Upload fake_news_detector.py with huggingface_hub

fake_news_detector.py

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+
+import joblib, pickle, numpy as np
+from huggingface_hub import hf_hub_download
+
+class FakeNewsDetector:
+    def __init__(self):
+        print("Loading Fake News Detector from Hugging Face...")
+        repo = "ghimirewe22/Classical_Model"
+        self.rf = joblib.load(hf_hub_download(repo, "rf_classifier.joblib"))
+        self.gb = joblib.load(hf_hub_download(repo, "gb_classifier.joblib"))
+        self.lr = joblib.load(hf_hub_download(repo, "lr_classifier.joblib"))
+        self.oc = joblib.load(hf_hub_download(repo, "oneclass_svm.joblib"))
+        self.vect = joblib.load(hf_hub_download(repo, "tfidf_vectorizer.joblib"))
+        self.pt = joblib.load(hf_hub_download(repo, "power_transformer.joblib"))
+        with open(hf_hub_download(repo, "vocab.pkl"), "rb") as f:
+            self.vocab = pickle.load(f)
+        print("Model loaded! Ready to detect fake news.")
+
+    def predict(self, text):
+        X = self.vect.transform([text]).toarray()
+        try:
+            X = self.pt.transform(X)
+        except: pass
+        
+        oc_vote = 0 if self.oc.predict(X)[0] == -1 else 1
+        votes = [
+            oc_vote,
+            int(self.lr.predict(X)[0]),
+            int(self.gb.predict(X)[0]),
+            int(self.rf.predict(X)[0])
+        ]
+        result = "REAL" if sum(votes) >= 2 else "FAKE"
+        prob = self._fake_prob(text)
+        return f"{result} ({prob:.1f}% fake)"
+
+    def _fake_prob(self, text):
+        X = self.vect.transform([text]).toarray()
+        try: X = self.pt.transform(X) except: pass
+        probs = []
+        for m in [self.lr, self.gb, self.rf]:
+            if hasattr(m, "predict_proba"):
+                probs.append(m.predict_proba(X)[0][0])
+        return np.mean(probs) * 100 if probs else 50.0