commit pertama docker api

Dockerfile

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+FROM python:3.11-slim
+
+WORKDIR /app
+
+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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+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel, Field
+import tensorflow as tf
+import numpy as np
+
+# =====================================================
+# LOAD MODEL
+# =====================================================
+
+MODEL_PATH = "model_cnn_best.h5"
+
+model = tf.keras.models.load_model(MODEL_PATH)
+
+# =====================================================
+# FASTAPI
+# =====================================================
+
+app = FastAPI(
+    title="CNN Pose Classifier API",
+    version="1.0.0",
+    description="API klasifikasi aktivitas menggunakan CNN + MediaPipe Pose"
+)
+
+# =====================================================
+# CORS
+# =====================================================
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],       # ganti dengan domain website jika production
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# =====================================================
+# REQUEST MODEL
+# =====================================================
+
+class PoseInput(BaseModel):
+
+    features: list[float] = Field(
+        ...,
+        min_length=66,
+        max_length=66,
+        description="66 pose features (x,y landmark)"
+    )
+
+# =====================================================
+# ROOT
+# =====================================================
+
+@app.get("/")
+def home():
+
+    return {
+        "status": "running",
+        "message": "CNN Pose Classifier API",
+        "input_shape": [66],
+        "output": "binary classification"
+    }
+
+# =====================================================
+# HEALTH CHECK
+# =====================================================
+
+@app.get("/health")
+
+def health():
+
+    return {
+        "status": "healthy"
+    }
+
+# =====================================================
+# PREDICTION
+# =====================================================
+
+@app.post("/predict")
+
+def predict(data: PoseInput):
+
+    try:
+
+        # -----------------------------
+        # Convert ke numpy
+        # -----------------------------
+
+        features = np.array(
+            data.features,
+            dtype=np.float32
+        )
+
+        # -----------------------------
+        # Validasi jumlah fitur
+        # -----------------------------
+
+        if features.shape != (66,):
+
+            raise HTTPException(
+                status_code=400,
+                detail="Input harus terdiri dari 66 fitur."
+            )
+
+        # -----------------------------
+        # Validasi NaN dan Inf
+        # -----------------------------
+
+        if np.isnan(features).any():
+
+            raise HTTPException(
+                status_code=400,
+                detail="Input mengandung NaN."
+            )
+
+        if np.isinf(features).any():
+
+            raise HTTPException(
+                status_code=400,
+                detail="Input mengandung Infinity."
+            )
+
+        # -----------------------------
+        # Reshape sesuai model
+        # (batch,66,1)
+        # -----------------------------
+
+        features = features.reshape(
+            1,
+            66,
+            1
+        )
+
+        # -----------------------------
+        # Predict
+        # -----------------------------
+
+        prediction = model.predict(
+            features,
+            verbose=0
+        )
+
+        probability = float(prediction[0][0])
+
+        if probability >= 0.5:
+            label = "berbahaya"
+        else:
+            label = "aman"
+
+        return {
+
+            "success": True,
+
+            "prediction": label,
+
+            "probability": round(probability, 4),
+
+            "confidence": round(
+                max(probability, 1 - probability),
+                4
+            )
+
+        }
+
+    except HTTPException:
+
+        raise
+
+    except Exception as e:
+
+        raise HTTPException(
+            status_code=500,
+            detail=str(e)
+        )

model_cnn_best.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3cb82eebaf13172dcb1f3c644eee1b670c4ad02ca646243844b1049e928c623b
+size 900328

requirements.txt

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+fastapi
+uvicorn
+tensorflow==2.17.0
+numpy
+pydantic
+h5py