Update app.py

app.py

@@ -137,42 +137,58 @@ def detect_deepfake_image(image_path):
     label = "FAKE" if confidence > 0.5 else "REAL"
     return {"label": label, "score": confidence}
 
+# # ---- Fake News Detection Section ----
+# st.subheader("📝 Fake News Detection")
+# news_input = st.text_area("Enter News Text:", placeholder="Type here...")
+
+# # Manually verified facts database (you can expand this)
+# fact_check_db = {
+#     "elon musk was born in 1932": "FAKE",
+#     "earth revolves around the sun": "REAL",
+#     "the moon is made of cheese": "FAKE",
+# }
+
+# def check_manual_facts(text):
+#     text_lower = text.lower().strip()
+#     return fact_check_db.get(text_lower, None)
+
+# if st.button("Check News"):
+#     st.write("🔍 Processing...")
+
+#     # Check if the news is in the fact-check database
+#     manual_result = check_manual_facts(news_input)
+#     if manual_result:
+#         if manual_result == "FAKE":
+#             st.error(f"⚠️ Result: This news is **FAKE** (Verified by Database).")
+#         else:
+#             st.success(f"✅ Result: This news is **REAL** (Verified by Database).")
+#     else:
+#         # Use AI model if fact is not in the database
+#         prediction = fake_news_detector(news_input)
+#         label = prediction[0]['label'].lower()
+#         confidence = prediction[0]['score']
+
+#         if "fake" in label or confidence < 0.5:
+#             st.error(f"⚠️ Result: This news is **FAKE**. (Confidence: {confidence:.2f})")
+#         else:
+#             st.success(f"✅ Result: This news is **REAL**. (Confidence: {confidence:.2f})")
 # ---- Fake News Detection Section ----
 st.subheader("📝 Fake News Detection")
 news_input = st.text_area("Enter News Text:", placeholder="Type here...")
 
-# Manually verified facts database (you can expand this)
-fact_check_db = {
-    "elon musk was born in 1932": "FAKE",
-    "earth revolves around the sun": "REAL",
-    "the moon is made of cheese": "FAKE",
-}
-
-def check_manual_facts(text):
-    text_lower = text.lower().strip()
-    return fact_check_db.get(text_lower, None)
-
 if st.button("Check News"):
     st.write("🔍 Processing...")
 
-    # Check if the news is in the fact-check database
-    manual_result = check_manual_facts(news_input)
-    if manual_result:
-        if manual_result == "FAKE":
-            st.error(f"⚠️ Result: This news is **FAKE** (Verified by Database).")
-        else:
-            st.success(f"✅ Result: This news is **REAL** (Verified by Database).")
-    else:
-        # Use AI model if fact is not in the database
-        prediction = fake_news_detector(news_input)
-        label = prediction[0]['label'].lower()
-        confidence = prediction[0]['score']
-
-        if "fake" in label or confidence < 0.5:
-            st.error(f"⚠️ Result: This news is **FAKE**. (Confidence: {confidence:.2f})")
-        else:
-            st.success(f"✅ Result: This news is **REAL**. (Confidence: {confidence:.2f})")
+    # Use AI model to classify the news
+    prediction = fake_news_detector(news_input)
+    label = prediction[0]['label'].lower()
+    confidence = prediction[0]['score']
 
+    # Ensure correct classification
+    if label in ["fake", "false", "negative"] or confidence < 0.5:
+        st.error(f"⚠️ Result: This news is **FAKE**. (Confidence: {confidence:.2f})")
+    else:
+        st.success(f"✅ Result: This news is **REAL**. (Confidence: {confidence:.2f})")
 # ---- Deepfake Image Detection Section ----
 st.subheader("📸 Deepfake Image Detection")
 uploaded_image = st.file_uploader("Upload an Image", type=["jpg", "png", "jpeg"])