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.gradio/certificate.pem

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+-----END CERTIFICATE-----

README.md

@@ -1,12 +1,6 @@
 ---
-title: 3rd Hack Nation
-emoji: 📊
-colorFrom: blue
-colorTo: indigo
+title: 3rd-hack-nation
+app_file: ./geo-risk-space/app.py
 sdk: gradio
 sdk_version: 5.49.1
-app_file: app.py
-pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

geo-risk-space/README.md

@@ -0,0 +1,25 @@
+---
+title: Geo-Risk Predictor
+emoji: 🌍
+colorFrom: green
+colorTo: blue
+sdk: gradio
+sdk_version: "4.44.0"
+app_file: app.py
+pinned: false
+---
+
+# 🌍 Geo-Risk Predictor
+
+Interactive Gradio app that estimates environmental and geographic risk using:
+- **Satellite imagery** (Google Earth Engine)
+- **Elevation data** (SRTM)
+- **Weather data** (Open-Meteo)
+- **Custom PyTorch model** for risk inference
+
+## How to run locally
+```bash
+pip install -r requirements.txt
+python app.py
+```
+-------------------------------

geo-risk-space/app.py

@@ -0,0 +1,245 @@
+# app.py
+import os
+import io
+import json
+import tempfile
+import torch
+import requests
+import numpy as np
+import pandas as pd
+import gradio as gr
+from PIL import Image
+import torchvision.transforms as T
+from geopy.geocoders import Nominatim
+
+# --- Import model ---
+from src.model import CompactGeoEmbed
+
+# --- Config ---
+
+
+MODEL_PATH = "model/geo_model.pth"
+GEE_KEY_PATH = os.environ.get("GEE_KEY_PATH", "keys/gee_service_account.json")
+DEVICE = torch.device("cpu")
+TF_SIZE = (120, 120)
+
+# --- Load local model ---
+def load_model():
+    model = CompactGeoEmbed(32, 96)
+    try:
+        state = torch.load(MODEL_PATH, map_location=DEVICE)
+        model.load_state_dict(state)
+        print("✅ Model loaded successfully from local path.")
+    except Exception as e:
+        print("⚠️ Model load failed:", e)
+    model.to(DEVICE).eval()
+    return model
+
+MODEL = load_model()
+tf = T.Compose([T.Resize(TF_SIZE), T.ToTensor()])
+
+# --- Reverse geocode (city, country info) ---
+def reverse_geocode(lat, lon):
+    try:
+        geolocator = Nominatim(user_agent="geo-risk-app", timeout=10)
+        loc = geolocator.reverse((lat, lon), language="en")
+        return loc.address if loc else "Unknown location"
+    except Exception as e:
+        print("⚠️ Reverse geocode failed:", e)
+        return "Unknown location"
+
+# --- Earth Engine init with service account ---
+def init_gee():
+    try:
+        import ee
+    except Exception as e:
+        print("⚠️ earthengine-api not installed:", e)
+        return False
+
+    if not os.path.exists(GEE_KEY_PATH):
+        print(f"⚠️ GEE key not found at {GEE_KEY_PATH}")
+        return False
+
+    try:
+        with open(GEE_KEY_PATH, "r") as f:
+            svc = json.load(f)
+        credentials = ee.ServiceAccountCredentials(svc["client_email"], GEE_KEY_PATH)
+        ee.Initialize(credentials)
+        print("✅ Earth Engine initialized with service account.")
+        return True
+    except Exception as e:
+        print("⚠️ GEE initialization failed:", e)
+        return False
+
+GEE_READY = init_gee()
+
+# --- Fetch satellite & elevation tiles from GEE ---
+def fetch_gee_images(lat, lon):
+    try:
+        if not GEE_READY:
+            raise RuntimeError("GEE not initialized")
+
+        import ee
+
+        p = ee.Geometry.Point([lon, lat])
+        region = p.buffer(1000).bounds()
+
+        s2 = (
+            ee.ImageCollection("COPERNICUS/S2_SR_HARMONIZED")
+            .filterBounds(p)
+            .filterDate("2024-01-01", "2024-12-31")
+            .sort("CLOUDY_PIXEL_PERCENTAGE")
+            .first()
+            .select(["B4", "B3", "B2"])
+        )
+        s2_url = s2.visualize(min=0, max=3000).getThumbURL(
+            {"region": region, "dimensions": f"{TF_SIZE[0]}x{TF_SIZE[1]}", "format": "png"}
+        )
+        s2_img = Image.open(io.BytesIO(requests.get(s2_url, timeout=10).content)).convert("RGB")
+
+        srtm = ee.Image("USGS/SRTMGL1_003")
+        elev_url = srtm.visualize(min=0, max=3000).getThumbURL(
+            {"region": region, "dimensions": f"{TF_SIZE[0]}x{TF_SIZE[1]}", "format": "png"}
+        )
+        elev_img = Image.open(io.BytesIO(requests.get(elev_url, timeout=10).content)).convert("L")
+
+        return s2_img, elev_img
+    except Exception as e:
+        print("⚠️ GEE fetch failed:", e)
+        rgb = Image.new("RGB", TF_SIZE, (127, 127, 127))
+        elev = Image.new("L", TF_SIZE, 127)
+        return rgb, elev
+
+# --- Weather API ---
+def fetch_weather(lat, lon):
+    try:
+        url = (
+            f"https://api.open-meteo.com/v1/forecast?"
+            f"latitude={lat}&longitude={lon}&daily=temperature_2m_max,"
+            "precipitation_sum,relative_humidity_2m_mean,wind_speed_10m_max"
+            "&forecast_days=1&timezone=UTC"
+        )
+        r = requests.get(url, timeout=8)
+        d = r.json().get("daily", {})
+        return {
+            "temp_max": float(d.get("temperature_2m_max", [None])[0]) if d else None,
+            "precip": float(d.get("precipitation_sum", [None])[0]) if d else None,
+            "humidity": float(d.get("relative_humidity_2m_mean", [None])[0]) if d else None,
+            "wind_speed": float(d.get("wind_speed_10m_max", [None])[0]) if d else None,
+        }
+    except Exception as e:
+        print("⚠️ Weather fetch failed:", e)
+        return {"temp_max": None, "precip": None, "humidity": None, "wind_speed": None}
+
+# --- Preprocess image ---
+def preprocess(img):
+    img = img.convert("RGB")
+    return tf(img).unsqueeze(0)
+
+# --- IP-based location fallback ---
+def get_ip_location():
+    try:
+        r = requests.get("https://ipapi.co/json", timeout=5)
+        data = r.json()
+        return round(float(data["latitude"]), 5), round(float(data["longitude"]), 5)
+    except Exception as e:
+        print("⚠️ IP location fetch failed:", e)
+        return 51.5072, -0.1276  # fallback to London
+
+# --- Inference ---
+def predict(lat, lon, img):
+    # fallback if user didn't fill lat/lon
+    if lat is None or lon is None:
+        lat, lon = get_ip_location()
+
+    lat = round(float(lat), 5)
+    lon = round(float(lon), 5)
+
+    location_str = reverse_geocode(lat, lon)
+    rgb_img, elev_img = fetch_gee_images(lat, lon)
+
+    # user-uploaded image takes priority
+    if img is not None:
+        rgb_img = img.resize(TF_SIZE)
+
+    x = preprocess(rgb_img).to(DEVICE)
+    e = torch.tensor(np.array(elev_img) / 255.0, dtype=torch.float32).unsqueeze(0).unsqueeze(0).to(DEVICE)
+
+    with torch.no_grad():
+        try:
+            _, _, r = MODEL(x, e)
+            risk_score = float(r.item())
+        except Exception as e:
+            print("⚠️ Model inference failed:", e)
+            risk_score = None
+
+    weather = fetch_weather(lat, lon)
+    result = {
+        "Location": location_str,
+        "Latitude": lat,
+        "Longitude": lon,
+        "Predicted_Risk_Score": round(risk_score, 4) if risk_score is not None else None,
+        **weather,
+    }
+
+    df = pd.DataFrame([result])
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".csv") as tmp:
+        tmp_path = tmp.name
+    df.to_csv(tmp_path, index=False)
+
+    return rgb_img, df, tmp_path
+
+# --- Gradio UI ---
+with gr.Blocks() as demo:
+    gr.Markdown("## 🌍 Geo-Risk Predictor (Local Model + GEE + Location Auto-Detect)")
+
+    with gr.Row():
+        lat = gr.Number(value=None, label="Latitude")
+        lon = gr.Number(value=None, label="Longitude")
+        get_loc_btn = gr.Button("📍 Use My Location")
+
+    img = gr.Image(type="pil", label=f"Optional RGB Tile (auto-resized to {TF_SIZE[0]}×{TF_SIZE[1]})")
+    run_btn = gr.Button("Run Prediction")
+
+    rgb_preview = gr.Image(label="Satellite Image Used")
+    output_df = gr.DataFrame(label="Predicted Data", interactive=False)
+    file_out = gr.File(label="Download CSV")
+
+    # main prediction button
+    run_btn.click(fn=predict, inputs=[lat, lon, img], outputs=[rgb_preview, output_df, file_out])
+
+    # JS geolocation with IP fallback
+    get_loc_btn.click(
+        None,
+        [],
+        [lat, lon],
+        js="""
+        async () => {
+            if (navigator.geolocation) {
+                try {
+                    const pos = await new Promise((res, rej) =>
+                        navigator.geolocation.getCurrentPosition(res, rej)
+                    );
+                    return [pos.coords.latitude.toFixed(5), pos.coords.longitude.toFixed(5)];
+                } catch (err) {
+                    console.warn("Browser geolocation failed, fallback to IP API.");
+                    const ip = await fetch("https://ipapi.co/json");
+                    const data = await ip.json();
+                    return [data.latitude.toFixed(5), data.longitude.toFixed(5)];
+                }
+            } else {
+                const ip = await fetch("https://ipapi.co/json");
+                const data = await ip.json();
+                return [data.latitude.toFixed(5), data.longitude.toFixed(5)];
+            }
+        }
+        """,
+    )
+
+if __name__ == "__main__":
+    # demo.launch(server_name="0.0.0.0",
+    #             server_port=int(os.environ.get("PORT", 7860)),
+    #             share=True)
+    
+    demo.launch(share=True)
+

geo-risk-space/keys/gee_service_account.json

@@ -0,0 +1,13 @@
+{
+  "type": "service_account",
+  "project_id": "theta-decker-477618-q0",
+  "private_key_id": "0f560b4d89f9a91880f5a4a0c995fa084a348bde",
+  "private_key": "-----BEGIN PRIVATE KEY-----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\njUurfHkPNQjJzH9T1f21prU=\n-----END PRIVATE KEY-----\n",
+  "client_email": "hack-nation-gee@theta-decker-477618-q0.iam.gserviceaccount.com",
+  "client_id": "109368693327747874205",
+  "auth_uri": "https://accounts.google.com/o/oauth2/auth",
+  "token_uri": "https://oauth2.googleapis.com/token",
+  "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
+  "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/hack-nation-gee%40theta-decker-477618-q0.iam.gserviceaccount.com",
+  "universe_domain": "googleapis.com"
+}

geo-risk-space/keys/theta-decker-477618-q0-0f560b4d89f9.json

@@ -0,0 +1,13 @@
+{
+  "type": "service_account",
+  "project_id": "theta-decker-477618-q0",
+  "private_key_id": "0f560b4d89f9a91880f5a4a0c995fa084a348bde",
+  "private_key": "-----BEGIN PRIVATE KEY-----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\njUurfHkPNQjJzH9T1f21prU=\n-----END PRIVATE KEY-----\n",
+  "client_email": "hack-nation-gee@theta-decker-477618-q0.iam.gserviceaccount.com",
+  "client_id": "109368693327747874205",
+  "auth_uri": "https://accounts.google.com/o/oauth2/auth",
+  "token_uri": "https://oauth2.googleapis.com/token",
+  "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
+  "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/hack-nation-gee%40theta-decker-477618-q0.iam.gserviceaccount.com",
+  "universe_domain": "googleapis.com"
+}

geo-risk-space/main-requirements.txt

@@ -0,0 +1,109 @@
+Package                   Version
+------------------------- -----------
+aiofiles                  24.1.0
+altair                    5.5.0
+annotated-doc             0.0.3
+annotated-types           0.7.0
+anyio                     4.11.0
+attrs                     25.4.0
+blinker                   1.9.0
+branca                    0.8.2
+brotli                    1.2.0
+cachetools                5.5.2
+certifi                   2025.10.5
+charset-normalizer        3.4.4
+click                     8.3.0
+earthengine-api           0.1.383
+fastapi                   0.121.1
+ffmpy                     0.6.4
+filelock                  3.20.0
+folium                    0.17.0
+fsspec                    2025.10.0
+geographiclib             2.1
+geopy                     2.4.1
+gitdb                     4.0.12
+GitPython                 3.1.45
+google-api-core           2.28.1
+google-api-python-client  2.187.0
+google-auth               2.43.0
+google-auth-httplib2      0.2.1
+google-cloud-core         2.5.0
+google-cloud-storage      3.5.0
+google-crc32c             1.7.1
+google-resumable-media    2.7.2
+googleapis-common-protos  1.72.0
+gradio                    5.49.1
+gradio_client             1.13.3
+groovy                    0.1.2
+h11                       0.16.0
+hf-xet                    1.2.0
+httpcore                  1.0.9
+httplib2                  0.31.0
+httpx                     0.28.1
+huggingface_hub           1.1.2
+idna                      3.11
+Jinja2                    3.1.6
+jsonschema                4.25.1
+jsonschema-specifications 2025.9.1
+markdown-it-py            4.0.0
+MarkupSafe                3.0.3
+mdurl                     0.1.2
+mpmath                    1.3.0
+narwhals                  2.10.2
+networkx                  3.5
+numpy                     2.3.4
+orjson                    3.11.4
+packaging                 24.2
+pandas                    2.2.2
+pillow                    10.4.0
+pip                       25.2
+proto-plus                1.26.1
+protobuf                  5.29.5
+pyarrow                   22.0.0
+pyasn1                    0.6.1
+pyasn1_modules            0.4.2
+pydantic                  2.11.10
+pydantic_core             2.33.2
+pydeck                    0.9.1
+pydub                     0.25.1
+Pygments                  2.19.2
+pyparsing                 3.2.5
+python-dateutil           2.9.0.post0
+python-multipart          0.0.20
+pytz                      2025.2
+PyYAML                    6.0.3
+referencing               0.37.0
+requests                  2.32.3
+rich                      13.9.4
+rpds-py                   0.28.0
+rsa                       4.9.1
+ruff                      0.14.4
+safehttpx                 0.1.7
+semantic-version          2.10.0
+setuptools                80.9.0
+shellingham               1.5.4
+six                       1.17.0
+smmap                     5.0.2
+sniffio                   1.3.1
+starlette                 0.49.3
+streamlit                 1.38.0
+streamlit_folium          0.22.0
+sympy                     1.14.0
+tenacity                  8.5.0
+toml                      0.10.2
+tomlkit                   0.13.3
+torch                     2.9.0
+torchvision               0.24.0
+tornado                   6.5.2
+tqdm                      4.67.1
+typer                     0.20.0
+typer-slim                0.20.0
+typing_extensions         4.15.0
+typing-inspection         0.4.2
+tzdata                    2025.2
+uritemplate               4.2.0
+urllib3                   2.5.0
+uvicorn                   0.38.0
+websockets                15.0.1
+wheel                     0.45.1
+xyzservices               2025.10.0

geo-risk-space/model/README.txt

@@ -0,0 +1,2 @@
+Place your trained model weights here as:
+    geo-risk-space/model/geo_model.pth

geo-risk-space/model/geo_model.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:949af48e93767a75576516b19d6426a1ab5662ab9d1fd19b184d8c0b0e56cc41
+size 9485835

geo-risk-space/requirements.txt

@@ -0,0 +1,12 @@
+gradio==4.44.0
+huggingface_hub==0.23.0
+torch
+torchvision
+pandas
+
+requests
+Pillow
+
+numpy
+earthengine-api
+geopy

geo-risk-space/src/model.py

@@ -0,0 +1,46 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision.models as models
+
+class CompactGeoEmbed(nn.Module):
+    def __init__(self, embed_c=32, proj_dim=96, pretrained=False):
+        super().__init__()
+        backbone = models.mobilenet_v2(
+            weights=None if not pretrained else models.MobileNet_V2_Weights.IMAGENET1K_V1
+        ).features
+        self.backbone = backbone
+        self.reduce = nn.Conv2d(1280, embed_c, 1)
+        self.elev_conv = nn.Conv2d(1, embed_c, 3, padding=1)
+        self.conv_head = nn.Sequential(
+            nn.Conv2d(embed_c * 2, embed_c, 3, padding=1),
+            nn.ReLU(True),
+            nn.Conv2d(embed_c, embed_c, 3, padding=1),
+            nn.ReLU(True),
+        )
+        self.proj = nn.Sequential(
+            nn.AdaptiveAvgPool2d(1),
+            nn.Flatten(),
+            nn.Linear(embed_c, proj_dim),
+            nn.ReLU(),
+            nn.Linear(proj_dim, proj_dim),
+        )
+        self.risk_head = nn.Sequential(
+            nn.Linear(proj_dim, proj_dim // 2),
+            nn.ReLU(),
+            nn.Linear(proj_dim // 2, 1),
+            nn.Sigmoid(),
+        )
+
+    def forward(self, img, elev=None):
+        x = self.backbone(img)
+        x = self.reduce(x)
+        if elev is None:
+            elev = torch.zeros(x.size(0), 1, img.size(2), img.size(3), device=x.device)
+        elev = F.interpolate(elev, size=x.shape[2:], mode="bilinear", align_corners=False)
+        e = self.elev_conv(elev)
+        x = self.conv_head(torch.cat([x, e], 1))
+        p = self.proj(x)
+        p = F.normalize(p, dim=1)
+        risk = self.risk_head(p).squeeze(-1)
+        return x, p, risk

t.sh

@@ -0,0 +1,180 @@
+#!/bin/bash
+set -e
+
+# === Create folder structure ===
+mkdir -p geo-risk-space/src geo-risk-space/model geo-risk-space/keys
+
+# === model placeholder ===
+cat > geo-risk-space/model/README.txt <<'EOF'
+Place your trained model weights here as:
+    geo-risk-space/model/geo_model.pth
+EOF
+
+# === src/model.py ===
+cat > geo-risk-space/src/model.py <<'EOF'
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision.models as models
+
+class CompactGeoEmbed(nn.Module):
+    def __init__(self, embed_c=32, proj_dim=96, pretrained=False):
+        super().__init__()
+        backbone = models.mobilenet_v2(
+            weights=None if not pretrained else models.MobileNet_V2_Weights.IMAGENET1K_V1
+        ).features
+        self.backbone = backbone
+        self.reduce = nn.Conv2d(1280, embed_c, 1)
+        self.elev_conv = nn.Conv2d(1, embed_c, 3, padding=1)
+        self.conv_head = nn.Sequential(
+            nn.Conv2d(embed_c * 2, embed_c, 3, padding=1),
+            nn.ReLU(True),
+            nn.Conv2d(embed_c, embed_c, 3, padding=1),
+            nn.ReLU(True),
+        )
+        self.proj = nn.Sequential(
+            nn.AdaptiveAvgPool2d(1),
+            nn.Flatten(),
+            nn.Linear(embed_c, proj_dim),
+            nn.ReLU(),
+            nn.Linear(proj_dim, proj_dim),
+        )
+        self.risk_head = nn.Sequential(
+            nn.Linear(proj_dim, proj_dim // 2),
+            nn.ReLU(),
+            nn.Linear(proj_dim // 2, 1),
+            nn.Sigmoid(),
+        )
+
+    def forward(self, img, elev=None):
+        x = self.backbone(img)
+        x = self.reduce(x)
+        if elev is None:
+            elev = torch.zeros(x.size(0), 1, img.size(2), img.size(3), device=x.device)
+        elev = F.interpolate(elev, size=x.shape[2:], mode="bilinear", align_corners=False)
+        e = self.elev_conv(elev)
+        x = self.conv_head(torch.cat([x, e], 1))
+        p = self.proj(x)
+        p = F.normalize(p, dim=1)
+        risk = self.risk_head(p).squeeze(-1)
+        return x, p, risk
+EOF
+
+# === app.py ===
+cat > geo-risk-space/app.py <<'EOF'
+import os, json, io, torch, requests, numpy as np, gradio as gr, ee
+from PIL import Image
+import torchvision.transforms as T
+from huggingface_hub import hf_hub_download
+from src.model import CompactGeoEmbed
+
+MODEL_LOCAL = "/app/model/geo_model.pth"
+HF_REPO_ID = "USERNAME/geo-risk-model"
+HF_FILENAME = "geo_model.pth"
+GEE_KEY_PATH = "/app/keys/gee_service_account.json"
+
+device = torch.device("cpu")
+
+def init_gee():
+    if os.path.exists(GEE_KEY_PATH):
+        with open(GEE_KEY_PATH) as f:
+            svc = json.load(f)
+        ee.Initialize(ee.ServiceAccountCredentials(svc["client_email"], GEE_KEY_PATH))
+        print("✅ GEE initialized")
+    else:
+        print("⚠️ Missing GEE key, skipping Earth Engine init")
+
+def load_model():
+    model = CompactGeoEmbed(32, 96)
+    state = None
+    if os.path.exists(MODEL_LOCAL):
+        state = torch.load(MODEL_LOCAL, map_location="cpu")
+    else:
+        try:
+            p = hf_hub_download(repo_id=HF_REPO_ID, filename=HF_FILENAME)
+            state = torch.load(p, map_location="cpu")
+        except Exception as e:
+            print("⚠️ No weights found:", e)
+    if state:
+        model.load_state_dict(state)
+    model.to(device).eval()
+    return model
+
+init_gee()
+MODEL = load_model()
+tf = T.Compose([T.Resize((128, 128)), T.ToTensor()])
+
+def fetch_satellite(lat, lon):
+    p = ee.Geometry.Point([lon, lat])
+    srtm = ee.Image("USGS/SRTMGL1_003")
+    ndvi = ee.ImageCollection("MODIS/061/MOD13A2").select("NDVI").mean()
+    rgb = (
+        ee.ImageCollection("COPERNICUS/S2_SR_HARMONIZED")
+        .filterBounds(p)
+        .filterDate("2023-01-01", "2024-01-01")
+        .sort("CLOUDY_PIXEL_PERCENTAGE")
+        .first()
+    )
+    region = p.buffer(1000).bounds()
+    elev_url = srtm.visualize(min=0, max=3000).getThumbURL(
+        {"region": region, "dimensions": "128x128", "format": "png"}
+    )
+    ndvi_url = ndvi.visualize(min=-2000, max=10000, palette=["white", "green"]).getThumbURL(
+        {"region": region, "dimensions": "128x128", "format": "png"}
+    )
+    elev = Image.open(io.BytesIO(requests.get(elev_url).content)).convert("L")
+    ndvi_img = Image.open(io.BytesIO(requests.get(ndvi_url).content)).convert("RGB")
+    return elev, ndvi_img
+
+def preprocess(img):
+    if img is None:
+        img = Image.new("RGB", (128, 128), (127, 127, 127))
+    return tf(img.convert("RGB")).unsqueeze(0)
+
+def predict(lat, lon, img):
+    lat, lon = float(lat), float(lon)
+    try:
+        elev, sat = fetch_satellite(lat, lon)
+    except Exception as e:
+        elev = Image.new("L", (128, 128), 127)
+        sat = img
+    x = preprocess(sat).to(device)
+    e = torch.tensor(np.array(elev) / 255.0, dtype=torch.float32).unsqueeze(0).unsqueeze(0).to(device)
+    with torch.no_grad():
+        _, _, r = MODEL(x, e)
+    return f"Predicted Risk Score: {float(r.item()):.4f}"
+
+with gr.Blocks() as demo:
+    gr.Markdown("## 🌍 Geo-Risk Prediction (GEE-powered)")
+    lat = gr.Number(value=51.5072, label="Latitude")
+    lon = gr.Number(value=-0.1276, label="Longitude")
+    img = gr.Image(type="pil", label="Optional RGB (Sentinel-2 fallback)")
+    out = gr.Textbox(label="Prediction")
+    gr.Button("Run").click(fn=predict, inputs=[lat, lon, img], outputs=out)
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=int(os.environ.get("PORT", 7860)))
+EOF
+
+# === requirements.txt ===
+cat > geo-risk-space/requirements.txt <<'EOF'
+torch
+torchvision
+gradio
+pillow
+requests
+huggingface_hub
+earthengine-api
+EOF
+
+# === README.md ===
+cat > geo-risk-space/README.md <<'EOF'
+# 🌍 Geo-Risk Prediction (GEE + Gradio)
+
+### Setup
+
+```bash
+bash setup_space.sh
+cd geo-risk-space
+pip install -r requirements.txt
+python app.py

upload-hf.py

@@ -0,0 +1,45 @@
+# upload_to_space_recursive.py
+import os
+from huggingface_hub import HfApi, upload_file, create_repo
+
+# === CONFIGURATION ===
+HF_TOKEN = os.environ.get("HF_TOKEN")  # export HF_TOKEN="hf_xxx..."
+REPO_ID = "Pingsz/hack-nation-3rd1"   # your Hugging Face Space
+REPO_TYPE = "space"
+LOCAL_DIR = "geo-risk-space"          # folder to upload
+
+api = HfApi(token=HF_TOKEN)
+
+# Create repo if doesn't exist
+try:
+    api.create_repo(repo_id=REPO_ID, repo_type=REPO_TYPE, private=False)
+    print(f"✅ Created Space: {REPO_ID}")
+except Exception as e:
+    print(f"ℹ️ Space may already exist: {e}")
+
+# Walk directory recursively
+for root, _, files in os.walk(LOCAL_DIR):
+    for file in files:
+        # Skip cache, compiled pyc, and secret keys
+        if file.endswith(".pyc") or "gee_service_account" in file or "theta-decker" in file:
+            print(f"⏩ Skipping secret or cache file: {file}")
+            continue
+
+        local_path = os.path.join(root, file)
+        # target path inside repo: preserve folder structure under geo-risk-space/
+        rel_path = os.path.relpath(local_path, LOCAL_DIR).replace("\\", "/")
+
+        print(f"⬆️ Uploading {rel_path} ...")
+        try:
+            upload_file(
+                path_or_fileobj=local_path,
+                path_in_repo=rel_path,
+                repo_id=REPO_ID,
+                repo_type=REPO_TYPE,
+                token=HF_TOKEN,
+            )
+        except Exception as e:
+            print(f"❌ Upload failed for {file}: {e}")
+
+print("✅ Upload complete.")
+print(f"🔗 Check your Space at: https://huggingface.co/spaces/{REPO_ID}")