Add pillow

app.py

@@ -1,20 +1,41 @@
 from vllm import LLM, SamplingParams
 import gradio as gr
+
+from PIL import Image
+from io import BytesIO
+import base64
+import requests
+
 repo_id = "mistral-community/pixtral-12b-240910" #Replace to the model you would like to use
 sampling_params = SamplingParams(max_tokens=8192, temperature=0.7)
 max_tokens_per_img = 4096
 max_img_per_msg = 5
 
+def encode_image(image: Image.Image, image_format="PNG") -> str:
+    im_file = BytesIO()
+    image.save(im_file, format=image_format)
+    im_bytes = im_file.getvalue()
+    im_64 = base64.b64encode(im_bytes).decode("utf-8")
+    return im_64
+
+
 # @spaces.GPU #[uncomment to use ZeroGPU]
 def infer(image_url, prompt, progress=gr.Progress(track_tqdm=True)):
     # tokenize image urls and text
     llm = LLM(model="mistralai/Pixtral-12B-2409",
               tokenizer_mode="mistral",
+              max_model_len=65536,
+              max_num_batched_tokens=max_img_per_msg * max_tokens_per_img,
               limit_mm_per_prompt={"image": max_img_per_msg})  # Name or path of your model
+
+    image = Image.open(BytesIO(requests.get(image_url).content))
+    image = image.resize((3844, 2408))
+    new_image_url = f"data:image/png;base64,{encode_image(image, image_format='PNG')}"
+
     messages = [
         {
             "role": "user",
-            "content": [{"type": "text", "text": prompt}, {"type": "image_url", "image_url": {"url": image_url}}]
+            "content": [{"type": "text", "text": prompt}, {"type": "image_url", "image_url": {"url": new_image_url}}]
         },
     ]
 

requirements.txt

@@ -1 +1,2 @@
 vllm==0.6.1
+Pillow