Update app.py

app.py

@@ -1,119 +1,122 @@
-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
-
+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
+import re
+
+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:
+    text = str(text).lower()                                      
+    text = re.sub(r'http\S+|www\.\S+', '', text)                  
+    text = re.sub(r'@\w+|#\w+', '', text)                         
+    text = re.sub(r'[^a-z\s]', ' ', text)                        
+    text = re.sub(r'\s+', ' ', text).strip()
+    return " ".join(text.split())
+
+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 (ambil CLS token-nya)
+    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, :] # take the CLS token only
+    text_emb = text_emb.cpu().numpy()
+
+    # 3. encode image (EVA-CLIP image embedding)
+    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 (longitude, latitude, location_cluster, hour, dayofweek, month)
+    add_feats = np.array(
+        [[longitude, latitude, location_cluster, hour, dayofweek, month]],
+        dtype=np.float32,
+    )
+
+    # 5. concatenate (early fusion): [image_emb, text_emb, add_feats]
+    fused = np.concatenate([img_emb, text_emb, add_feats], axis=1)
+
+    # 6. predict
+    proba = xgb_model.predict_proba(fused)[0]
+    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)