Create app.py

app.py

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+from flask import Flask, request, jsonify
+from flask_cors import CORS
+from transformers import pipeline
+from PIL import Image
+import pytesseract
+import base64
+import io
+
+app = Flask(__name__)
+CORS(app)
+
+# Load pretrained BERT model
+classifier = pipeline("text-classification", model="jy46604790/Fake-News-Bert-Detect", tokenizer="jy46604790/Fake-News-Bert-Detect")
+
+@app.route("/predict", methods=["POST"])
+def predict_text():
+    data = request.json
+    text = data.get("text", "")
+    if not text.strip():
+        return jsonify({"error": "No text provided"}), 400
+
+    result = classifier(text)[0]
+    label = "Real" if result['label'] == 'LABEL_1' else "Fake"
+    return jsonify({"label": label, "confidence": round(result["score"] * 100, 2)})
+
+@app.route("/predict-image", methods=["POST"])
+def predict_image():
+    data = request.json
+    image_b64 = data.get("image")
+
+    if not image_b64:
+        return jsonify({"error": "No image provided"}), 400
+
+    try:
+        img_bytes = base64.b64decode(image_b64)
+        img = Image.open(io.BytesIO(img_bytes))
+        text = pytesseract.image_to_string(img)
+    except Exception as e:
+        return jsonify({"error": "Invalid image data"}), 400
+
+    if not text.strip():
+        return jsonify({"error": "No text found in image"}), 400
+
+    result = classifier(text)[0]
+    label = "Real" if result['label'] == 'LABEL_1' else "Fake"
+    return jsonify({"label": label, "confidence": round(result["score"] * 100, 2), "extracted_text": text})