Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,89 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+from PIL import Image
+import torch
+import os
+import time
+import tempfile
+from huggingface_hub import snapshot_download
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+class ImageGenerator:
+    def __init__(self, ae_path, dit_path, qwen2vl_model_path, max_length=640):
+        # Initialize the model with the provided paths
+        self.ae_path = ae_path
+        self.dit_path = dit_path
+        self.qwen2vl_model_path = qwen2vl_model_path
+        self.max_length = max_length
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.load_model()
+
+    def load_model(self):
+        # Load model weights or any necessary model setup here
+        pass
+    
+    def to_cuda(self):
+        # Move model to GPU if available
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        # Example: Loading your model (use actual code to load)
+        self.model = torch.load(self.ae_path, map_location=self.device)
+        # Additional model loading logic for your specific case
+
+def inference(prompt, image, seed, size_level, model):
+    # Add model prediction logic here
+    # Example: Pass image and prompt to the model to generate output
+    # Modify according to your actual model's inference code
+    result_image = image  # Placeholder, replace with actual generation logic
+    used_seed = seed if seed != -1 else int(time.time())  # Use random seed if -1
+    return result_image, used_seed
+
+# Set page config for better UI layout
+st.set_page_config(page_title="Ghibli style", layout="centered")
+st.title("🖼️ Ghibli style for Free : AI Image Editing")
+st.markdown("Ghibli style images with AI.")
+
+# === User Inputs ===
+prompt = "Turn into an illustration in Studio Ghibli style"
+uploaded_image = st.file_uploader("📤 Upload an Image", type=["jpg", "jpeg", "png"])
+seed = st.number_input("🎲 Random Seed (-1 for random)", value=-1, step=1)
+size_level = st.number_input("📐 Size Level (minimum 512)", value=512, min_value=512, step=32)
+
+generate_button = st.button("🚀 Generate")
+
+# === Load Model (Cached) ===
+@st.cache_resource
+def load_model():
+    repo = "stepfun-ai/Step1X-Edit"
+    local_dir = "./step1x_weights"
+    os.makedirs(local_dir, exist_ok=True)
+    snapshot_download(repo_id=repo, local_dir=local_dir, local_dir_use_symlinks=False)
+
+    model = ImageGenerator(
+        ae_path=os.path.join(local_dir, 'vae.safetensors'),
+        dit_path=os.path.join(local_dir, "step1x-edit-i1258.safetensors"),
+        qwen2vl_model_path='Qwen/Qwen2.5-VL-7B-Instruct',
+        max_length=640
+    )
+    return model
+
+image_edit_model = load_model()
+
+# === Inference and Image Display ===
+if generate_button and uploaded_image is not None:
+    input_image = Image.open(uploaded_image).convert("RGB")
+    # Resize image for faster inference (adjust to your model's requirements)
+    input_image.thumbnail((size_level, size_level))
+    
+    with st.spinner("🔄 Generating edited image..."):
+        start = time.time()
+        try:
+            result_image, used_seed = inference(prompt, input_image, seed, size_level, image_edit_model)
+            end = time.time()
+
+            st.success(f"✅ Done in {end - start:.2f} seconds — Seed used: {used_seed}")
+            
+            # Save and display the result in temporary file
+            with tempfile.NamedTemporaryFile(dir="/tmp", delete=False, suffix=".png") as temp_file:
+                result_image.save(temp_file.name)
+                st.image(temp_file.name, caption="🖼️ Edited Image", use_column_width=True)
+        except Exception as e:
+            st.error(f"❌ Inference failed: {e}")
+            st.stop()