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app.py

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+# app.py
+import streamlit as st
+from PIL import Image
+import io
+from face_swap import swap_faces
+
+# --- Page config ---
+st.set_page_config(page_title="🎭 Face Swap Demo", layout="centered")
+
+# --- Hero section ---
+st.markdown(
+    """
+    <style>
+    .title {
+        font-size: 2.5rem;
+        font-weight: bold;
+        text-align: center;
+        margin-bottom: 0.5rem;
+    }
+    .subtitle {
+        text-align: center;
+        font-size: 1.2rem;
+        color: #555;
+        margin-bottom: 2rem;
+    }
+    </style>
+    <div class="title">🎭 Face Swap Demo</div>
+    <div class="subtitle">Upload two images and see the magic of AI face-swapping</div>
+    """,
+    unsafe_allow_html=True,
+)
+
+# --- Upload section in cards ---
+col1, col2 = st.columns(2)
+with col1:
+    st.markdown("### 🧑 Source Face")
+    src_file = st.file_uploader(
+        "Upload Source Image", type=["jpg", "jpeg", "png"], key="src"
+    )
+with col2:
+    st.markdown("### 🎯 Target Image")
+    tgt_file = st.file_uploader(
+        "Upload Target Image", type=["jpg", "jpeg", "png"], key="tgt"
+    )
+
+# --- Options ---
+with st.sidebar:
+    st.header("⚙️ Options")
+    resize_max = st.number_input(
+        "Resize images to max dimension (px)", value=800, min_value=200, max_value=2000
+    )
+    download_name = st.text_input("Download filename", value="face_swap_result.png")
+
+def load_image(file):
+    img = Image.open(file).convert("RGB")
+    # resize if large
+    w, h = img.size
+    max_dim = max(w, h)
+    if max_dim > resize_max:
+        scale = resize_max / max_dim
+        img = img.resize((int(w * scale), int(h * scale)), Image.LANCZOS)
+    return img
+
+# --- Run button ---
+if st.button("🚀 Run Face Swap", use_container_width=True):
+    if not src_file or not tgt_file:
+        st.error("⚠️ Please upload both source and target images.")
+    else:
+        src_img = load_image(src_file)
+        tgt_img = load_image(tgt_file)
+
+        st.markdown("### 🔍 Preview Images")
+        st.image([src_img, tgt_img], caption=["Source", "Target"], width=300)
+
+        with st.spinner("Running face-swap... please wait ⏳"):
+            try:
+                result_pil = swap_faces(src_img, tgt_img)
+
+                st.success("✨ Done! Check the result below")
+
+                # Show side-by-side comparison
+                col_a, col_b = st.columns(2)
+                with col_a:
+                    st.markdown("**Before**")
+                    st.image(tgt_img, use_column_width=True)
+                with col_b:
+                    st.markdown("**After (Swapped)**")
+                    st.image(result_pil, use_column_width=True)
+
+                # Download button
+                buf = io.BytesIO()
+                result_pil.save(buf, format="PNG")
+                st.download_button(
+                    "⬇️ Download result",
+                    buf.getvalue(),
+                    file_name=download_name,
+                    mime="image/png",
+                    use_container_width=True,
+                )
+
+            except Exception as e:
+                st.error(f"❌ Face-swap failed: {e}")
+                st.exception(e)
+
+# --- Footer ---
+st.markdown(
+    """
+    <hr>
+    <div style="text-align:center; color: gray; font-size: 0.9rem;">
+        Built with ❤️ using Streamlit and InsightFace
+    </div>
+    """,
+    unsafe_allow_html=True,
+)
+

face_swap.py

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+import numpy as np
+from PIL import Image
+import insightface
+from insightface.app import FaceAnalysis
+
+import os
+import requests
+
+MODEL_URL = "https://huggingface.co/kaizma/face-swap-inswapper/resolve/da20be1c8ba9b074d52c6a0540f8935d3e3605e5/inswapper_128.onnx"
+MODEL_PATH = "inswapper_128.onnx"
+
+# Download model if not exists
+if not os.path.exists(MODEL_PATH):
+    print("Downloading model...")
+    r = requests.get(MODEL_URL, stream=True)
+    with open(MODEL_PATH, "wb") as f:
+        for chunk in r.iter_content(chunk_size=8192):
+            f.write(chunk)
+    print("Model downloaded.")
+
+
+def swap_faces(src_pil, tgt_pil):
+    # Convert PIL to numpy (BGR)
+    src = np.array(src_pil)[:, :, ::-1].copy()
+    tgt = np.array(tgt_pil)[:, :, ::-1].copy()
+
+    # Initialize face analysis and swapper
+    app = FaceAnalysis(name='buffalo_l')
+    app.prepare(ctx_id=0, det_size=(640, 640))
+    model_path = r"C:\Users\Dell\Desktop\Face Swap\inswapper_128.onnx"
+    swapper = insightface.model_zoo.get_model(model_path)
+
+    # Detect faces
+    src_faces = app.get(src)
+    tgt_faces = app.get(tgt)
+    if len(src_faces) == 0 or len(tgt_faces) == 0:
+        raise ValueError("No face detected in one of the images.")
+
+    source_face = src_faces[0]
+    res = tgt.copy()
+    for face in tgt_faces:
+        res = swapper.get(res, face, source_face, paste_back=True)
+
+    # Convert back to PIL (RGB)
+    res_pil = Image.fromarray(res[:, :, ::-1])
+    return res_pil