Update app.py

app.py

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+from fastapi import FastAPI
+from flask import Flask, request, jsonify
+from pydantic import BaseModel
+from transformers import pipeline
+from PIL import Image
+import joblib
+import re
+import string
+import io
+import uvicorn
+from threading import Thread
+
+# Initialize Flask & FastAPI
+app = Flask(__name__)
+api = FastAPI()
+
+# ✅ Load NSFW Image Classification Model
+pipe = pipeline("image-classification", model="LukeJacob2023/nsfw-image-detector")
+
+# ✅ Load Toxic Text Classification Model
+try:
+    model = joblib.load("toxic_classifier.pkl")
+    vectorizer = joblib.load("vectorizer.pkl")
+    print("✅ Model & Vectorizer Loaded Successfully!")
+except Exception as e:
+    print(f"❌ Error: {e}")
+    exit(1)
+
+# 📌 Text Input Data Model
+class TextInput(BaseModel):
+    text: str
+
+# 🔹 Text Preprocessing Function
+def preprocess_text(text):
+    text = text.lower()
+    text = re.sub(r'\d+', '', text)  # Remove numbers
+    text = text.translate(str.maketrans('', '', string.punctuation))  # Remove punctuation
+    return text.strip()
+
+# 📌 NSFW Image Classification API (Flask)
+@app.route('/classify_image', methods=['POST'])
+def classify_image():
+    if 'file' not in request.files:
+        return jsonify({"error": "No file uploaded"}), 400
+    
+    file = request.files['file']
+    image = Image.open(io.BytesIO(file.read()))
+    results = pipe(image)
+    
+    classification_label = max(results, key=lambda x: x['score'])['label']
+    nsfw_labels = {"sexy", "porn", "hentai"}
+    nsfw_status = "NSFW" if classification_label in nsfw_labels else "SFW"
+
+    return jsonify({"status": nsfw_status, "results": results})
+
+# 📌 Toxic Text Classification API (FastAPI)
+@api.post("/classify_text/")
+async def classify_text(data: TextInput):
+    try:
+        processed_text = preprocess_text(data.text)
+        text_vectorized = vectorizer.transform([processed_text])
+        prediction = model.predict(text_vectorized)
+        result = "Toxic" if prediction[0] == 1 else "Safe"
+        return {"prediction": result}
+    except Exception as e:
+        return {"error": str(e)}
+
+# 🔥 Run both servers using Gunicorn
+def run_flask():
+    app.run(host="0.0.0.0", port=5000)
+
+def run_fastapi():
+    uvicorn.run(api, host="0.0.0.0", port=8000)
+
+if __name__ == "__main__":
+    Thread(target=run_flask).start()
+    Thread(target=run_fastapi).start()
+