Update app.py
Browse files
app.py
CHANGED
@@ -173,22 +173,27 @@ def detect_deepfake_image(image_path):
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# else:
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# st.success(f"β
Result: This news is **REAL**. (Confidence: {confidence:.2f})")
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# ---- Fake News Detection Section ----
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st.subheader("π Fake News Detection")
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news_input = st.text_area("Enter News Text:", placeholder="Type here...")
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if st.button("Check News"):
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st.write("π Processing...")
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#
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prediction = fake_news_detector(news_input)
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label = prediction[0]['label'].lower()
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confidence = prediction[0]['score']
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#
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if label
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st.error(f"β οΈ Result: This news is **FAKE**. (Confidence: {confidence:.2f})")
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-
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st.success(f"β
Result: This news is **REAL**. (Confidence: {confidence:.2f})")
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# ---- Deepfake Image Detection Section ----
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st.subheader("πΈ Deepfake Image Detection")
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uploaded_image = st.file_uploader("Upload an Image", type=["jpg", "png", "jpeg"])
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# else:
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# st.success(f"β
Result: This news is **REAL**. (Confidence: {confidence:.2f})")
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# ---- Fake News Detection Section ----
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import streamlit as st
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+
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st.subheader("π Fake News Detection")
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news_input = st.text_area("Enter News Text:", placeholder="Type here...")
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if st.button("Check News"):
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st.write("π Processing...")
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# Get AI model prediction
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prediction = fake_news_detector(news_input)
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label = prediction[0]['label'].lower()
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confidence = prediction[0]['score']
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# Correct classification logic
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if label == "fake" or confidence > 0.6: # Adjusted confidence threshold
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st.error(f"β οΈ Result: This news is **FAKE**. (Confidence: {confidence:.2f})")
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elif label == "real" and confidence > 0.6:
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st.success(f"β
Result: This news is **REAL**. (Confidence: {confidence:.2f})")
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else:
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st.warning(f"β οΈ Unable to classify with high confidence. Please verify manually. (Confidence: {confidence:.2f})")
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# ---- Deepfake Image Detection Section ----
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st.subheader("πΈ Deepfake Image Detection")
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uploaded_image = st.file_uploader("Upload an Image", type=["jpg", "png", "jpeg"])
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