Upload 3 files

app.py

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+import streamlit as st
+import os
+import zipfile
+from ultralytics import YOLO
+from PIL import Image
+import numpy as np
+
+# Title
+st.title("🔍 Deepfake Image Detection using YOLOv8")
+
+# Extract model if not extracted
+model_dir = "yolo_model"
+zip_path = "yolo_trained_model.zip"
+
+if not os.path.exists(model_dir):
+    with zipfile.ZipFile(zip_path, 'r') as zip_ref:
+        zip_ref.extractall(model_dir)
+    st.success("✅ Model unzipped successfully.")
+
+# Load model
+model_files = [f for f in os.listdir(model_dir) if f.endswith('.pt')]
+if model_files:
+    model_path = os.path.join(model_dir, model_files[0])
+    model = YOLO(model_path)
+    st.success("✅ YOLOv8 Model loaded!")
+else:
+    st.error("❌ No .pt file found in the unzipped model folder.")
+
+# Upload image
+uploaded_image = st.file_uploader("📁 Upload an Image", type=["jpg", "jpeg", "png"])
+
+if uploaded_image is not None:
+    image = Image.open(uploaded_image).convert("RGB")
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+
+    # Prediction button
+    if st.button("Detect Deepfake"):
+        with st.spinner("Analyzing..."):
+            results = model.predict(image)
+
+        # Draw boxes on the image
+        result_image = results[0].plot()  # This plots bounding boxes
+
+        # Convert to PIL Image and display
+        result_pil = Image.fromarray(result_image[..., ::-1])  # BGR to RGB
+        st.image(result_pil, caption="Detection Result", use_column_width=True)
+
+        # Optional: Show label info
+        st.write("🔎 Detected Labels:", results[0].names)

requirements.txt

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+streamlit
+ultralytics
+pillow

yolo_trained_model.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9e1904c908edcf867b661b1f9019d8e51ab1fe8372c9587be8122917d7383eb8
+size 11329168