Update app.py

app.py

@@ -2,26 +2,64 @@ import streamlit as st
 import cv2
 import numpy as np
 from PIL import Image
-import paddle
-from ppgan.apps import AnimeGANPredictor
+from io import BytesIO
+from openvino.runtime import Core
+import random
 
-st.set_page_config(page_title="AnimeGAN", layout="centered")
-st.title("🎨 Convert Your Photo to Anime Style")
+st.set_page_config(page_title="Anime & Cartoon Stylizer", layout="wide")
+st.title("🖼️ Anime & Cartoon Stylizer with OpenVINO")
+
+persona = st.radio("Choose your vibe:", ["Classic", "ChatBoy 💘", "Poetic 🌸"], horizontal=True)
+style = st.selectbox("Choose your style:", ["AnimeGAN", "CartoonGAN"])
 
 @st.cache_resource
-def load_model():
-    return AnimeGANPredictor()
+def load_model(style_name):
+    ie = Core()
+    model_path = "animegan.xml" if style_name == "AnimeGAN" else "cartoongan.xml"
+    model = ie.read_model(model=model_path)
+    compiled_model = ie.compile_model(model=model, device_name="CPU")
+    return compiled_model, compiled_model.input(0), compiled_model.output(0)
+
+def preprocess(image: Image.Image):
+    img = np.array(image.resize((256, 256))).astype(np.float32)
+    img = img / 127.5 - 1.0
+    img = np.transpose(img, (2, 0, 1))
+    return np.expand_dims(img, axis=0)
+
+def postprocess(output):
+    result = output.squeeze().transpose(1, 2, 0)
+    result = (result + 1.0) * 127.5
+    return np.clip(result, 0, 255).astype(np.uint8)
 
-predictor = load_model()
+def get_compliment():
+    return random.choice([
+        "✨ You look like the protagonist of a dreamy anime romance.",
+        "💘 That transformation? Utterly magical.",
+        "🌸 Your photo just bloomed into a masterpiece.",
+        "🎀 If Studio Ghibli saw this, they'd cast you instantly.",
+        "🫶 This anime version of you? It's giving main character energy."
+    ])
 
 uploaded_file = st.file_uploader("Upload a photo", type=["jpg", "jpeg", "png"])
 if uploaded_file:
     image = Image.open(uploaded_file).convert("RGB")
-    st.image(image, caption="Original Image", use_column_width=True)
-
-    if st.button("Generate Anime"):
-        img_np = np.array(image)
-        img_bgr = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
-        result = predictor.run(img_bgr)
-        result_rgb = cv2.cvtColor(result, cv2.COLOR_BGR2RGB)
-        st.image(result_rgb, caption="Anime Style", use_column_width=True)
+    st.subheader("📸 Original vs Stylized")
+    col1, col2 = st.columns(2)
+    col1.image(image, caption="Original", use_column_width=True)
+
+    if st.button("Generate Style"):
+        compiled_model, input_layer, output_layer = load_model(style)
+        input_tensor = preprocess(image)
+        output = compiled_model([input_tensor])[output_layer]
+        result = postprocess(output)
+        result_pil = Image.fromarray(result)
+        col2.image(result_pil, caption=f"{style} Style", use_column_width=True)
+
+        buf = BytesIO()
+        result_pil.save(buf, format="PNG")
+        st.download_button("Download Stylized Image", data=buf.getvalue(), file_name=f"{style.lower()}_style.png", mime="image/png")
+
+        if persona == "ChatBoy 💘":
+            st.markdown(f"**{get_compliment()}**")
+        elif persona == "Poetic 🌸":
+            st.markdown("🌙 *Your image now dances in the moonlight of a painted dream.*")