Update app.py

app.py

@@ -2,15 +2,17 @@ import streamlit as st
 from PIL import Image
 import torch
 from diffusers import StableDiffusionImg2VidPipeline
+import numpy as np
+import io
 
 # إعداد الصفحة
-st.set_page_config(page_title="Image to Video (SVD)", page_icon="🎥")
+st.set_page_config(page_title="Image to Video with Editing", page_icon="🎥")
 
 # عنوان الصفحة
 st.title("Stable Video Diffusion - Image to Video")
 
 # تعليمات
-st.write("Upload an image, and the model will generate a short video clip from it.")
+st.write("Upload an image to generate a video. You can also adjust settings for video generation.")
 
 # تحميل النموذج
 @st.cache_resource
@@ -24,6 +26,9 @@ pipe = load_model()
 # إدخال المستخدم
 uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
 
+# إعداد الخيارات لتعديل الفيديو
+frame_count = st.slider("Number of frames", min_value=10, max_value=50, value=25, step=5)
+
 if uploaded_image is not None:
     image = Image.open(uploaded_image)
     st.image(image, caption='Uploaded Image', use_column_width=True)
@@ -31,17 +36,22 @@ if uploaded_image is not None:
     if st.button('Generate Video'):
         # تحويل الصورة إلى فيديو
         with st.spinner("Generating video..."):
-            video_frames = pipe(image)
+            video_frames = pipe(image, num_frames=frame_count)
         
         st.success("Video generated successfully!")
-        
+
         # عرض الفيديو
         st.video(video_frames[0], format="video/mp4")
 
+        # تنزيل الفيديو
+        video_bytes = io.BytesIO()
+        video_frames[0].save(video_bytes, format="mp4")
+        st.download_button(label="Download Video", data=video_bytes.getvalue(), file_name="generated_video.mp4", mime="video/mp4")
+
 # تقديم بعض المعلومات حول النموذج
 st.write("""
 ### About the Model:
 SVD Image-to-Video is a latent diffusion model trained to generate short video clips from an image conditioning. 
-This model generates 25 frames at a resolution of 576x1024 given a context frame of the same size, fine-tuned from the SVD Image-to-Video [14 frames] model.
+This model generates frames at a resolution of 576x1024 given a context frame of the same size, fine-tuned from the SVD Image-to-Video [14 frames] model.
 The widely used f8-decoder is also fine-tuned for temporal consistency, making the output videos more stable and coherent.
 """)