_config(
    page_title="AuraClima - AI Climate Intelligence",
    page_icon="🌍",
    layout="wide",
    initial_sidebar_state="expanded"
)

# Custom CSS for dark theme and styling
st.markdown("""
<style>
    .stApp {
        background: linear-gradient(135deg, #0c1017 0%, #1a1f2e 100%);
        color: #ffffff;
    }
    
    .main-header {
        text-align: center;
        background: linear-gradient(135deg, #1f77b4, #FF7F0E);
        -webkit-background-clip: text;
        -webkit-text-fill-color: transparent;
        background-clip: text;
        font-size: 3.5rem;
        font-weight: 800;
        margin-bottom: 1rem;
        text-shadow: 0 0 30px rgba(31, 119, 180, 0.3);
    }
    
    .subtitle {
        text-align: center;
        color: #FF7F0E;
        font-size: 1.5rem;
        font-style: italic;
        margin-bottom: 2rem;
        text-shadow: 0 0 20px rgba(255, 127, 14, 0.2);
    }
    
    .model-card {
        background: linear-gradient(145deg, #1e2530, #2a3441);
        border-radius: 15px;
        padding: 20px;
        margin: 15px 0;
        border: 1px solid rgba(31, 119, 180, 0.3);
        box-shadow: 0 8px 32px rgba(0, 0, 0, 0.3);
        backdrop-filter: blur(10px);
    }
    
    .metric-container {
        background: linear-gradient(135deg, #1f77b4, #2a9fd6);
        border-radius: 12px;
        padding: 15px;
        text-align: center;
        margin: 10px 0;
        box-shadow: 0 4px 20px rgba(31, 119, 180, 0.4);
    }
    
    .metric-value {
        font-size: 2rem;
        font-weight: bold;
        color: #ffffff;
    }
    
    .metric-label {
        color: #e0e6ed;
        font-size: 0.9rem;
        margin-top: 5px;
    }
    
    .ai-badge {
        background: linear-gradient(45deg, #FF7F0E, #ff9a3c);
        color: white;
        padding: 5px 15px;
        border-radius: 20px;
        font-size: 0.8rem;
        font-weight: bold;
        display: inline-block;
        margin: 5px;
        box-shadow: 0 2px 10px rgba(255, 127, 14, 0.3);
    }
    
    .sidebar .sidebar-content {
        background: linear-gradient(180deg, #1a1f2e, #0c1017);
    }
    
    .stSelectbox > div > div {
        background-color: #2a3441;
        border: 1px solid #1f77b4;
        border-radius: 8px;
    }
    
    .stSlider > div > div {
        background: linear-gradient(90deg, #1f77b4, #FF7F0E);
    }
    
    .stButton > button {
        background: linear-gradient(135deg, #1f77b4, #FF7F0E);
        color: white;
        border: none;
        border-radius: 8px;
        padding: 10px 20px;
        font-weight: bold;
        transition: all 0.3s ease;
    }
    
    .stButton > button:hover {
        transform: translateY(-2px);
        box-shadow: 0 5px 20px rgba(31, 119, 180, 0.4);
    }
    
    .forecast-section {
        background: rgba(31, 119, 180, 0.1);
        border-radius: 15px;
        padding: 20px;
        margin: 20px 0;
        border-left: 4px solid #1f77b4;
    }
</style>
""", unsafe_allow_html=True)


@st.cache_resource
def load_all():
    base = os.path.dirname(__file__)
    models_dir = os.path.join(base, "models")
    data_dir = os.path.join(base, "data")

    # Load models
    model1 = tf.keras.models.load_model(os.path.join(models_dir, "model1.keras"))
    model2 = tf.keras.models.load_model(os.path.join(models_dir, "model2.keras"))
    model3 = tf.keras.models.load_model(os.path.join(models_dir, "model3.keras"))

    # Load scalers
    scaler1 = joblib.load(os.path.join(models_dir, "scaler1.save"))
    scalerX2 = joblib.load(os.path.join(models_dir, "scalerX2.save"))
    scalerY2 = joblib.load(os.path.join(models_dir, "scalerY2.save"))
    scaler3 = joblib.load(os.path.join(models_dir, "scaler3.save"))

    # Load feature columns list for model2
    with open(os.path.join(models_dir, "feature_cols2.list"), "rb") as f:
        feature_cols2 = pickle.load(f)

    # Load CSV data if present
    df_agri = None
    agri_path = os.path.join(data_dir, "Agrofood_co2_emission.csv")
    if os.path.exists(agri_path):
        df_agri = pd.read_csv(agri_path)

    df_co2 = None
    co2_path = os.path.join(data_dir, "CO2_Emissions_1960-2018.csv")
    if os.path.exists(co2_path):
        df_co2 = pd.read_csv(co2_path)
        if 'Country Name' not in df_co2.columns:
            st.error(f"Expected 'Country Name' in CO2 CSV, found: {df_co2.columns.tolist()}")
            df_co2 = None
        else:
            dummies = pd.get_dummies(df_co2['Country Name'], prefix='Country')
            country_features = dummies.columns.tolist()
            df_co2 = pd.concat([df_co2, dummies], axis=1)
    else:
        country_features = None

    return {
            "model1": model1, "model2": model2, "model3": model3,
            "scaler1": scaler1, "scalerX2": scalerX2, "scalerY2": scalerY2, "scaler3": scaler3, # <--- Ensure scaler3 is returned
            "feature_cols2": feature_cols2, "df_agri": df_agri, "df_co2": df_co2,
            "country_features": country_features,
        }



def forecast_model1(model, scaler, recent_values):
    arr = np.array(recent_values).reshape(-1, 1)
    scaled = scaler.transform(arr).flatten()
    inp = scaled.reshape((1, len(scaled), 1))
    scaled_pred = model.predict(inp, verbose=0)[0, 0]
    pred = scaler.inverse_transform([[scaled_pred]])[0, 0]
    return pred


def predict_model2(model, scalerX, scalerY, feature_array):
    X = np.array(feature_array).reshape(1, -1)
    Xs = scalerX.transform(X)
    ys = model.predict(Xs, verbose=0)
    ypred = scalerY.inverse_transform(ys.reshape(-1, 1)).flatten()[0]
    return ypred


def forecast_model3(model, scaler, recent_series, country_vec):
    window = len(recent_series)
    # co2_scaled = scaler.transform(np.array(recent_series).reshape(-1, 1)).flatten()
    co2_col = np.array(recent_series).reshape(window, 1)
    country_mat = np.tile(country_vec.reshape(1, -1), (window, 1))

    # Concatenate raw CO2 values with country vector
    seq = np.concatenate([co2_col, country_mat], axis=1)

    # Reshape input for LSTM
    inp = seq.reshape(1, window, seq.shape[1])

    # Make prediction - model outputs raw, unscaled values
    ypred_raw_output = model.predict(inp, verbose=0).flatten()

    # --- PREVIOUSLY INCORRECT INVERSE TRANSFORM REMOVED ---
    # ypred = scaler.inverse_transform(ypred_scaled.reshape(-1, 1)).flatten()
    # The model's output is already the final, unscaled prediction
    ypred = ypred_raw_output

    return ypred

def create_animated_metric(label, value, icon="🎯"):
    st.markdown(f"""
    <div class="metric-container">
        <div style="font-size: 1.2rem;">{icon}</div>
        <div class="metric-value">{value}</div>
        <div class="metric-label">{label}</div>
    </div>
    """, unsafe_allow_html=True)


def sidebar_nav():
    st.sidebar.markdown("""
    <div style="text-align: center; padding: 20px;">
        <div style="font-size: 4rem;">🌍</div>
        <h1 style="color: #1f77b4; margin: 10px 0;">AuraClima</h1>
        <p style="color: #FF7F0E; font-style: italic; margin-bottom: 20px;">
            "See the unseen, act on the future"
        </p>
        <div class="ai-badge">🤖 AI-Powered</div>
        <div class="ai-badge">⚡ Real-time</div>
    </div>
    """, unsafe_allow_html=True)

    st.sidebar.markdown("---")
    page = st.sidebar.radio("🚀 Navigate", ["🏠 Home", "🌍 Climate Intelligence", "ℹ️ About"],
                            label_visibility="collapsed")
    return page


def home_page():
    # Centered title
    st.markdown('<h1 class="main-header">🌍 AuraClima</h1>', unsafe_allow_html=True)

    # AI Features showcase
    col1, col2, col3 = st.columns(3)

    with col1:
        st.markdown("""
        <div class="model-card">
            <div style="text-align: center;">
                <div style="font-size: 3rem; margin-bottom: 10px;">🌱</div>
                <h3 style="color: #1f77b4;">Agricultural AI</h3>
                <p style="color: #e0e6ed;">LSTM Time Series Forecasting</p>
                <div class="ai-badge">Neural Network</div>
            </div>
        </div>
        """, unsafe_allow_html=True)

    with col2:
        st.markdown("""
        <div class="model-card">
            <div style="text-align: center;">
                <div style="font-size: 3rem; margin-bottom: 10px;">📊</div>
                <h3 style="color: #FF7F0E;">Feature Analysis</h3>
                <p style="color: #e0e6ed;">Multi-variate Regression</p>
                <div class="ai-badge">Deep Learning</div>
            </div>
        </div>
        """, unsafe_allow_html=True)

    with col3:
        st.markdown("""
        <div class="model-card">
            <div style="text-align: center;">
                <div style="font-size: 3rem; margin-bottom: 10px;">💨</div>
                <h3 style="color: #1f77b4;">CO₂ Intelligence</h3>
                <p style="color: #e0e6ed;">Sequence-to-Sequence</p>
                <div class="ai-badge">Advanced LSTM</div>
            </div>
        </div>
        """, unsafe_allow_html=True)

    st.markdown("---")

    st.markdown("""
    <div style="text-align: center; padding: 30px;">
        <h3 style="color: #1f77b4;">🚀 Advanced AI Climate Modeling</h3>
        <p style="color: #e0e6ed; font-size: 1.1rem; max-width: 600px; margin: 0 auto;">
            Leverage cutting-edge machine learning to forecast climate patterns, emissions, and environmental trends. 
            Our AI models process complex data to provide actionable insights for a sustainable future.
        </p>
    </div>
    """, unsafe_allow_html=True)


def create_enhanced_plot(hist_years, series_co2, fut_years, pred3, country):
    # Create subplot with secondary y-axis for better visualization
    fig = make_subplots(
        rows=1, cols=1,
        subplot_titles=[f"🌍 AI Climate Intelligence: {country}"],
        specs=[[{"secondary_y": False}]]
    )

    # Historical data
    fig.add_trace(
        go.Scatter(
            x=hist_years,
            y=series_co2,
            mode='lines+markers',
            name='Historical Emissions',
            line=dict(color='#1f77b4', width=3),
            marker=dict(size=6, color='#1f77b4'),
            hovertemplate='<b>Year:</b> %{x}<br><b>CO₂:</b> %{y:.2f}<extra></extra>'
        )
    )

    # Forecast data
    fig.add_trace(
        go.Scatter(
            x=fut_years,
            y=pred3,
            mode='lines+markers',
            name='AI Forecast',
            line=dict(color='#FF7F0E', width=4, dash='dash'),
            marker=dict(size=8, color='#FF7F0E', symbol='diamond'),
            hovertemplate='<b>Year:</b> %{x}<br><b>Predicted CO₂:</b> %{y:.2f}<extra></extra>'
        )
    )

    # Connection line
    fig.add_trace(
        go.Scatter(
            x=[hist_years[-1], fut_years[0]],
            y=[series_co2[-1], pred3[0]],
            mode='lines',
            name='Transition',
            line=dict(color='#2ca02c', width=2, dash='dot'),
            showlegend=False
        )
    )

    # Update layout with dark theme
    fig.update_layout(
        title=dict(
            text=f"<b>CO₂ Emissions Forecast for {country}</b>",
            x=0.5,
            font=dict(size=18, color='white')
        ),
        xaxis_title="Year",
        yaxis_title="CO₂ Emissions",
        plot_bgcolor='rgba(0,0,0,0)',
        paper_bgcolor='rgba(0,0,0,0)',
        font=dict(color='white'),
        legend=dict(
            bgcolor='rgba(30, 37, 48, 0.8)',
            bordercolor='#1f77b4',
            borderwidth=1
        ),
        hovermode='x unified'
    )

    # Update axes
    fig.update_xaxes(
        gridcolor='rgba(31, 119, 180, 0.2)',
        griddash='dash',
        showgrid=True
    )
    fig.update_yaxes(
        gridcolor='rgba(31, 119, 180, 0.2)',
        griddash='dash',
        showgrid=True
    )

    return fig


def forecast_by_country(data):
    st.markdown('<h2 style="color: #1f77b4; text-align: center;">🌍 Climate Intelligence Dashboard</h2>',
                unsafe_allow_html=True)

    model1, scaler1 = data["model1"], data["scaler1"]
    model2, scalerX2, scalerY2, feature_cols2 = data["model2"], data["scalerX2"], data["scalerY2"], data[
        "feature_cols2"]
    model3, scaler3 = data["model3"], data["scaler3"]
    df_agri, df_co2 = data["df_agri"], data["df_co2"]

    if df_agri is None:
        st.error("🚨 Agricultural dataset not found. Climate Intelligence unavailable.")
        return

    countries = sorted(df_agri['Area'].dropna().unique())

    # Enhanced country selector
    st.markdown("""
    <div style="text-align: center; margin: 20px 0;">
        <h4 style="color: #FF7F0E;">🎯 Select Country for AI Analysis</h4>
    </div>
    """, unsafe_allow_html=True)

    country = st.selectbox("", countries, label_visibility="collapsed")

    if not country:
        return

    df_ct = df_agri[df_agri['Area'] == country].sort_values('Year')
    latest_year = int(df_ct['Year'].max())

    # Create three columns for models
    st.markdown("---")
    st.markdown('<h3 style="color: #1f77b4; text-align: center;">🤖 AI Model Predictions</h3>', unsafe_allow_html=True)

    col1, col2, col3 = st.columns(3)

    # Model 1 - LSTM Forecast
    with col1:
        st.markdown("""
        <div class="forecast-section">
            <h4 style="color: #1f77b4;">🌱 LSTM Time Series</h4>
            <p style="color: #e0e6ed; font-size: 0.9rem;">Neural network analyzing temporal patterns</p>
        </div>
        """, unsafe_allow_html=True)

        inp1 = model1.input_shape
        window1 = inp1[1]
        series1 = df_ct.set_index('Year')['total_emission']
        years1 = sorted(series1.index)

        if len(years1) >= window1:
            recent_vals = series1.loc[years1[-window1:]].values
            with st.spinner("🔄 AI Processing..."):
                pred1 = forecast_model1(model1, scaler1, recent_vals)
            create_animated_metric("Next Year Emission", f"{pred1:.2f}", "🌱")
        else:
            st.info(f"⚠️ Need ≥{window1} years of data")

    # Model 2 - Feature Analysis
    with col2:
        st.markdown("""
        <div class="forecast-section">
            <h4 style="color: #FF7F0E;">📊 Feature Analysis</h4>
            <p style="color: #e0e6ed; font-size: 0.9rem;">Multi-variate regression modeling</p>
        </div>
        """, unsafe_allow_html=True)

        row_latest = df_ct[df_ct['Year'] == latest_year].iloc[0]
        feature_array = []
        for col in feature_cols2:
            if col.startswith("Area_"):
                feature_array.append(1.0 if col == f"Area_{country}" else 0.0)
            else:
                val = row_latest.get(col, 0.0)
                feature_array.append(float(val))

        try:
            with st.spinner("🔄 Analyzing features..."):
                pred2 = predict_model2(model2, scalerX2, scalerY2, feature_array)
            create_animated_metric("Feature Prediction", f"{pred2:.2f}", "📊")
        except Exception as e:
            st.error(f"❌ Model error: {e}")

    # Model 3 - CO2 Intelligence
    with col3:
        st.markdown("""
        <div class="forecast-section">
            <h4 style="color: #1f77b4;">💨 CO₂ Intelligence</h4>
            <p style="color: #e0e6ed; font-size: 0.9rem;">Advanced sequence modeling</p>
        </div>
        """, unsafe_allow_html=True)

        if df_co2 is not None:
            dfc = df_co2[df_co2['Country Name'] == country]
            country_features = data["country_features"]
            country_vec = np.zeros(len(country_features))

            for i, name in enumerate(country_features):
                if name == f"Country_{country}":
                    country_vec[i] = 1
                    break

            if not dfc.empty:
                year_cols = [c for c in dfc.columns if c.isdigit()]
                series_co2 = dfc.iloc[0][year_cols].astype(float).values
                inp3 = model3.input_shape
                window3 = inp3[1]

                if len(series_co2) >= window3:
                    recent3 = series_co2[-window3:]
                    with st.spinner("🔄 CO₂ forecasting..."):
                        pred3 = forecast_model3(model3, scaler3, recent3, country_vec)

                    avg_forecast = np.mean(pred3)
                    create_animated_metric("Avg CO₂ Forecast", f"{avg_forecast:.2f}", "💨")
                else:
                    st.info(f"⚠️ Need ≥{window3} years")
        else:
            st.error("❌ CO₂ data unavailable")

    # Interactive Parameter Tuning
    st.markdown("---")
    st.markdown('<h3 style="color: #FF7F0E; text-align: center;">⚙️ Interactive Parameter Tuning</h3>',
                unsafe_allow_html=True)

    with st.expander("🎛️ Adjust Model Parameters", expanded=False):
        st.markdown("**Modify features to explore different scenarios:**")

        tweaked = []
        cols_numeric = [c for c in feature_cols2 if not c.startswith("Area_")]

        cols = st.columns(2)
        for i, col in enumerate(feature_cols2):
            if col.startswith("Area_"):
                tweaked.append(feature_array[i])
            else:
                series_col = df_agri[col].dropna().astype(float)
                if not series_col.empty:
                    mn, mx = float(series_col.min()), float(series_col.max())
                    default = feature_array[i]
                    slider_val = cols[i % 2].slider(f"🔧 {col}", mn, mx, default, key=f"slider_{col}")
                    tweaked.append(slider_val)
                else:
                    tweaked.append(feature_array[i])

        if st.button("🚀 Run Enhanced Prediction"):
            try:
                with st.spinner("🤖 AI recalculating..."):
                    pred2b = predict_model2(model2, scalerX2, scalerY2, tweaked)
                create_animated_metric("Adjusted Prediction", f"{pred2b:.2f}", "🎯")
            except Exception as e:
                st.error(f"❌ Error: {e}")

    # Enhanced CO2 Visualization
    if df_co2 is not None and not dfc.empty and len(series_co2) >= window3:
        st.markdown("---")
        st.markdown('<h3 style="color: #1f77b4; text-align: center;">📈 Advanced CO₂ Visualization</h3>',
                    unsafe_allow_html=True)

        hist_years = list(map(int, year_cols))
        last_year = hist_years[-1]
        fut_years = [last_year + i + 1 for i in range(len(pred3))]

        # Create enhanced interactive plot
        fig = create_enhanced_plot(hist_years, series_co2, fut_years, pred3, country)
        st.plotly_chart(fig, use_container_width=True)

        # Forecast summary table
        st.markdown('<h4 style="color: #FF7F0E;">📋 Detailed Forecast Summary</h4>', unsafe_allow_html=True)
        forecast_df = pd.DataFrame({
            '🗓️ Year': fut_years,
            '💨 Predicted CO₂': [f"{val:.2f}" for val in pred3],
            '📈 Trend': ['↗️' if i == 0 or pred3[i] > pred3[i - 1] else '↘️' for i in range(len(pred3))]
        })
        st.dataframe(forecast_df, use_container_width=True)


def about_page():
    st.markdown('<h1 class="main-header">🌍 AuraClima</h1>', unsafe_allow_html=True)
    st.markdown('<p class="subtitle">Advanced AI Climate Intelligence Platform</p>', unsafe_allow_html=True)

    st.markdown("""
    <div class="model-card">
        <h3 style="color: #1f77b4;">🎯 Mission</h3>
        <p style="color: #e0e6ed;">
            AuraClima leverages cutting-edge artificial intelligence to forecast climate patterns and emissions,
            empowering decision-makers to "See the unseen, act on the future."
        </p>
    </div>
    """, unsafe_allow_html=True)

    col1, col2 = st.columns(2)

    with col1:
        st.markdown("""
        <div class="model-card">
            <h4 style="color: #FF7F0E;">🤖 Technology Stack</h4>
            <div class="ai-badge">TensorFlow</div>
            <div class="ai-badge">LSTM Networks</div>
            <div class="ai-badge">Neural Networks</div>
            <div class="ai-badge">Time Series</div>
        </div>
        """, unsafe_allow_html=True)

    with col2:
        st.markdown("""
        <div class="model-card">
            <h4 style="color: #1f77b4;">🎨 Brand Identity</h4>
            <p style="color: #e0e6ed;">
                <strong>Primary:</strong> <span style="color: #1f77b4;">Blue (#1f77b4)</span><br>
                <strong>Secondary:</strong> <span style="color: #FF7F0E;">Orange (#FF7F0E)</span>
            </p>
        </div>
        """, unsafe_allow_html=True)

    st.markdown("""
    <div style="text-align: center; margin-top: 30px;">
        <p style="color: #e0e6ed;">
            <strong>Developed by:</strong> Abdullah Imran<br>
            <strong>Contact:</strong> [email protected]
        </p>
    </div>
    """, unsafe_allow_html=True)


# Main Application
def main():
    # Load resources once
    data = load_all()

    # Sidebar navigation
    page = sidebar_nav()

    # Page routing
    if page == "🏠 Home":
        home_page()
    elif page == "🌍 Climate Intelligence":
        forecast_by_country(data)
    elif page == "ℹ️ About":
        about_page()


if __name__ == "__main__":
    main()