Spaces:
Running
Running
| 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) | |
| 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 | |
| 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 | |
| return None | |
| # --- Streamlit App --- | |
| def main(): | |
| st.title("Chest X-ray Analysis with Maira-2 (Hugging Face Spaces)") | |
| st.write( | |
| "Upload a chest X-ray image. This app uses the Maira-2 model within this Hugging Face Space." | |
| ) | |
| uploaded_file = st.file_uploader("Choose a chest X-ray image (JPG, PNG)", type=["jpg", "jpeg", "png"]) | |
| if uploaded_file is not None: | |
| image = Image.open(uploaded_file) | |
| st.image(image, caption="Uploaded Image", use_column_width=True) | |
| with st.spinner("Analyzing image with Maira-2..."): | |
| analysis_results = analyze_image_with_maira(image) | |
| if analysis_results: | |
| # --- Results Display (VQA format) --- | |
| if isinstance(analysis_results, dict) and 'answer' in analysis_results: | |
| st.subheader("Findings:") | |
| st.write(analysis_results['answer']) | |
| else: | |
| st.warning("Unexpected API response format.") | |
| st.write("Raw API response:", analysis_results) | |
| else: | |
| st.error("Failed to get analysis results.") | |
| else: | |
| st.write("Please upload an image.") | |
| st.write("---") | |
| st.write("Disclaimer: For informational purposes only. Not medical advice.") | |
| if __name__ == "__main__": | |
| main() |