Update app.py

app.py

@@ -16,23 +16,13 @@ model = Qwen2VLForConditionalGeneration.from_pretrained(
 )
 processor = AutoProcessor.from_pretrained("./Qwen2-VL-7B-Instruct")
 
-def array_to_image_path(image_array):
+def array_to_image(image_array):
     if image_array is None:
         raise ValueError("No image provided. Please upload an image before submitting.")
     # Convert numpy array to PIL Image
-    img = Image.fromarray(np.uint8(image_array))
+    image = Image.fromarray(np.uint8(image_array)).convert("RGB")
     
-    # Generate a unique filename using timestamp
-    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-    filename = f"image_{timestamp}.png"
-    
-    # Save the image
-    img.save(filename)
-    
-    # Get the full path of the saved image
-    full_path = os.path.abspath(filename)
-    
-    return full_path
+    return image
 
 def generate_embeddings(text):
     model = SentenceTransformer('./all-MiniLM-L6-v2')
@@ -40,9 +30,7 @@ def generate_embeddings(text):
     return embeddings
 
 def describe_image(image_array):
-    image_path = array_to_image_path(image_array)
-    os.chmod(image_path, stat.S_IROTH)
-    image = Image.open(image_path)
+    image = array_to_image(image_array)
     
     messages = [
         {