Deploy Geo-Intelligent AI System

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+geoai_clean_final_best.keras filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,1011 @@
+
+import os
+import json
+import numpy as np
+import cv2
+import gradio as gr
+import tensorflow as tf
+
+from tensorflow.keras.models import load_model
+from groq import Groq
+
+
+# =========================================================
+# CONFIGURATION
+# =========================================================
+
+MODEL_PATH = "geoai_clean_final_best.keras"
+
+classes = ['AnnualCrop', 'Forest', 'HerbaceousVegetation', 'Highway', 'Industrial', 'Pasture', 'PermanentCrop', 'Residential', 'River', 'SeaLake']
+
+geo_knowledge_base = {'AnnualCrop': {'land_cover_type': 'Agricultural Land', 'description': 'Land primarily used for seasonal crop cultivation and annual agricultural production.', 'characteristics': ['Regular field patterns', 'Seasonal vegetation cycles', 'Managed agricultural activity'], 'environmental_significance': 'Important for food production, crop monitoring, and agricultural land-use assessment.', 'applications': ['Crop monitoring', 'Agricultural planning', 'Land-use assessment']}, 'Forest': {'land_cover_type': 'Natural Vegetation', 'description': 'Land dominated by dense tree cover and forest vegetation.', 'characteristics': ['Dense vegetation', 'Continuous tree canopy', 'High biomass coverage'], 'environmental_significance': 'Important for biodiversity, carbon storage, and ecosystem monitoring.', 'applications': ['Deforestation monitoring', 'Biodiversity assessment', 'Environmental conservation']}, 'HerbaceousVegetation': {'land_cover_type': 'Natural Vegetation', 'description': 'Land dominated by grasses, herbs, and other non-woody vegetation.', 'characteristics': ['Low-growing vegetation', 'Grass-dominated surface', 'Limited tree cover'], 'environmental_significance': 'Important for habitat assessment, vegetation monitoring, and ecosystem analysis.', 'applications': ['Vegetation monitoring', 'Habitat assessment', 'Land-cover mapping']}, 'Highway': {'land_cover_type': 'Transportation Infrastructure', 'description': 'Land containing major road networks and associated transportation infrastructure.', 'characteristics': ['Linear road structures', 'Connected transport routes', 'Artificial surfaces'], 'environmental_significance': 'Useful for infrastructure mapping, urban expansion studies, and accessibility analysis.', 'applications': ['Road network monitoring', 'Infrastructure planning', 'Urban development analysis']}, 'Industrial': {'land_cover_type': 'Built-up Land', 'description': 'Land dominated by factories, warehouses, and large industrial infrastructure.', 'characteristics': ['Large artificial structures', 'Dense built surfaces', 'Industrial facilities'], 'environmental_significance': 'Relevant for urban growth, industrial development, and environmental impact monitoring.', 'applications': ['Industrial mapping', 'Urban planning', 'Environmental impact assessment']}, 'Pasture': {'land_cover_type': 'Agricultural Grassland', 'description': 'Grass-covered land primarily used for livestock grazing.', 'characteristics': ['Open grassland', 'Low vegetation', 'Agricultural land management'], 'environmental_significance': 'Important for livestock management and agricultural land-use monitoring.', 'applications': ['Grazing land assessment', 'Agricultural planning', 'Grassland monitoring']}, 'PermanentCrop': {'land_cover_type': 'Permanent Agricultural Land', 'description': 'Land used for long-term crops such as orchards, vineyards, and plantations.', 'characteristics': ['Persistent crop patterns', 'Organized planting structures', 'Long-term cultivation'], 'environmental_significance': 'Useful for monitoring permanent agricultural systems and long-term land management.', 'applications': ['Plantation monitoring', 'Agricultural mapping', 'Land management']}, 'Residential': {'land_cover_type': 'Built-up Land', 'description': 'Land dominated by houses, buildings, and residential infrastructure.', 'characteristics': ['Dense building patterns', 'Road networks', 'Urban housing structures'], 'environmental_significance': 'Important for urban growth analysis, population-related planning, and land-use monitoring.', 'applications': ['Urban planning', 'Settlement mapping', 'Urban expansion monitoring']}, 'River': {'land_cover_type': 'Water Body', 'description': 'A natural flowing water system visible within the landscape.', 'characteristics': ['Linear water features', 'Continuous flow patterns', 'Natural drainage structures'], 'environmental_significance': 'Important for water-resource management, flood assessment, and ecosystem monitoring.', 'applications': ['Flood monitoring', 'Water-resource assessment', 'Environmental monitoring']}, 'SeaLake': {'land_cover_type': 'Water Body', 'description': 'A large natural or artificial body of standing water.', 'characteristics': ['Large continuous water surface', 'Low surface texture', 'Distinct land-water boundaries'], 'environmental_significance': 'Important for water-resource monitoring and aquatic ecosystem assessment.', 'applications': ['Water-body monitoring', 'Environmental assessment', 'Coastal and lake analysis']}}
+
+
+# =========================================================
+# LOAD FINAL TRAINED MODEL
+# =========================================================
+
+print("Loading GeoAI model...")
+
+xai_model = load_model(
+    MODEL_PATH,
+    compile=False
+)
+
+print("GeoAI model loaded successfully.")
+
+
+# =========================================================
+# GROQ LLM CONNECTION
+# =========================================================
+
+GROQ_API_KEY = os.getenv("GROQ_API_KEY")
+
+if not GROQ_API_KEY:
+    print(
+        "WARNING: GROQ_API_KEY is not configured. "
+        "Image classification will work, but LLM features "
+        "require the Hugging Face Space secret."
+    )
+
+groq_client = Groq(
+    api_key=GROQ_API_KEY or "missing-key"
+)
+
+
+# =========================================================
+# EXACT WORKING GEOAI FUNCTIONS
+# =========================================================
+
+def make_gradcam_heatmap(img_array, model, backbone_name="mobilenetv2_1.00_96"):
+
+    # Get nested MobileNetV2 backbone
+    backbone = model.get_layer(backbone_name)
+
+    # Model that returns final backbone feature maps
+    backbone_feature_model = tf.keras.Model(
+        inputs=backbone.input,
+        outputs=backbone.get_layer("out_relu").output
+    )
+
+    # Get preprocessing layers from full model
+    augmentation_layer = model.get_layer("data_augmentation")
+    resizing_layer = model.get_layer("resizing_1")
+    rescaling_layer = model.get_layer("rescaling_2")
+
+    # Get classification head layers
+    gap_layer = model.get_layer("global_average_pooling2d_2")
+    dropout_1 = model.get_layer("dropout_8")
+    dense_layer = model.get_layer("dense_12")
+    dropout_2 = model.get_layer("dropout_9")
+    output_layer = model.get_layer("dense_13")
+
+    with tf.GradientTape() as tape:
+
+        # Same preprocessing path as the final model
+        x = augmentation_layer(img_array, training=False)
+        x = resizing_layer(x)
+        x = rescaling_layer(x)
+
+        # Extract final convolutional feature maps
+        conv_outputs = backbone_feature_model(x, training=False)
+
+        tape.watch(conv_outputs)
+
+        # Same classification path as the final model
+        predictions = gap_layer(conv_outputs)
+        predictions = dropout_1(predictions, training=False)
+        predictions = dense_layer(predictions)
+        predictions = dropout_2(predictions, training=False)
+        predictions = output_layer(predictions)
+
+        predicted_index = tf.argmax(predictions[0])
+        class_score = predictions[:, predicted_index]
+
+    # Gradient of predicted class with respect to feature maps
+    gradients = tape.gradient(class_score, conv_outputs)
+
+    # Average gradients across spatial dimensions
+    pooled_gradients = tf.reduce_mean(
+        gradients,
+        axis=(0, 1, 2)
+    )
+
+    # Weight feature maps by importance
+    conv_outputs = conv_outputs[0]
+
+    heatmap = tf.reduce_sum(
+        conv_outputs * pooled_gradients,
+        axis=-1
+    )
+
+    # Keep positive influence only
+    heatmap = tf.maximum(heatmap, 0)
+
+    # Normalize to 0–1
+    max_value = tf.reduce_max(heatmap)
+
+    heatmap = tf.where(
+        max_value > 0,
+        heatmap / max_value,
+        heatmap
+    )
+
+    return heatmap.numpy()
+
+
+
+def analyze_satellite_image(image_array, model=xai_model):
+
+    # Ensure image is float32 and normalized to 0–1
+    image_array = np.asarray(image_array, dtype=np.float32)
+
+    if image_array.max() > 1.0:
+        image_array = image_array / 255.0
+
+    # Ensure correct size
+    if image_array.shape[:2] != (64, 64):
+        image_array = cv2.resize(
+            image_array,
+            (64, 64)
+        )
+
+    # Ensure RGB
+    if image_array.ndim == 2:
+        image_array = cv2.cvtColor(
+            image_array,
+            cv2.COLOR_GRAY2RGB
+        )
+
+    if image_array.shape[-1] == 4:
+        image_array = image_array[:, :, :3]
+
+    # Add batch dimension
+    img_batch = np.expand_dims(
+        image_array,
+        axis=0
+    )
+
+    # Prediction
+    probabilities = model.predict(
+        img_batch,
+        verbose=0
+    )[0]
+
+    predicted_index = int(
+        np.argmax(probabilities)
+    )
+
+    predicted_class = classes[
+        predicted_index
+    ]
+
+    confidence = float(
+        probabilities[predicted_index] * 100
+    )
+
+    # Top 3 predictions
+    top3_indices = np.argsort(
+        probabilities
+    )[-3:][::-1]
+
+    top3_predictions = [
+        {
+            "class": classes[i],
+            "confidence": float(
+                probabilities[i] * 100
+            )
+        }
+        for i in top3_indices
+    ]
+
+    # Generate Grad-CAM
+    heatmap = make_gradcam_heatmap(
+        img_batch,
+        model
+    )
+
+    heatmap_resized = cv2.resize(
+        heatmap,
+        (64, 64)
+    )
+
+    # Colored heatmap
+    heatmap_uint8 = np.uint8(
+        255 * heatmap_resized
+    )
+
+    heatmap_colored = cv2.applyColorMap(
+        heatmap_uint8,
+        cv2.COLORMAP_JET
+    )
+
+    heatmap_colored = cv2.cvtColor(
+        heatmap_colored,
+        cv2.COLOR_BGR2RGB
+    )
+
+    # Original image
+    original_uint8 = np.uint8(
+        np.clip(
+            image_array * 255,
+            0,
+            255
+        )
+    )
+
+    # Grad-CAM overlay
+    overlay = cv2.addWeighted(
+        original_uint8,
+        0.6,
+        heatmap_colored,
+        0.4,
+        0
+    )
+
+    # Confidence interpretation
+    if confidence >= 90:
+        confidence_level = "High"
+    elif confidence >= 70:
+        confidence_level = "Moderate"
+    else:
+        confidence_level = "Low"
+
+    # Structured GeoAI output
+    result = {
+        "predicted_class": predicted_class,
+        "confidence": confidence,
+        "confidence_level": confidence_level,
+        "top3_predictions": top3_predictions
+    }
+
+    return result, heatmap_resized, overlay
+
+
+
+def create_structured_geo_analysis(model_result):
+
+    predicted_class = model_result["predicted_class"]
+    knowledge = geo_knowledge_base[predicted_class]
+
+    # Reliability statement
+    confidence = model_result["confidence"]
+
+    if confidence >= 90:
+        reliability = (
+            "The model shows strong confidence in this prediction."
+        )
+    elif confidence >= 70:
+        reliability = (
+            "The prediction is reasonably confident, but alternative "
+            "land-cover classes should also be considered."
+        )
+    else:
+        reliability = (
+            "The prediction has low confidence and should be interpreted "
+            "carefully alongside the alternative predictions."
+        )
+
+    structured_analysis = {
+        "prediction": {
+            "land_cover_class": predicted_class,
+            "confidence_percentage": round(confidence, 2),
+            "confidence_level": model_result["confidence_level"]
+        },
+
+        "geospatial_information": {
+            "land_cover_type": knowledge["land_cover_type"],
+            "description": knowledge["description"],
+            "characteristics": knowledge["characteristics"],
+            "environmental_significance":
+                knowledge["environmental_significance"],
+            "potential_applications": knowledge["applications"]
+        },
+
+        "alternative_predictions": [
+            {
+                "class": item["class"],
+                "confidence_percentage":
+                    round(item["confidence"], 2)
+            }
+            for item in model_result["top3_predictions"][1:]
+        ],
+
+        "explainability": {
+            "method": "Grad-CAM",
+            "interpretation": (
+                "The highlighted regions in the Grad-CAM visualization "
+                "represent areas that most influenced the model's prediction."
+            ),
+            "spatial_resolution_note": (
+                "The explanation identifies broad influential regions "
+                "rather than exact object boundaries."
+            )
+        },
+
+        "reliability_assessment": reliability
+    }
+
+    return structured_analysis
+
+
+
+def ask_geoai_llm(question, structured_output):
+
+    # Convert verified GeoAI output into JSON context
+    geo_context = json.dumps(
+        structured_output,
+        indent=2
+    )
+
+    system_prompt = """
+You are the natural-language reasoning component of a GeoAI system.
+
+You receive verified structured information produced by:
+1. A satellite-image classification model
+2. A confidence analysis module
+3. A Grad-CAM explainability module
+4. A geospatial knowledge base
+
+Rules:
+- Answer using only the supplied GeoAI context.
+- Do not invent geographic locations, coordinates, objects, or facts.
+- Do not change the model's predicted class or confidence.
+- If the requested information is unavailable, clearly say so.
+- Treat Grad-CAM as broad model attention, not exact segmentation.
+- Keep answers clear and concise.
+"""
+
+    user_prompt = f"""
+VERIFIED GEOAI CONTEXT:
+
+{geo_context}
+
+USER QUESTION:
+{question}
+
+Answer the user's question using only the verified context above.
+"""
+
+    response = groq_client.chat.completions.create(
+        model="openai/gpt-oss-20b",
+        messages=[
+            {
+                "role": "system",
+                "content": system_prompt
+            },
+            {
+                "role": "user",
+                "content": user_prompt
+            }
+        ],
+        temperature=0.2,
+        max_completion_tokens=300
+    )
+
+    return response.choices[0].message.content
+
+
+
+def gradio_ask_question(question, structured_state):
+
+    if not question or not question.strip():
+        return "Please enter a question."
+
+    if not structured_state:
+        return "Please upload and analyze a satellite image first."
+
+    answer = ask_geoai_llm(
+        question,
+        structured_state
+    )
+
+    return answer
+
+
+
+def premium_analyze_image(uploaded_image):
+
+    if uploaded_image is None:
+        raise gr.Error(
+            "Upload a satellite image before starting the analysis."
+        )
+
+    image_array = np.asarray(
+        uploaded_image,
+        dtype=np.float32
+    )
+
+    if image_array.max() > 1.0:
+        image_array = image_array / 255.0
+
+    # Core GeoAI analysis
+    model_result, heatmap, overlay = analyze_satellite_image(
+        image_array,
+        model=xai_model
+    )
+
+    # Structured geospatial intelligence
+    structured_output = create_structured_geo_analysis(
+        model_result
+    )
+
+    # Detailed LLM report
+    initial_question = (
+        "Explain what this image shows, how reliable the result is, "
+        "and what the analysis can be used for."
+    )
+
+    llm_explanation = ask_geoai_llm(
+        initial_question,
+        structured_output
+    )
+
+    predicted_class = model_result["predicted_class"]
+    confidence = model_result["confidence"]
+    confidence_level = model_result["confidence_level"]
+
+    # Clean display name
+    display_class = (
+        predicted_class
+        .replace("HerbaceousVegetation", "Herbaceous Vegetation")
+        .replace("PermanentCrop", "Permanent Crop")
+        .replace("AnnualCrop", "Annual Crop")
+        .replace("SeaLake", "Sea / Lake")
+    )
+
+    # Hero prediction card
+    prediction_summary = f"""
+    <div class="prediction-card">
+        <div class="prediction-eyebrow">PRIMARY CLASSIFICATION</div>
+        <div class="prediction-name">{display_class}</div>
+        <div class="prediction-meta">
+            <span class="confidence-pill">{confidence:.2f}% confidence</span>
+            <span class="level-pill">{confidence_level} certainty</span>
+        </div>
+    </div>
+    """
+
+    # Top-3 scores
+    top3_scores = {
+        item["class"]: item["confidence"] / 100
+        for item in model_result["top3_predictions"]
+    }
+
+    # Compact model metadata
+    analysis_meta = f"""
+    <div class="meta-grid">
+        <div class="meta-item">
+            <span class="meta-label">MODEL</span>
+            <strong>Fine-tuned MobileNetV2</strong>
+        </div>
+        <div class="meta-item">
+            <span class="meta-label">EXPLAINABILITY</span>
+            <strong>Grad-CAM</strong>
+        </div>
+        <div class="meta-item">
+            <span class="meta-label">CONFIDENCE</span>
+            <strong>{confidence_level}</strong>
+        </div>
+    </div>
+    """
+
+    return (
+        prediction_summary,
+        uploaded_image,
+        overlay,
+        top3_scores,
+        llm_explanation,
+        analysis_meta,
+        structured_output
+    )
+
+
+
+# =========================================================
+# EXACT PREMIUM DESIGN SYSTEM
+# =========================================================
+
+premium_css = '\n/* =========================================================\n   GEO-INTELLIGENT AI — PREMIUM DESIGN SYSTEM\n   ========================================================= */\n\n:root {\n    --bg-0: #05080d;\n    --bg-1: #081019;\n    --bg-2: #0b1520;\n    --surface: rgba(12, 23, 34, 0.82);\n    --surface-strong: rgba(14, 27, 40, 0.96);\n    --surface-soft: rgba(255, 255, 255, 0.035);\n\n    --border: rgba(148, 184, 207, 0.14);\n    --border-hover: rgba(77, 226, 183, 0.34);\n\n    --text-1: #f4f8fb;\n    --text-2: #a9bac7;\n    --text-3: #708695;\n\n    --accent: #4de2b7;\n    --accent-2: #54b9ff;\n    --accent-soft: rgba(77, 226, 183, 0.12);\n\n    --shadow-lg:\n        0 30px 80px rgba(0, 0, 0, 0.38),\n        0 1px 0 rgba(255, 255, 255, 0.03) inset;\n\n    --radius-xl: 28px;\n    --radius-lg: 22px;\n    --radius-md: 16px;\n}\n\n\n/* ---------- GLOBAL ---------- */\n\n.gradio-container {\n    max-width: 1500px !important;\n    margin: 0 auto !important;\n    padding: 0 28px 60px !important;\n    background:\n        radial-gradient(\n            circle at 12% 8%,\n            rgba(39, 188, 152, 0.10),\n            transparent 28%\n        ),\n        radial-gradient(\n            circle at 88% 12%,\n            rgba(61, 145, 218, 0.10),\n            transparent 26%\n        ),\n        linear-gradient(\n            180deg,\n            var(--bg-0) 0%,\n            var(--bg-1) 48%,\n            #060b11 100%\n        ) !important;\n    color: var(--text-1) !important;\n    min-height: 100vh;\n}\n\nbody {\n    background: var(--bg-0) !important;\n}\n\n\n/* ---------- TOP NAV ---------- */\n\n.top-nav {\n    display: flex;\n    justify-content: space-between;\n    align-items: center;\n    padding: 24px 4px 18px;\n}\n\n.brand-lockup {\n    display: flex;\n    align-items: center;\n    gap: 12px;\n}\n\n.brand-mark {\n    width: 42px;\n    height: 42px;\n    display: grid;\n    place-items: center;\n    border-radius: 13px;\n    background:\n        linear-gradient(\n            135deg,\n            rgba(77, 226, 183, 0.22),\n            rgba(84, 185, 255, 0.14)\n        );\n    border: 1px solid rgba(77, 226, 183, 0.24);\n    font-size: 21px;\n    box-shadow: 0 10px 30px rgba(77, 226, 183, 0.08);\n}\n\n.brand-name {\n    font-size: 16px;\n    font-weight: 750;\n    letter-spacing: -0.02em;\n}\n\n.brand-sub {\n    color: var(--text-3);\n    font-size: 11px;\n    margin-top: 2px;\n    letter-spacing: 0.08em;\n    text-transform: uppercase;\n}\n\n.system-status {\n    display: flex;\n    align-items: center;\n    gap: 9px;\n    padding: 9px 13px;\n    border-radius: 999px;\n    border: 1px solid var(--border);\n    background: rgba(255,255,255,0.025);\n    color: var(--text-2);\n    font-size: 12px;\n}\n\n.status-dot {\n    width: 8px;\n    height: 8px;\n    border-radius: 50%;\n    background: var(--accent);\n    box-shadow: 0 0 14px rgba(77, 226, 183, 0.75);\n}\n\n\n/* ---------- HERO ---------- */\n\n.hero {\n    position: relative;\n    overflow: hidden;\n    padding: 74px 38px 68px;\n    text-align: center;\n    border: 1px solid var(--border);\n    border-radius: 32px;\n    background:\n        linear-gradient(\n            135deg,\n            rgba(77, 226, 183, 0.055),\n            transparent 35%\n        ),\n        linear-gradient(\n            225deg,\n            rgba(84, 185, 255, 0.055),\n            transparent 38%\n        ),\n        rgba(8, 16, 25, 0.76);\n    box-shadow: var(--shadow-lg);\n}\n\n.hero::before {\n    content: "";\n    position: absolute;\n    width: 440px;\n    height: 440px;\n    left: 50%;\n    top: -340px;\n    transform: translateX(-50%);\n    border-radius: 50%;\n    background: rgba(77, 226, 183, 0.13);\n    filter: blur(80px);\n    pointer-events: none;\n}\n\n.hero-kicker {\n    display: inline-flex;\n    align-items: center;\n    gap: 8px;\n    padding: 8px 13px;\n    border-radius: 999px;\n    border: 1px solid rgba(77, 226, 183, 0.18);\n    background: rgba(77, 226, 183, 0.07);\n    color: #8af0d2;\n    font-size: 11px;\n    font-weight: 700;\n    letter-spacing: 0.12em;\n    text-transform: uppercase;\n}\n\n.hero h1 {\n    margin: 24px auto 16px !important;\n    max-width: 950px;\n    color: var(--text-1) !important;\n    font-size: clamp(42px, 6vw, 76px) !important;\n    line-height: 0.98 !important;\n    letter-spacing: -0.055em !important;\n    font-weight: 800 !important;\n}\n\n.hero-gradient {\n    background:\n        linear-gradient(\n            90deg,\n            #ffffff 0%,\n            #8cf2d5 46%,\n            #74c9ff 100%\n        );\n    -webkit-background-clip: text;\n    -webkit-text-fill-color: transparent;\n}\n\n.hero-copy {\n    max-width: 730px;\n    margin: 0 auto;\n    color: var(--text-2);\n    font-size: 17px;\n    line-height: 1.75;\n}\n\n.capability-row {\n    margin-top: 28px;\n    display: flex;\n    justify-content: center;\n    gap: 10px;\n    flex-wrap: wrap;\n}\n\n.capability-chip {\n    padding: 9px 13px;\n    border-radius: 999px;\n    color: var(--text-2);\n    background: rgba(255,255,255,0.035);\n    border: 1px solid var(--border);\n    font-size: 12px;\n}\n\n\n/* ---------- SECTION SYSTEM ---------- */\n\n.section-wrap {\n    margin-top: 28px;\n}\n\n.section-heading {\n    margin: 0 0 14px 4px;\n}\n\n.section-eyebrow {\n    color: var(--accent);\n    font-size: 11px;\n    font-weight: 750;\n    letter-spacing: 0.13em;\n    text-transform: uppercase;\n}\n\n.section-title {\n    margin-top: 7px;\n    color: var(--text-1);\n    font-size: 24px;\n    font-weight: 750;\n    letter-spacing: -0.035em;\n}\n\n.section-copy {\n    margin-top: 7px;\n    color: var(--text-3);\n    font-size: 13px;\n}\n\n\n/* ---------- PREMIUM CARDS ---------- */\n\n.premium-card {\n    border: 1px solid var(--border) !important;\n    border-radius: var(--radius-xl) !important;\n    background:\n        linear-gradient(\n            145deg,\n            rgba(255,255,255,0.025),\n            transparent 42%\n        ),\n        var(--surface) !important;\n    box-shadow: var(--shadow-lg);\n    overflow: hidden;\n}\n\n.card-pad {\n    padding: 22px;\n}\n\n\n/* ---------- UPLOAD ---------- */\n\n.upload-zone {\n    min-height: 430px;\n}\n\n.upload-zone .image-container {\n    border-radius: 20px !important;\n    overflow: hidden !important;\n}\n\n.upload-zone [data-testid="image"] {\n    min-height: 360px !important;\n}\n\n.upload-hint {\n    margin-top: 14px;\n    display: flex;\n    gap: 8px;\n    color: var(--text-3);\n    font-size: 12px;\n}\n\n\n/* ---------- BUTTONS ---------- */\n\n.primary-action {\n    margin-top: 14px !important;\n    min-height: 54px !important;\n    border: 0 !important;\n    border-radius: 16px !important;\n    background:\n        linear-gradient(\n            110deg,\n            #38cfa5,\n            #47aef2\n        ) !important;\n    color: #03110d !important;\n    font-weight: 800 !important;\n    letter-spacing: -0.01em;\n    box-shadow:\n        0 16px 34px rgba(55, 205, 163, 0.16) !important;\n    transition:\n        transform 180ms ease,\n        box-shadow 180ms ease,\n        filter 180ms ease !important;\n}\n\n.primary-action:hover {\n    transform: translateY(-2px);\n    filter: brightness(1.05);\n    box-shadow:\n        0 20px 42px rgba(55, 205, 163, 0.23) !important;\n}\n\n.secondary-action {\n    min-height: 48px !important;\n    border-radius: 14px !important;\n    border: 1px solid rgba(77, 226, 183, 0.24) !important;\n    background: rgba(77, 226, 183, 0.08) !important;\n    color: #9af4d9 !important;\n    font-weight: 700 !important;\n}\n\n\n/* ---------- PREDICTION HERO ---------- */\n\n.prediction-card {\n    position: relative;\n    padding: 28px;\n    min-height: 188px;\n    border-radius: 24px;\n    border: 1px solid rgba(77, 226, 183, 0.19);\n    background:\n        radial-gradient(\n            circle at 92% 5%,\n            rgba(84, 185, 255, 0.14),\n            transparent 32%\n        ),\n        linear-gradient(\n            135deg,\n            rgba(77, 226, 183, 0.10),\n            rgba(255,255,255,0.018)\n        );\n}\n\n.prediction-eyebrow {\n    color: #83eacc;\n    font-size: 10px;\n    font-weight: 800;\n    letter-spacing: 0.15em;\n}\n\n.prediction-name {\n    margin-top: 16px;\n    color: white;\n    font-size: clamp(30px, 4vw, 48px);\n    font-weight: 800;\n    line-height: 1;\n    letter-spacing: -0.05em;\n}\n\n.prediction-meta {\n    margin-top: 22px;\n    display: flex;\n    gap: 9px;\n    flex-wrap: wrap;\n}\n\n.confidence-pill,\n.level-pill {\n    display: inline-flex;\n    padding: 8px 11px;\n    border-radius: 999px;\n    font-size: 11px;\n    font-weight: 700;\n}\n\n.confidence-pill {\n    color: #9af4d9;\n    background: rgba(77, 226, 183, 0.10);\n    border: 1px solid rgba(77, 226, 183, 0.18);\n}\n\n.level-pill {\n    color: #a8d8ff;\n    background: rgba(84, 185, 255, 0.09);\n    border: 1px solid rgba(84, 185, 255, 0.17);\n}\n\n\n/* ---------- IMAGE COMPARISON ---------- */\n\n.visual-panel [data-testid="image"] {\n    min-height: 390px !important;\n}\n\n.visual-panel img {\n    object-fit: contain !important;\n    border-radius: 18px !important;\n}\n\n.panel-label {\n    color: var(--text-2);\n    font-size: 12px;\n    font-weight: 700;\n    letter-spacing: 0.02em;\n}\n\n\n/* ---------- META GRID ---------- */\n\n.meta-grid {\n    display: grid;\n    grid-template-columns: repeat(3, 1fr);\n    gap: 10px;\n}\n\n.meta-item {\n    min-height: 84px;\n    padding: 15px;\n    border-radius: 16px;\n    background: rgba(255,255,255,0.025);\n    border: 1px solid var(--border);\n}\n\n.meta-label {\n    display: block;\n    margin-bottom: 9px;\n    color: var(--text-3);\n    font-size: 9px;\n    font-weight: 800;\n    letter-spacing: 0.13em;\n}\n\n.meta-item strong {\n    color: var(--text-1);\n    font-size: 12px;\n}\n\n\n/* ---------- AI REPORT ---------- */\n\n.ai-report {\n    padding: 10px 4px;\n}\n\n.ai-report h1,\n.ai-report h2,\n.ai-report h3 {\n    color: var(--text-1) !important;\n    letter-spacing: -0.03em;\n}\n\n.ai-report p,\n.ai-report li {\n    color: #c1ced7 !important;\n    font-size: 15px !important;\n    line-height: 1.8 !important;\n}\n\n.ai-report strong {\n    color: #eef8f5 !important;\n}\n\n\n/* ---------- COPILOT ---------- */\n\n.copilot-shell {\n    border: 1px solid rgba(84, 185, 255, 0.16) !important;\n    border-radius: 28px !important;\n    background:\n        radial-gradient(\n            circle at 100% 0%,\n            rgba(84, 185, 255, 0.08),\n            transparent 32%\n        ),\n        var(--surface) !important;\n    box-shadow: var(--shadow-lg);\n}\n\n.prompt-row {\n    display: flex;\n    flex-wrap: wrap;\n    gap: 8px;\n    margin: 4px 0 14px;\n}\n\n.prompt-chip {\n    padding: 8px 11px;\n    border-radius: 999px;\n    border: 1px solid var(--border);\n    background: rgba(255,255,255,0.025);\n    color: var(--text-2);\n    font-size: 11px;\n}\n\n.copilot-input textarea {\n    min-height: 72px !important;\n    border-radius: 16px !important;\n}\n\n.copilot-answer {\n    min-height: 100px;\n    padding: 4px;\n}\n\n\n/* ---------- LABEL / CONFIDENCE PANEL ---------- */\n\n.confidence-panel {\n    border-radius: 22px !important;\n}\n\n.confidence-panel .label-wrap {\n    background: transparent !important;\n}\n\n\n/* ---------- FOOTER ---------- */\n\n.app-footer {\n    margin-top: 34px;\n    padding: 22px 4px 4px;\n    display: flex;\n    justify-content: space-between;\n    gap: 20px;\n    border-top: 1px solid var(--border);\n    color: var(--text-3);\n    font-size: 11px;\n}\n\n\n/* ---------- GRADIO CLEANUP ---------- */\n\nfooter {\n    display: none !important;\n}\n\nbutton,\ntextarea,\ninput {\n    transition: all 180ms ease !important;\n}\n\n.block {\n    border-color: var(--border) !important;\n}\n\nlabel span {\n    color: var(--text-2) !important;\n}\n\n\n/* ---------- RESPONSIVE ---------- */\n\n@media (max-width: 900px) {\n\n    .gradio-container {\n        padding: 0 14px 40px !important;\n    }\n\n    .hero {\n        padding: 52px 20px 48px;\n        border-radius: 24px;\n    }\n\n    .hero h1 {\n        font-size: 44px !important;\n    }\n\n    .hero-copy {\n        font-size: 15px;\n    }\n\n    .meta-grid {\n        grid-template-columns: 1fr;\n    }\n\n    .top-nav {\n        align-items: flex-start;\n    }\n\n    .brand-sub {\n        display: none;\n    }\n\n    .visual-panel [data-testid="image"],\n    .upload-zone [data-testid="image"] {\n        min-height: 280px !important;\n    }\n\n    .app-footer {\n        flex-direction: column;\n    }\n}\n\n\n@media (max-width: 560px) {\n\n    .system-status {\n        display: none;\n    }\n\n    .hero h1 {\n        font-size: 38px !important;\n    }\n\n    .hero {\n        padding-left: 16px;\n        padding-right: 16px;\n    }\n\n    .prediction-card {\n        padding: 22px;\n    }\n\n    .prediction-name {\n        font-size: 34px;\n    }\n}\n\n.prompt-chip-button {\n    flex: 0 0 auto !important;\n    width: auto !important;\n    min-width: 0 !important;\n    min-height: 40px !important;\n    padding: 0 16px !important;\n\n    border-radius: 999px !important;\n    border: 1px solid rgba(148, 184, 207, 0.18) !important;\n\n    background: rgba(255, 255, 255, 0.025) !important;\n    color: #b9d7ea !important;\n\n    font-size: 12px !important;\n    font-weight: 600 !important;\n\n    transition:\n        transform 180ms ease,\n        border-color 180ms ease,\n        background 180ms ease,\n        box-shadow 180ms ease !important;\n}\n\n.prompt-chip-button:hover {\n    transform: translateY(-2px) !important;\n\n    border-color: rgba(77, 226, 183, 0.45) !important;\n\n    background: rgba(77, 226, 183, 0.08) !important;\n    color: #a3f5dd !important;\n\n    box-shadow:\n        0 10px 28px rgba(77, 226, 183, 0.08) !important;\n}\n'
+
+
+# =========================================================
+# EXACT WORKING PREMIUM INTERFACE
+# =========================================================
+
+with gr.Blocks(
+    title="Geo-Intelligent AI",
+    css=premium_css,
+    theme=gr.themes.Base()
+) as premium_demo:
+
+    # Persistent analysis context
+    structured_state = gr.State()
+
+    # =====================================================
+    # TOP NAVIGATION
+    # =====================================================
+
+    gr.HTML("""
+    <div class="top-nav">
+        <div class="brand-lockup">
+            <div class="brand-mark">◈</div>
+            <div>
+                <div class="brand-name">Geo-Intelligent AI</div>
+                <div class="brand-sub">
+                    Explainable Geospatial Intelligence
+                </div>
+            </div>
+        </div>
+
+        <div class="system-status">
+            <span class="status-dot"></span>
+            AI systems operational
+        </div>
+    </div>
+    """)
+
+    # =====================================================
+    # HERO
+    # =====================================================
+
+    gr.HTML("""
+    <section class="hero">
+        <div class="hero-kicker">
+            ◉ MULTIMODAL GEOSPATIAL INTELLIGENCE
+        </div>
+
+        <h1>
+            Understand Earth imagery with
+            <span class="hero-gradient">intelligent context.</span>
+        </h1>
+
+        <p class="hero-copy">
+            Transform satellite imagery into explainable land-cover
+            intelligence using deep learning, visual attribution,
+            structured geospatial knowledge, and natural-language AI.
+        </p>
+
+        <div class="capability-row">
+            <span class="capability-chip">10 Land-Cover Classes</span>
+            <span class="capability-chip">90.44% Test Accuracy</span>
+            <span class="capability-chip">Grad-CAM Explainability</span>
+            <span class="capability-chip">LLM-Powered Analysis</span>
+        </div>
+    </section>
+    """)
+
+    # =====================================================
+    # ANALYSIS WORKSPACE
+    # =====================================================
+
+    gr.HTML("""
+    <div class="section-wrap">
+        <div class="section-heading">
+            <div class="section-eyebrow">01 / ANALYZE</div>
+            <div class="section-title">
+                Satellite Intelligence Workspace
+            </div>
+            <div class="section-copy">
+                Upload an image and run the complete GeoAI analysis pipeline.
+            </div>
+        </div>
+    </div>
+    """)
+
+    with gr.Row(equal_height=False):
+
+        # LEFT — UPLOAD
+        with gr.Column(
+            scale=5,
+            elem_classes=["premium-card", "card-pad"]
+        ):
+
+            input_image = gr.Image(
+                type="numpy",
+                label="Satellite Image",
+                sources=["upload"],
+                height=420,
+                elem_classes=["upload-zone"]
+            )
+
+            gr.HTML("""
+            <div class="upload-hint">
+                <span>◎</span>
+                <span>
+                    Best results are obtained from satellite or
+                    aerial land-cover imagery.
+                </span>
+            </div>
+            """)
+
+            analyze_button = gr.Button(
+                "Analyze Satellite Image  →",
+                variant="primary",
+                elem_classes=["primary-action"]
+            )
+
+        # RIGHT — PRIMARY RESULT
+        with gr.Column(scale=4):
+
+            prediction_summary = gr.HTML(
+                """
+                <div class="prediction-card">
+                    <div class="prediction-eyebrow">
+                        AWAITING ANALYSIS
+                    </div>
+
+                    <div class="prediction-name">
+                        No image analyzed
+                    </div>
+
+                    <div class="prediction-meta">
+                        <span class="level-pill">
+                            Upload an image to begin
+                        </span>
+                    </div>
+                </div>
+                """
+            )
+
+            with gr.Column(
+                elem_classes=["premium-card", "card-pad"]
+            ):
+
+                gr.HTML("""
+                <div class="section-eyebrow">
+                    CONFIDENCE DISTRIBUTION
+                </div>
+                """)
+
+                prediction_output = gr.Label(
+                    label="Top-3 Land-Cover Predictions",
+                    num_top_classes=3,
+                    elem_classes=["confidence-panel"]
+                )
+
+            analysis_meta = gr.HTML(
+                """
+                <div class="meta-grid">
+                    <div class="meta-item">
+                        <span class="meta-label">MODEL</span>
+                        <strong>MobileNetV2</strong>
+                    </div>
+
+                    <div class="meta-item">
+                        <span class="meta-label">
+                            EXPLAINABILITY
+                        </span>
+                        <strong>Grad-CAM</strong>
+                    </div>
+
+                    <div class="meta-item">
+                        <span class="meta-label">STATUS</span>
+                        <strong>Ready</strong>
+                    </div>
+                </div>
+                """
+            )
+
+    # =====================================================
+    # VISUAL INTELLIGENCE
+    # =====================================================
+
+    gr.HTML("""
+    <div class="section-wrap">
+        <div class="section-heading">
+            <div class="section-eyebrow">02 / EXPLAIN</div>
+
+            <div class="section-title">
+                Visual Intelligence
+            </div>
+
+            <div class="section-copy">
+                Compare the source image with the regions that most
+                influenced the model's prediction.
+            </div>
+        </div>
+    </div>
+    """)
+
+    with gr.Row(equal_height=True):
+
+        with gr.Column(
+            elem_classes=[
+                "premium-card",
+                "card-pad",
+                "visual-panel"
+            ]
+        ):
+
+            gr.HTML(
+                '<div class="panel-label">ORIGINAL IMAGE</div>'
+            )
+
+            original_output = gr.Image(
+                label=None,
+                interactive=False,
+                height=430,
+                show_label=False
+            )
+
+        with gr.Column(
+            elem_classes=[
+                "premium-card",
+                "card-pad",
+                "visual-panel"
+            ]
+        ):
+
+            gr.HTML(
+                '<div class="panel-label">GRAD-CAM ATTENTION</div>'
+            )
+
+            gradcam_output = gr.Image(
+                label=None,
+                interactive=False,
+                height=430,
+                show_label=False
+            )
+
+    # =====================================================
+    # AI INTELLIGENCE REPORT
+    # =====================================================
+
+    gr.HTML("""
+    <div class="section-wrap">
+        <div class="section-heading">
+            <div class="section-eyebrow">03 / INTERPRET</div>
+
+            <div class="section-title">
+                GeoAI Intelligence Report
+            </div>
+
+            <div class="section-copy">
+                A natural-language interpretation grounded in the
+                model output and geospatial knowledge layer.
+            </div>
+        </div>
+    </div>
+    """)
+
+    with gr.Column(
+        elem_classes=["premium-card", "card-pad"]
+    ):
+
+        explanation_output = gr.Markdown(
+            """
+            Upload and analyze a satellite image to generate
+            the intelligence report.
+            """,
+            elem_classes=["ai-report"]
+        )
+
+    # =====================================================
+    # GEOAI COPILOT
+    # =====================================================
+
+    gr.HTML("""
+    <div class="section-wrap">
+        <div class="section-heading">
+            <div class="section-eyebrow">04 / ASK</div>
+
+            <div class="section-title">
+                GeoAI Copilot
+            </div>
+
+            <div class="section-copy">
+                Ask follow-up questions about the currently
+                analyzed image.
+            </div>
+        </div>
+    </div>
+    """)
+
+    with gr.Column(
+        elem_classes=["copilot-shell", "card-pad"]
+    ):
+
+        # =============================================
+        # CLICKABLE SUGGESTED QUESTIONS
+        # =============================================
+
+        with gr.Row(
+            elem_classes=["prompt-row"]
+        ):
+
+            prompt_reliability = gr.Button(
+                "How reliable is this prediction?",
+                elem_classes=["prompt-chip-button"]
+            )
+
+            prompt_focus = gr.Button(
+                "What did the model focus on?",
+                elem_classes=["prompt-chip-button"]
+            )
+
+            prompt_importance = gr.Button(
+                "Why is this land cover important?",
+                elem_classes=["prompt-chip-button"]
+            )
+
+            prompt_alternatives = gr.Button(
+                "What are the alternative predictions?",
+                elem_classes=["prompt-chip-button"]
+            )
+
+        question_input = gr.Textbox(
+            label="Ask about this analysis",
+            placeholder=(
+                "Ask about the prediction, confidence, "
+                "explainability, significance, or applications..."
+            ),
+            lines=2,
+            elem_classes=["copilot-input"]
+        )
+
+        with gr.Row():
+
+            ask_button = gr.Button(
+                "Ask GeoAI  →",
+                variant="secondary",
+                elem_classes=["secondary-action"]
+            )
+
+            clear_question_button = gr.Button(
+                "Clear Question"
+            )
+
+        answer_output = gr.Markdown(
+            """
+            Your GeoAI Copilot response will appear here.
+            """,
+            elem_classes=["copilot-answer"]
+        )
+
+    # =====================================================
+    # FOOTER
+    # =====================================================
+
+    gr.HTML("""
+    <div class="app-footer">
+        <span>
+            Geo-Intelligent AI System ·
+            Explainable Satellite Intelligence
+        </span>
+
+        <span>
+            MobileNetV2 · Grad-CAM ·
+            Structured GeoAI · Open-Weight LLM
+        </span>
+    </div>
+    """)
+
+    # =====================================================
+    # HELPER FUNCTION FOR SUGGESTED QUESTIONS
+    # =====================================================
+
+    def ask_suggested_question(
+        question,
+        structured_state
+    ):
+
+        answer = gradio_ask_question(
+            question,
+            structured_state
+        )
+
+        return question, answer
+
+    # =====================================================
+    # EVENT WIRING
+    # =====================================================
+
+    # Analyze satellite image
+    analyze_button.click(
+        fn=premium_analyze_image,
+        inputs=input_image,
+        outputs=[
+            prediction_summary,
+            original_output,
+            gradcam_output,
+            prediction_output,
+            explanation_output,
+            analysis_meta,
+            structured_state
+        ]
+    )
+
+    # Ask manually typed question
+    ask_button.click(
+        fn=gradio_ask_question,
+        inputs=[
+            question_input,
+            structured_state
+        ],
+        outputs=answer_output
+    )
+
+    # Press Enter to ask
+    question_input.submit(
+        fn=gradio_ask_question,
+        inputs=[
+            question_input,
+            structured_state
+        ],
+        outputs=answer_output
+    )
+
+    # Clear question and answer
+    clear_question_button.click(
+        fn=lambda: (
+            "",
+            "Your GeoAI Copilot response will appear here."
+        ),
+        inputs=None,
+        outputs=[
+            question_input,
+            answer_output
+        ]
+    )
+
+    # =====================================================
+    # SUGGESTED QUESTION EVENTS
+    # =====================================================
+
+    prompt_reliability.click(
+        fn=lambda state: ask_suggested_question(
+            "How reliable is this prediction?",
+            state
+        ),
+        inputs=structured_state,
+        outputs=[
+            question_input,
+            answer_output
+        ]
+    )
+
+    prompt_focus.click(
+        fn=lambda state: ask_suggested_question(
+            "What did the model focus on?",
+            state
+        ),
+        inputs=structured_state,
+        outputs=[
+            question_input,
+            answer_output
+        ]
+    )
+
+    prompt_importance.click(
+        fn=lambda state: ask_suggested_question(
+            "Why is this land cover important?",
+            state
+        ),
+        inputs=structured_state,
+        outputs=[
+            question_input,
+            answer_output
+        ]
+    )
+
+    prompt_alternatives.click(
+        fn=lambda state: ask_suggested_question(
+            "What are the alternative predictions?",
+            state
+        ),
+        inputs=structured_state,
+        outputs=[
+            question_input,
+            answer_output
+        ]
+    )
+    
+
+
+# =========================================================
+# HUGGING FACE SPACES LAUNCH
+# =========================================================
+
+if __name__ == "__main__":
+    premium_demo.launch()

geoai_clean_final_best.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bf5b8d0424e94f061bae591d9f882b77216130422ec5b627a17d099c3187fdf2
+size 23721940

requirements.txt

@@ -0,0 +1,5 @@
+gradio==6.19.0
+tensorflow-cpu
+opencv-python-headless
+numpy
+groq