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model.py

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+import joblib
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.decomposition import TruncatedSVD
+
+# Define file paths
+MODEL_PATH = r"C:\Users\LENOVO\Desktop\ai-content-detector-extension\models\random_forest_model.pkl"
+VECTORIZER_PATH = r"C:\Users\LENOVO\Desktop\ai-content-detector-extension\models\vectorizer.pkl"
+SVD_PATH = r"C:\Users\LENOVO\Desktop\ai-content-detector-extension\models\svd.pkl"
+
+try:
+    # Load the trained model, vectorizer, and SVD transformer
+    model = joblib.load(MODEL_PATH)
+    vectorizer = joblib.load(VECTORIZER_PATH)
+    svd = joblib.load(SVD_PATH)
+    print("✅ Model, vectorizer, and SVD loaded successfully!")
+except Exception as e:
+    print(f"❌ Error loading model files: {e}")
+    exit(1)
+
+def predict_text(text: str) -> int:
+    """Preprocess input text and predict using trained model."""
+    try:
+        X_tfidf = vectorizer.transform([text])  # Convert to TF-IDF
+        X_reduced = svd.transform(X_tfidf)  # Apply dimensionality reduction
+        prediction = model.predict(X_reduced)[0]  # Predict label (0 or 1)
+
+        probability = float(model.predict_proba(X_reduced)[0][1])
+        print(probability) #printing probability
+
+        return {"generated": int(prediction), "probability": probability}
+    except Exception as e:
+        print(f"❌ Prediction error: {e}")
+        return {"generated": -1, "probability": 0.0}
+        

server.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+from fastapi.middleware.cors import CORSMiddleware
+from api.model import predict_text
+
+app = FastAPI(title="AI Content Detector API")
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["chrome-extension://*"],  # Allow all Chrome extensions
+    allow_credentials=True,
+    allow_methods=["*"],  # Allow all HTTP methods (GET, POST, etc.)
+    allow_headers=["*"],  # Allow all headers
+)
+
+class TextInput(BaseModel):
+    text: str
+
+@app.post("/predict/")
+async def predict(input_data: TextInput):
+    """Receive text and return AI detection result."""
+    result = predict_text(input_data.text)
+
+    print(f"RESULT\n{result}")
+
+    return {
+        "generated": result["generated"], 
+        "probability": result["probability"]
+    }
+
+# if __name__ == "__main__":
+#     import uvicorn
+#     uvicorn.run(app, host="0.0.0.0", port=8000)