initial commit

app.py

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+import streamlit as st
+from PIL import Image
+from transformers import BlipForConditionalGeneration, BlipProcessor
+
+processor = BlipProcessor.from_pretrained("nathansutton/generate-cxr")
+model = BlipForConditionalGeneration.from_pretrained("nathansutton/generate-cxr")
+
+def humanize_report(report: str) -> str:
+    report = report.replace("impression :","IMPRESSION:\n").replace("findings :","FINDINGS:\n").replace("indication :","INDICATION:\n")
+    sentences = [x.split("\n") for x in report.split(".") if x]
+    sentences = [item for sublist in sentences for item in sublist]
+    sentences = [x.strip().capitalize() if ':' not in x else x for x in sentences]
+    return ".  ".join(sentences).replace(":.",":").replace("IMPRESSION:","\n\nIMPRESSION:\n\n").replace("FINDINGS:","\n\nFINDINGS:\n\n").replace("INDICATION:","INDICATION:\n\n")
+
+indication = st.text_input("What is the indication for this study")
+img_file_buffer = st.file_uploader("Upload a single view from a Chest X-Ray (JPG preferred)")
+if img_file_buffer is not None and indication is not None:
+
+    image = Image.open(img_file_buffer)
+    st.image(image, use_column_width=True)
+    inputs = processor(
+        images=Image.open(img_file_buffer), 
+        text='indication:' + indication,
+        return_tensors="pt"
+    )
+    output = model.generate(**inputs,max_length=512)
+    report = processor.decode(output[0], skip_special_tokens=True)
+    st.write(humanize_report(report))
+
+

requirements.txt

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+streamlit>=1.17.0,<1.18
+transformers[torch]>=4.26.0,<4.27
+pillow>=9.4.0,<9.5