Update app.py

app.py

@@ -37,7 +37,7 @@ MAX_DURATION = round(MAX_FRAMES_MODEL / FIXED_FPS, 1)
 # =========================================================
 print("Loading pipeline components...")
 
-# تحميل المكونات أولاً بدون نقلها للـ GPU لتوفير الذاكرة أثناء التحميل
+# Load models in bfloat16
 transformer = WanTransformer3DModel.from_pretrained(
     MODEL_ID,
     subfolder="transformer",
@@ -61,7 +61,6 @@ pipe = WanImageToVideoPipeline.from_pretrained(
     token=HF_TOKEN
 )
 
-# نقل الموديل إلى CUDA الآن
 print("Moving to CUDA...")
 pipe = pipe.to("cuda")
 
@@ -94,7 +93,6 @@ except Exception as e:
 # QUANTIZATION & AOT OPTIMIZATION
 # =========================================================
 print("Applying quantization...")
-# تنظيف الذاكرة قبل العمليات الثقيلة
 torch.cuda.empty_cache()
 gc.collect()
 
@@ -164,10 +162,9 @@ def get_num_frames(duration_seconds: float):
 # =========================================================
 # MAIN GENERATION FUNCTION
 # =========================================================
-# زيادة الوقت المسموح به إلى 180 ثانية لتجنب التايم أوت
 @spaces.GPU(duration=180)
 def generate_video(
-    input_image,
+    input_image_path,  # Receives file path now, not PIL object
     prompt,
     steps=4,
     negative_prompt=default_negative_prompt,
@@ -178,14 +175,22 @@ def generate_video(
     randomize_seed=False,
     progress=gr.Progress(track_tqdm=True),
 ):
-    # تنظيف الذاكرة في بداية الدالة
+    # Cleanup memory
     gc.collect()
     torch.cuda.empty_cache()
     
     try:
-        if input_image is None:
+        # 1. Validation checks
+        if not input_image_path:
             raise gr.Error("Please upload an input image.")
-            
+        
+        if not os.path.exists(input_image_path):
+             raise gr.Error("Image file not found! Please re-upload the image.")
+
+        # 2. Manual Image Opening
+        # We open it inside the function to avoid connection timeouts
+        input_image = Image.open(input_image_path).convert("RGB")
+
         num_frames = get_num_frames(duration_seconds)
         current_seed = random.randint(0, MAX_SEED) if randomize_seed else int(seed)
         resized_image = resize_image(input_image)
@@ -210,23 +215,23 @@ def generate_video(
         
         export_to_video(output_frames_list, video_path, fps=FIXED_FPS)
         
-        # تنظيف الذاكرة بعد الانتهاء
+        # Cleanup
         del output_frames_list
+        del input_image
+        del resized_image
         torch.cuda.empty_cache()
         
         return video_path, current_seed
 
     except Exception as e:
-        # طباعة الخطأ الحقيقي في الكونسول
         print(f"Error during generation: {e}")
-        # إعادة رفع الخطأ ليظهر للمستخدم
         raise gr.Error(f"Generation failed: {str(e)}")
 
 # =========================================================
 # GRADIO UI
 # =========================================================
 
-# تعريف كود جوجل أناليتكس ليتم حقنه
+# Google Analytics Script
 ga_script = """
 <script async src="https://www.googletagmanager.com/gtag/js?id=G-1TD40BVM04"></script>
 <script>
@@ -281,7 +286,8 @@ with gr.Blocks(theme=gr.themes.Soft(), head=ga_script) as demo:
 
     with gr.Row():
         with gr.Column():
-            input_image_component = gr.Image(type="pil", label="Input Image")
+            # CHANGE: type="filepath" fixes the file not found error
+            input_image_component = gr.Image(type="filepath", label="Input Image")
             prompt_input = gr.Textbox(label="Prompt", value=default_prompt_i2v)
             
             duration_seconds_input = gr.Slider(