Update app.py

app.py

@@ -16,23 +16,6 @@ model_id = "microsoft/Phi-3-vision-128k-instruct"
 model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True, torch_dtype="auto")
 processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True) 
 
-from PIL import Image
-import base64
-import zlib
-from io import BytesIO
-
-def decode_and_decompress_image(base64_string):
-    # Decode the Base64 string to bytes
-    compressed_data = base64.b64decode(base64_string.encode('utf-8'))
-
-    # Decompress the data using zlib
-    img_bytes = zlib.decompress(compressed_data)
-
-    # Open the image from bytes
-    img_buffer = BytesIO(img_bytes)
-    image = Image.open(img_buffer)
-    
-    return image
 
 PLACEHOLDER = """
 <div style="padding: 30px; text-align: center; display: flex; flex-direction: column; align-items: center;">
@@ -87,8 +70,7 @@ def bot_streaming(message, history):
         conversation.append({"role": "user", "content": message['text']})
     print(f"prompt is -\n{conversation}")
     prompt = processor.tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True)
-    # image = Image.open(image)
-    image = decode_and_decompress_image(image)
+    image = Image.open(image)
     inputs = processor(prompt, image, return_tensors="pt").to("cuda:0") 
 
     streamer = TextIteratorStreamer(processor, **{"skip_special_tokens": True, "skip_prompt": True, 'clean_up_tokenization_spaces':False,})