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Dockerfile

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+FROM python:3.10-slim
+
+WORKDIR /app
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    libglib2.0-0 \
+    libsm6 \
+    libxext6 \
+    libxrender-dev \
+    && rm -rf /var/lib/apt/lists/*
+
+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"]

Fer2013.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bdeb325637a94b721c4eee4493c020a9a5800fc21104d25570024e3b874d0ba9
+size 157500720

README.md

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 ---
-title: Emotions
-emoji: 💻
-colorFrom: yellow
-colorTo: pink
+title: Facial Emotion Recognition
+emoji: 😃
+colorFrom: blue
+colorTo: purple
 sdk: docker
+app_port: 7860
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Facial Emotion Recognition API
+
+API لتصنيف المشاعر من صور الوجه باستخدام موديل CNN مدرب على dataset **FER2013**.
+
+## Endpoints
+
+- `GET /` — health check
+- `POST /predict` — ارفعي صورة (multipart/form-data, field name: `file`) وترجعلك:
+  - `predicted_emotion`: التوقع (Angry, Disgust, Fear, Happy, Sad, Surprise, Neutral)
+  - `confidence`: نسبة الثقة
+  - `all_probabilities`: احتمالات كل الفئات
+
+## ملاحظات
+
+- الموديل بيتحمل من ملف `Fer2013.h5` الموجود في نفس الـ Space.
+- الصورة بتتحول تلقائيًا Grayscale وتتعمل resize لـ 48x48 قبل التوقع.
+- Swagger UI متاح على `/docs` بعد نشر الـ Space.

app.py

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+import os
+import numpy as np
+from fastapi import FastAPI, File, UploadFile
+from fastapi.middleware.cors import CORSMiddleware
+from PIL import Image
+import io
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+
+app = FastAPI(title="Facial Emotion Recognition API")
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+MODEL_PATH = "Fer2013.h5"
+EMOTION_LABELS = ["Angry", "Disgust", "Fear", "Happy", "Sad", "Surprise", "Neutral"]
+
+model = None
+
+
+@app.on_event("startup")
+def load_fer_model():
+    global model
+    if not os.path.exists(MODEL_PATH):
+        raise FileNotFoundError(
+            f"Model file '{MODEL_PATH}' not found. Make sure it is uploaded to the Space root."
+        )
+    model = load_model(MODEL_PATH, compile=False)
+    print("Model loaded successfully.")
+
+
+def preprocess_image(image_bytes: bytes) -> np.ndarray:
+    image = Image.open(io.BytesIO(image_bytes)).convert("L")  # grayscale
+    image = image.resize((48, 48))
+    array = np.array(image, dtype=np.float32) / 255.0
+    array = array.reshape(1, 48, 48, 1)
+    return array
+
+
+@app.get("/")
+def root():
+    return {"status": "ok", "message": "Facial Emotion Recognition API is running."}
+
+
+@app.post("/predict")
+async def predict(file: UploadFile = File(...)):
+    contents = await file.read()
+    input_array = preprocess_image(contents)
+
+    predictions = model.predict(input_array)[0]
+    predicted_idx = int(np.argmax(predictions))
+
+    result = {
+        "predicted_emotion": EMOTION_LABELS[predicted_idx],
+        "confidence": float(predictions[predicted_idx]),
+        "all_probabilities": {
+            EMOTION_LABELS[i]: float(predictions[i]) for i in range(len(EMOTION_LABELS))
+        },
+    }
+    return result

requirements.txt

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+fastapi==0.110.0
+uvicorn[standard]==0.29.0
+tensorflow-cpu==2.15.0
+numpy==1.26.4
+pillow==10.3.0
+python-multipart==0.0.9