Create app.py

app.py

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+import streamlit as st
+from PIL import Image
+from huggingface_hub import HfApi, HfFolder, HfApi, ModelCard, whoami
+from huggingface_hub import InferenceClient  # Use 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
+# 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")
+    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()