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Dockerfile

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+FROM python:3.11-slim
+
+WORKDIR /app
+
+RUN pip install --no-cache-dir torch>=2.6.0 --index-url https://download.pytorch.org/whl/cpu
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY app.py .
+
+EXPOSE 7860
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

@@ -1,10 +1,23 @@
 ---
-title: Sentiment Analysis V1
-emoji: 🌖
-colorFrom: gray
-colorTo: red
+title: StockPro Sentiment V1
+emoji: 📊
+colorFrom: blue
+colorTo: indigo
 sdk: docker
 pinned: false
+app_port: 7860
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# StockPro Sentiment V1 (Comparison)
+
+Uses `ihsan31415/indo-roBERTa-financial-sentiment` — Indonesian financial RoBERTa, 97.49% accuracy.
+
+## API
+
+### `POST /predict`
+
+```json
+{ "texts": ["BBCA cetak laba bersih Rp50 triliun"] }
+```
+
+### `GET /health`

app.py

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+import os
+from contextlib import asynccontextmanager
+
+from fastapi import FastAPI, HTTPException, Request
+from pydantic import BaseModel
+from transformers import pipeline
+
+MODEL_NAME = "ihsan31415/indo-roBERTa-financial-sentiment"
+API_KEY = os.getenv("API_KEY")
+
+# Label mapping — flipped: 0=Positive, 1=Neutral, 2=Negative
+LABEL_MAP = {
+    "label_0": "positive",
+    "label_1": "neutral",
+    "label_2": "negative",
+    "positive": "positive",
+    "neutral": "neutral",
+    "negative": "negative",
+}
+
+classifier = None
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    global classifier
+    print(f"Loading model: {MODEL_NAME}")
+    classifier = pipeline("text-classification", model=MODEL_NAME)
+    print("Model loaded.")
+    yield
+
+
+app = FastAPI(title="StockPro Sentiment V1", lifespan=lifespan)
+
+
+class PredictRequest(BaseModel):
+    texts: list[str]
+
+
+@app.post("/predict")
+async def predict(body: PredictRequest, request: Request):
+    if API_KEY:
+        key = request.headers.get("X-API-Key")
+        if key != API_KEY:
+            raise HTTPException(status_code=401, detail="Invalid API key")
+
+    texts = body.texts
+    if not texts:
+        raise HTTPException(status_code=400, detail="texts must not be empty")
+    if len(texts) > 20:
+        raise HTTPException(status_code=400, detail="Maximum 20 texts per request")
+
+    if classifier is None:
+        raise HTTPException(status_code=503, detail="Model not loaded yet")
+
+    predictions = classifier(texts, truncation=True, max_length=512)
+
+    results = []
+    for text, pred in zip(texts, predictions):
+        label = LABEL_MAP.get(pred["label"].lower(), "neutral")
+        results.append({
+            "text": text,
+            "sentiment": label,
+            "score": round(pred["score"], 4),
+        })
+
+    return {"results": results}
+
+
+@app.get("/health")
+def health():
+    return {"status": "ok", "model_loaded": classifier is not None, "model": MODEL_NAME}

requirements.txt

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+fastapi==0.115.5
+uvicorn[standard]==0.32.1
+transformers>=4.51.0
+torch>=2.6.0