Upload 5 files

changed to 9 blocks

app.py

@@ -1,77 +1,77 @@
-import streamlit as st
-import torch
-import numpy as np
-from PIL import Image
-import torchvision.transforms as T
-from model import load_generator
-from io import BytesIO
-
-st.set_page_config(page_title="Summer ↔ Winter CycleGAN", page_icon="🏔️", layout="centered")
-st.title("🏔️ Summer ↔ Winter Translation")
-st.markdown("Upload a landscape photo and convert it between **summer** and **winter**.")
-
-@st.cache_resource
-def get_generators():
-    device = "cpu"
-    gen_a2b = load_generator("gen_a2b_fp16.pth", device)
-    gen_b2a = load_generator("gen_b2a_fp16.pth", device)
-    return gen_a2b, gen_b2a, device
-
-gen_a2b, gen_b2a, device = get_generators()
-st.success(f"Model loaded on **{device}**", icon="✅")
-
-MEAN = (0.5, 0.5, 0.5)
-STD  = (0.5, 0.5, 0.5)
-
-to_tensor = T.Compose([
-    T.Resize((256, 256)),
-    T.ToTensor(),
-    T.Normalize(MEAN, STD),
-])
-
-def to_pil(tensor):
-    img = tensor.squeeze(0).cpu().float()
-    for i, (m, s) in enumerate(zip(MEAN, STD)):
-        img[i] = img[i] * s + m
-    img = torch.clamp(img, 0, 1)
-    return T.ToPILImage()(img)
-
-direction = st.radio(
-    "Translation direction",
-    ["☀️ Summer → ❄️ Winter", "❄️ Winter → ☀️ Summer"],
-    horizontal=True,
-)
-
-uploaded = st.file_uploader("Upload landscape photo (JPG/PNG)", type=["jpg", "jpeg", "png"])
-
-if uploaded is not None:
-    try:
-        raw = uploaded.read()
-        input_img = Image.open(BytesIO(raw)).convert("RGB")
-        
-        col1, col2 = st.columns(2)
-        with col1:
-            st.subheader("Input")
-            st.image(input_img, use_container_width=True)
-
-        with st.spinner("Translating..."):
-            tensor = to_tensor(input_img).unsqueeze(0).to(device)
-            generator = gen_a2b if "Summer" in direction.split("→")[0] else gen_b2a
-            with torch.no_grad():
-                output_tensor = generator(tensor)
-            output_img = to_pil(output_tensor)
-
-        with col2:
-            st.subheader("Output")
-            st.image(output_img, use_container_width=True)
-
-        buf = BytesIO()
-        output_img.save(buf, format="PNG")
-        st.download_button("⬇️ Download result", buf.getvalue(), "translated.png", "image/png")
-
-    except Exception as error:
-        st.error(f"Error: {error}")
-        st.exception(error)
-
-st.markdown("---")
-st.markdown("**Model:** CycleGAN ResNet-6 · **Train:** Yosemite + Alpine (Unsplash)")
+import streamlit as st
+import torch
+import numpy as np
+from PIL import Image
+import torchvision.transforms as T
+from model import load_generator
+from io import BytesIO
+
+st.set_page_config(page_title="Summer ↔ Winter CycleGAN", page_icon="🏔️", layout="centered")
+st.title("🏔️ Summer ↔ Winter Translation")
+st.markdown("Upload a landscape photo and convert it between **summer** and **winter**.")
+
+@st.cache_resource
+def get_generators():
+    device = "cpu"
+    gen_a2b = load_generator("gen_a2b_fp16.pth", device)
+    gen_b2a = load_generator("gen_b2a_fp16.pth", device)
+    return gen_a2b, gen_b2a, device
+
+gen_a2b, gen_b2a, device = get_generators()
+st.success(f"Model loaded on **{device}**", icon="✅")
+
+MEAN = (0.5, 0.5, 0.5)
+STD  = (0.5, 0.5, 0.5)
+
+to_tensor = T.Compose([
+    T.Resize((256, 256)),
+    T.ToTensor(),
+    T.Normalize(MEAN, STD),
+])
+
+def to_pil(tensor):
+    img = tensor.squeeze(0).cpu().float()
+    for i, (m, s) in enumerate(zip(MEAN, STD)):
+        img[i] = img[i] * s + m
+    img = torch.clamp(img, 0, 1)
+    return T.ToPILImage()(img)
+
+direction = st.radio(
+    "Translation direction",
+    ["☀️ Summer → ❄️ Winter", "❄️ Winter → ☀️ Summer"],
+    horizontal=True,
+)
+
+uploaded = st.file_uploader("Upload landscape photo (JPG/PNG)", type=["jpg", "jpeg", "png"])
+
+if uploaded is not None:
+    try:
+        raw = uploaded.read()
+        input_img = Image.open(BytesIO(raw)).convert("RGB")
+        
+        col1, col2 = st.columns(2)
+        with col1:
+            st.subheader("Input")
+            st.image(input_img, use_container_width=True)
+
+        with st.spinner("Translating..."):
+            tensor = to_tensor(input_img).unsqueeze(0).to(device)
+            generator = gen_a2b if "Summer" in direction.split("→")[0] else gen_b2a
+            with torch.no_grad():
+                output_tensor = generator(tensor)
+            output_img = to_pil(output_tensor)
+
+        with col2:
+            st.subheader("Output")
+            st.image(output_img, use_container_width=True)
+
+        buf = BytesIO()
+        output_img.save(buf, format="PNG")
+        st.download_button("⬇️ Download result", buf.getvalue(), "translated.png", "image/png")
+
+    except Exception as error:
+        st.error(f"Error: {error}")
+        st.exception(error)
+
+st.markdown("---")
+st.markdown("**Model:** CycleGAN ResNet-9 blocks (64 channels) · **Train / Test:** Yosemite / Alpine (Unsplash)")

gen_a2b_fp16.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:34cd9e43b8d797b91fe295fb6cb81569d88eb1246a2838e23681395e85035fc0
-size 15686583
+oid sha256:da5c03b451d035d13e78a7de49b0b41dee5d6c06c60b8d522f9d7652a376e64c
+size 22771407

gen_b2a_fp16.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:2ef6e8fa18dac09a8df1452e82616c64f77d476c472df794e082ed381abd6f00
-size 15686583
+oid sha256:68096562fc25dcecf71a6dd7f2f6299a4f263941eae27af31acd6bd3275d9b2b
+size 22771407

model.py

@@ -18,7 +18,7 @@ class ResidualBlock(nn.Module):
         return x + self.block(x)
  
 class ResNetGenerator(nn.Module):
-    def __init__(self, in_channels=3, out_channels=3, n_filters=64, n_res_blocks=6):
+    def __init__(self, in_channels=3, out_channels=3, n_filters=64, n_res_blocks=9):
         super().__init__()
         model = [
             nn.ReflectionPad2d(3),
@@ -66,4 +66,4 @@ def load_generator(path, device="cpu"):
     state_dict = {k: v.float() for k, v in state_dict.items()}
     gen.load_state_dict(state_dict)
     gen.to(device).eval()
-    return gen
+    return gen