Update app.py

app.py

@@ -2,41 +2,32 @@ import streamlit as st
 from PIL import Image
 from huggingface_hub import InferenceClient
 import io
-import base64
 
 # --- Configuration (Simplified for Spaces) ---
 
-# No need for API token if running *within* a Space
-# The Space's environment will handle authentication
-
-# --- Image Encoding ---
-def encode_image(image):
-    buffered = io.BytesIO()
-    # Convert to RGB *before* saving as JPEG
-    if image.mode == "RGBA":
-        image = image.convert("RGB")
-    image.save(buffered, format="JPEG")  # Save as JPEG
-    img_str = base64.b64encode(buffered.getvalue()).decode()
-    return img_str
-
-
 # --- Model Interaction (using InferenceClient) ---
 
 def analyze_image_with_maira(image):
     """Analyzes the image using the Maira-2 model via the Hugging Face Inference API.
     """
     try:
-        encoded_image = encode_image(image)
+        # Prepare image data - no need to encode for InferenceClient if sending bytes directly
+        image_bytes = io.BytesIO()
+        if image.mode == "RGBA":  # Handle RGBA images (if any)
+            image = image.convert("RGB")
+        image.save(image_bytes, format="JPEG")
+        image_bytes = image_bytes.getvalue()  # Get the bytes
+
         client = InferenceClient()  # No token needed inside the Space
         result = client.question_answering(
             question="Analyze this chest X-ray image and provide detailed findings. Include any abnormalities, their locations, and potential diagnoses. Be as specific as possible.",
-            image=encoded_image,  # Pass the encoded image directly
+            image=image_bytes,  # Pass the image bytes directly
             model="microsoft/maira-2"  # Specify the model
         )
         return result
 
     except Exception as e:
-        st.error(f"An error occurred: {e}")  # General exception handling is sufficient here
+        st.error(f"An error occurred: {e}")
         return None