Add Flask Docker API

Dockerfile

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+# Use Python base image
+FROM python:3.10-slim
+
+# Set work directory
+WORKDIR /app
+
+# Install dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy app files
+COPY . .
+
+# Expose API port
+EXPOSE 7860
+
+# Run Flask app
+CMD ["python", "app.py"]

README.md

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----
-title: Skin Classifier Api
-emoji: 🐢
-colorFrom: yellow
-colorTo: blue
-sdk: docker
-pinned: false
-short_description: Skin type classifier
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Skin Type Classifier API
+
+A Flask API for predicting skin type (dry or oily) from images.
+
+## Endpoints
+
+- `GET /` - Test if API is running
+- `POST /predict` - Upload an image for prediction
+
+## Example POST request
+Send a multipart form data with `file` as the key for the image.
+
+## Hosted on Hugging Face Spaces

app.py

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+from flask import Flask, request, jsonify
+from PIL import Image
+import torch
+import torchvision.transforms as transforms
+from transformers import AutoModelForImageClassification
+
+# Load model
+MODEL_NAME = "anismizi/skin-type-classifier"
+model = AutoModelForImageClassification.from_pretrained(MODEL_NAME)
+model.eval()
+
+# Define preprocessing
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                         std=[0.229, 0.224, 0.225])
+])
+
+app = Flask(__name__)
+
+@app.route("/")
+def home():
+    return jsonify({"message": "Skin Type Classifier API is running!"})
+
+@app.route("/predict", methods=["POST"])
+def predict():
+    if 'file' not in request.files:
+        return jsonify({"error": "No file provided"}), 400
+    file = request.files['file']
+    try:
+        image = Image.open(file.stream).convert("RGB")
+        input_tensor = transform(image).unsqueeze(0)
+        with torch.no_grad():
+            outputs = model(input_tensor)
+            probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
+            predicted_class = probabilities.argmax().item()
+            confidence = probabilities[0][predicted_class].item()
+
+        labels = ["dry", "oily"]
+        result = {
+            "predicted_class": labels[predicted_class],
+            "confidence": round(confidence * 100, 2)
+        }
+        return jsonify(result)
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=7860)

requirements.txt

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+flask
+torch
+torchvision
+Pillow
+transformers