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c800d7e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 | import os
import torch
import easyocr
from flask import Flask, request, jsonify, render_template
from flask_cors import CORS
import os
from PIL import Image
from transformers import AutoImageProcessor, AutoModelForImageClassification
app = Flask(__name__)
CORS(app)
MODEL_NAME = "prithivMLmods/deepfake-detector-model-v1"
UPLOAD_FOLDER = "uploads"
os.makedirs(UPLOAD_FOLDER, exist_ok=True)
print("Loading AI Models...")
processor = AutoImageProcessor.from_pretrained(MODEL_NAME)
model = AutoModelForImageClassification.from_pretrained(MODEL_NAME)
reader = easyocr.Reader(['en'], gpu=False)
print("All Models Loaded Successfully!")
@app.route("/health")
def health():
return {"status": "ok"}, 200
@app.route("/about")
def about():
return render_template("about.html")
@app.route("/")
def home():
return render_template("index.html")
@app.route("/terms")
def terms():
return render_template("terms.html")
@app.route("/analyze", methods=["POST"])
def analyze():
file_path = None
try:
if "image" not in request.files:
return jsonify({"error": "No image uploaded"}), 400
file = request.files["image"]
if file.filename == "":
return jsonify({"error": "Empty file"}), 400
file_path = os.path.join(UPLOAD_FOLDER, file.filename)
file.save(file_path)
img = Image.open(file_path)
width, height = img.size
bytes_size = os.path.getsize(file_path)
size_mb = round(bytes_size / (1024 * 1024), 2)
img_rgb = img.convert("RGB")
result = reader.readtext(file_path)
text = " ".join([r[1] for r in result]).strip()
inputs = processor(images=img_rgb, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
probs = torch.nn.functional.softmax(outputs.logits, dim=1).squeeze().tolist()
fake_score = probs[0]
real_score = probs[1]
is_real = real_score > fake_score
confidence = round((real_score if is_real else fake_score) * 100, 2)
return jsonify({
"analysis": {
"is_real": bool(is_real),
"confidence": confidence,
"reason": "Likely Real" if is_real else "Likely AI Generated"
},
"text": text,
"metadata": {
"width": int(width),
"height": int(height),
"size_mb": float(size_mb)
},
"labels": ["deepfake-check", "ocr"]
})
except Exception as e:
print("ERROR:", str(e))
return jsonify({"error": str(e)}), 500
finally:
if file_path and os.path.exists(file_path):
try:
os.remove(file_path)
except:
pass
if __name__ == "__main__":
port = int(os.environ.get("PORT", 5000))
app.run(host='0.0.0.0', port=port) |