Update app.py

app.py

@@ -7,12 +7,16 @@ from huggingface_hub import InferenceClient
 # Initialize the Hugging Face Inference Client
 client = InferenceClient("microsoft/llava-med-7b-delta")
 
-# Function to encode image as base64
-def image_to_base64(image):
-    buffered = io.BytesIO()
-    image.save(buffered, format="PNG")
-    img_str = base64.b64encode(buffered.getvalue()).decode('utf-8')
-    return img_str
+# Custom Field for Base64 Encoded Image
+class Base64ImageField(gr.Field):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def preprocess(self, image):
+        buffered = io.BytesIO()
+        image.save(buffered, format="PNG")
+        img_str = base64.b64encode(buffered.getvalue()).decode('utf-8')
+        return img_str
 
 # Function to interact with LLAVA model
 def respond(
@@ -35,13 +39,13 @@ def respond(
     messages.append({"role": "user", "content": message})
 
     if image:
-        # Convert image(s) to base64
+        # Convert image(s) to base64 using the custom field
         if isinstance(image, Image.Image):
-            image_b64 = image_to_base64(image)
+            image_b64 = Base64ImageField().preprocess(image)
             messages.append({"role": "user", "content": "Image uploaded", "image": image_b64})
         else:
             for img in image:
-                image_b64 = image_to_base64(img)
+                image_b64 = Base64ImageField().preprocess(img)
                 messages.append({"role": "user", "content": "Image uploaded", "image": image_b64})
 
     # Call Hugging Face model for response