Update app.py

app.py

@@ -1,7 +1,6 @@
 import streamlit as st
 from PIL import Image
-from huggingface_hub import HfApi, HfFolder, HfApi, ModelCard, whoami
-from huggingface_hub import InferenceClient  # Use InferenceClient
+from huggingface_hub import InferenceClient
 import io
 import base64
 
@@ -9,12 +8,14 @@ import base64
 
 # No need for API token if running *within* a Space
 # The Space's environment will handle authentication
-# The model ID is implicitly available if the Space is built around that model
 
 # --- Image Encoding ---
 def encode_image(image):
     buffered = io.BytesIO()
-    image.save(buffered, format="JPEG")
+    # 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
 
@@ -30,12 +31,12 @@ def analyze_image_with_maira(image):
         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
-            model="microsoft/maira-2" # Specify the model
+            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}")  # General exception handling is sufficient here
         return None
 
 
@@ -65,7 +66,7 @@ def main():
                 st.warning("Unexpected API response format.")
                 st.write("Raw API response:", analysis_results)
         else:
-           st.error("Failed to get analysis results.")
+            st.error("Failed to get analysis results.")
 
     else:
         st.write("Please upload an image.")