Spaces:
Sleeping
Sleeping
Create app.py
Browse files
app.py
ADDED
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import streamlit as st
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from PIL import Image
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from huggingface_hub import HfApi, HfFolder, HfApi, ModelCard, whoami
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from huggingface_hub import InferenceClient # Use InferenceClient
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import io
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import base64
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# --- Configuration (Simplified for Spaces) ---
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# No need for API token if running *within* a Space
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# The Space's environment will handle authentication
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# The model ID is implicitly available if the Space is built around that model
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# --- Image Encoding ---
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def encode_image(image):
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buffered = io.BytesIO()
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image.save(buffered, format="JPEG")
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img_str = base64.b64encode(buffered.getvalue()).decode()
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return img_str
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# --- Model Interaction (using InferenceClient) ---
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def analyze_image_with_maira(image):
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"""Analyzes the image using the Maira-2 model via the Hugging Face Inference API.
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"""
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try:
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encoded_image = encode_image(image)
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client = InferenceClient() # No token needed inside the Space
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result = client.question_answering(
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question="Analyze this chest X-ray image and provide detailed findings. Include any abnormalities, their locations, and potential diagnoses. Be as specific as possible.",
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image=encoded_image, # Pass the encoded image directly
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model="microsoft/maira-2" # Specify the model
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)
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return result
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except Exception as e:
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st.error(f"An error occurred: {e}") # General exception handling is sufficient here
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return None
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# --- Streamlit App ---
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def main():
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st.title("Chest X-ray Analysis with Maira-2 (Hugging Face Spaces)")
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st.write(
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"Upload a chest X-ray image. This app uses the Maira-2 model within this Hugging Face Space."
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)
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uploaded_file = st.file_uploader("Choose a chest X-ray image (JPG, PNG)", type=["jpg", "jpeg", "png"])
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if uploaded_file is not None:
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image = Image.open(uploaded_file)
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st.image(image, caption="Uploaded Image", use_column_width=True)
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with st.spinner("Analyzing image with Maira-2..."):
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analysis_results = analyze_image_with_maira(image)
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if analysis_results:
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# --- Results Display (VQA format) ---
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if isinstance(analysis_results, dict) and 'answer' in analysis_results:
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st.subheader("Findings:")
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st.write(analysis_results['answer'])
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else:
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st.warning("Unexpected API response format.")
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st.write("Raw API response:", analysis_results)
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else:
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st.error("Failed to get analysis results.")
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else:
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st.write("Please upload an image.")
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st.write("---")
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st.write("Disclaimer: For informational purposes only. Not medical advice.")
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if __name__ == "__main__":
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main()
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