initial commit

Dockerfile

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+FROM python:3.10
+
+WORKDIR /code
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+EXPOSE 7860
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+import io
+import pickle
+
+import numpy as np
+import torch
+from fastapi import FastAPI, UploadFile, File, Form
+from fastapi.middleware.cors import CORSMiddleware
+from PIL import Image
+from transformers import AutoTokenizer, AutoModel
+import open_clip
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+TEXT_MODEL_NAME = "indobenchmark/indobert-large-p1"
+tokenizer = AutoTokenizer.from_pretrained(TEXT_MODEL_NAME)
+text_model = AutoModel.from_pretrained(TEXT_MODEL_NAME).to(device)
+text_model.eval()
+
+clip_model, _, clip_preprocess = open_clip.create_model_and_transforms(
+    "EVA01-g-14-plus",
+    pretrained="merged2b_s11b_b114k"
+)
+clip_model.to(device)
+clip_model.eval()
+
+with open("xgb_full.pkl", "rb") as f:
+    xgb_model = pickle.load(f)
+
+def preprocess_text(text: str) -> str:
+    # nanti ditambahin preprocessingnya
+    return text.strip()
+
+app = FastAPI(
+    title="Multimodal Water Pollution Risk API",
+    description=(
+        "Input: text + image + geospatial + time\n"
+        "Model: IndoBERT + EVA-CLIP (HF Hub) + XGBoost (xgb.pkl)\n"
+    ),
+    version="1.0.0",
+)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+@app.get("/")
+def root():
+    return {
+        "status": "OK",
+        "message": "Multimodal Water Pollution Risk API is running.",
+        "info": "Use POST /predict with text, image, and features.",
+    }
+
+@app.post("/predict")
+async def predict(
+    text: str = Form(...),
+    longitude: float = Form(...),
+    latitude: float = Form(...),
+    location_cluster: int = Form(...),
+    hour: int = Form(...),
+    dayofweek: int = Form(...),
+    month: int = Form(...),
+    image: UploadFile = File(...),
+):
+    # 1. Preprocess text
+    cleaned_text = preprocess_text(text)
+
+    # 2. Encode text -> IndoBERT CLS embedding (shape: [1, 1024])
+    text_inputs = tokenizer(
+        cleaned_text,
+        return_tensors="pt",
+        padding="max_length",
+        truncation=True,
+        max_length=128,
+    )
+    text_inputs = {k: v.to(device) for k, v in text_inputs.items()}
+    with torch.no_grad():
+        text_emb = text_model(**text_inputs).last_hidden_state[:, 0, :]
+    text_emb = text_emb.cpu().numpy()
+
+    # 3. Encode image -> EVA-CLIP image embedding (shape: [1, 1024] / sesuai model)
+    img_bytes = await image.read()
+    pil_img = Image.open(io.BytesIO(img_bytes)).convert("RGB")
+    img_tensor = clip_preprocess(pil_img).unsqueeze(0).to(device)
+
+    with torch.no_grad():
+        img_emb = clip_model.encode_image(img_tensor)
+    img_emb = img_emb.cpu().numpy()
+
+    # 4. Additional numeric features (same order as training)
+    add_feats = np.array(
+        [[longitude, latitude, location_cluster, hour, dayofweek, month]],
+        dtype=np.float32,
+    )
+
+    # 5. Concatenate: [image_emb, text_emb, add_feats]
+    # pastikan bentuk-nya [1, dim_image + dim_text + 6]
+    fused = np.concatenate([img_emb, text_emb, add_feats], axis=1)
+
+    # 6. XGBoost prediction
+    proba = xgb_model.predict_proba(fused)[0]  # shape: [2]
+    pred_idx = int(np.argmax(proba))
+    label = "KRITIS" if pred_idx == 1 else "WASPADA"
+
+    return {
+        "prediction": label,
+        "probabilities": {
+            "WASPADA": float(proba[0]),
+            "KRITIS": float(proba[1]),
+        },
+    }
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host="0.0.0.0", port=7860)

requirements.txt

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+torch
+transformers
+xgboost
+openclip-torch
+timm
+Pillow
+numpy
+uvicorn
+fastapi
+pydantic
+python-multipart
+sentencepiece
+protobuf

xgb_full.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fa565eb70fed4e6a4099597161946283b789dec9355c8f11f4e4a9cc23d24bb6
+size 1046727