Initial working FastAPI deployment

Dockerfile

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+FROM python:3.9
+
+RUN useradd -m -u 1000 user
+USER user
+
+ENV PATH="/home/user/.local/bin:$PATH"
+WORKDIR /app
+
+COPY --chown=user . .
+
+RUN pip install --no-cache-dir --upgrade pip && \
+    pip install --no-cache-dir -r requirements.txt
+
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

main.py

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+from fastapi import FastAPI, File, UploadFile, Form
+from fastapi.middleware.cors import CORSMiddleware
+from typing import Optional
+import numpy as np
+import tensorflow as tf
+from PIL import Image
+import io
+import keras
+import os
+import gdown
+from keras.saving import register_keras_serializable
+
+# Enable unsafe deserialization for custom Lambda layers
+keras.config.enable_unsafe_deserialization()
+
+# --- CUSTOM FUNCTION ---
+@register_keras_serializable(package="Custom", name="custom_max_pool")
+def custom_max_pool(x):
+    return tf.reduce_max(x, axis=[1, 2], keepdims=True)
+
+# --- CONFIG ---
+IMG_SIZE = 224
+NUM_CLASSES = 23
+
+MODEL_PATHS = {
+    "EfficientNetB3": "saved_models/EfficientNetB3_recovered.keras",
+    "ResNet50": "saved_models/ResNet50_recovered.keras",
+    "MobileNetV2": "saved_models/MobileNetV2_recovered.keras",
+    "DenseNet121": "saved_models/DenseNet121_recovered.keras"
+}
+
+MODEL_URLS = {
+    "EfficientNetB3": "https://drive.google.com/uc?id=1jP4-HoFFbGIFugqgRpVVt0V3LhOoKVkY",
+    "ResNet50": "https://drive.google.com/uc?id=1yv4duVkGHTyLEpw92CCJcUy9Y1VxC6Ec",
+    "MobileNetV2": "https://drive.google.com/uc?id=1fJtogp6fH7F2Wa2YvN_KTklgK2-ufqMN",
+    "DenseNet121": "https://drive.google.com/uc?id=1lJ0nlTP7cMTglEM6XIaTvAEZHVJ4dsN8"
+}
+
+MODEL_WEIGHTS = {
+    "EfficientNetB3": 0.260,
+    "ResNet50": 0.256,
+    "MobileNetV2": 0.222,
+    "DenseNet121": 0.261
+}
+
+PREPROCESS_FUNCS = {
+    "EfficientNetB3": tf.keras.applications.efficientnet.preprocess_input,
+    "ResNet50": tf.keras.applications.resnet.preprocess_input,
+    "MobileNetV2": tf.keras.applications.mobilenet_v2.preprocess_input,
+    "DenseNet121": tf.keras.applications.densenet.preprocess_input
+}
+
+# --- FASTAPI SETUP ---
+app = FastAPI()
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # Restrict origins in production
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# --- HEALTH CHECK ENDPOINT ---
+@app.get("/")
+def root():
+    return {"status": "🩺 App is running."}
+
+# --- GLOBAL MODELS DICT ---
+models = {}
+
+# --- LOAD MODELS AT STARTUP ---
+@app.on_event("startup")
+def load_models():
+    global models
+    print("Loading models...")
+    os.makedirs("saved_models", exist_ok=True)
+
+    for name, path in MODEL_PATHS.items():
+        if not os.path.exists(path):
+            print(f"Downloading {name} from Google Drive...")
+            gdown.download(MODEL_URLS[name], path, quiet=False)
+
+        print(f"Loading {name}...")
+        models[name] = tf.keras.models.load_model(path)
+    
+    print("✅ All models loaded.")
+
+# --- UTILS ---
+def read_imagefile(file) -> Image.Image:
+    image = Image.open(io.BytesIO(file))
+    return image.convert("RGB")
+
+# --- ENSEMBLE PREDICTION ---
+def predict_ensemble(image):
+    image = image.resize((IMG_SIZE, IMG_SIZE))
+    image_array = np.array(image)
+    ensemble_pred = np.zeros((NUM_CLASSES,))
+    for name, model in models.items():
+        preproc = PREPROCESS_FUNCS[name]
+        img_proc = preproc(image_array.copy())
+        img_proc = np.expand_dims(img_proc, axis=0)
+        pred = model.predict(img_proc, verbose=0)[0]
+        ensemble_pred += pred * MODEL_WEIGHTS[name]
+    return ensemble_pred
+
+# --- METADATA-BASED ADJUSTMENT ---
+def adjust_with_metadata(predictions, metadata):
+    adjusted = []
+    for pred in predictions:
+        label = pred["label"]
+        score = pred["confidence"]
+
+        try:
+            age = int(metadata["age"])
+            condition = metadata.get("condition", "").lower()
+            skin_type = metadata.get("skin_type", "").lower()
+
+            if "acne" in label.lower() and age > 40:
+                score *= 0.6
+            if "eczema" in label.lower() and skin_type == "dry":
+                score *= 1.2
+            if "warts" in label.lower() and age < 12:
+                score *= 1.3
+            if "fungal" in label.lower() and "itchy" in condition:
+                score *= 1.2
+        except Exception as e:
+            print("Metadata adjustment error:", e)
+
+        adjusted.append({"label": label, "confidence": score})
+
+    adjusted = sorted(adjusted, key=lambda x: x["confidence"], reverse=True)
+    return adjusted[:3]
+
+# --- PREDICT ENDPOINT ---
+@app.post("/predict/")
+async def predict(
+    file: UploadFile = File(...),
+    age: int = Form(...),
+    race: str = Form(...),
+    gender: str = Form(...),
+    skin_color: str = Form(...),
+    skin_type: str = Form(...),
+    condition_description: str = Form(...)
+):
+    image = read_imagefile(await file.read())
+    prediction = predict_ensemble(image)
+
+    class_labels = [
+        "Acne and Rosacea Photos",
+        "Actinic Keratosis Basal Cell Carcinoma and other Malignant Lesions",
+        "Atopic Dermatitis Photos",
+        "Bullous Disease Photos",
+        "Cellulitis Impetigo and other Bacterial Infections",
+        "Eczema Photos",
+        "Exanthems and Drug Eruptions",
+        "Hair Loss Photos Alopecia and other Hair Diseases",
+        "Herpes HPV and other STDs Photos",
+        "Light Diseases and Disorders of Pigmentation",
+        "Lupus and other Connective Tissue diseases",
+        "Melanoma Skin Cancer Nevi and Moles",
+        "Nail Fungus and other Nail Disease",
+        "Poison Ivy Photos and other Contact Dermatitis",
+        "Psoriasis pictures Lichen Planus and related diseases",
+        "Scabies Lyme Disease and other Infestations and Bites",
+        "Seborrheic Keratoses and other Benign Tumors",
+        "Systemic Disease",
+        "Tinea Ringworm Candidiasis and other Fungal Infections",
+        "Urticaria Hives",
+        "Vascular Tumors",
+        "Vasculitis Photos",
+        "Warts Molluscum and other Viral Infections"
+    ]
+
+    top3_indices = prediction.argsort()[-3:][::-1]
+    top_preds = [
+        {
+            "label": class_labels[i],
+            "confidence": float(prediction[i])
+        }
+        for i in top3_indices
+    ]
+
+    metadata = {
+        "age": age,
+        "race": race,
+        "gender": gender,
+        "skin_color": skin_color,
+        "skin_type": skin_type,
+        "condition": condition_description
+    }
+
+    adjusted_preds = adjust_with_metadata(top_preds, metadata)
+
+    return {
+        "prediction": adjusted_preds,
+        "metadata": metadata
+    }

requirements.txt

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+fastapi
+uvicorn[standard]
+tensorflow
+pillow
+numpy
+gdown