create app.py

app.py

@@ -0,0 +1,52 @@
+import streamlit as st
+import torch
+from transformers import AutoProcessor, VisionEncoderDecoderModel
+import requests
+from PIL import Image
+import pandas as pd
+
+st.write("Processing HCFA claims")
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+processor = AutoProcessor.from_pretrained("Laskari-Naveen/HCFA_99")
+model = VisionEncoderDecoderModel.from_pretrained("Laskari-Naveen/HCFA_99").to(device)
+
+
+def run_prediction(image, model, processor):
+    pixel_values = processor(image, return_tensors="pt").pixel_values
+    task_prompt = "<s>"
+    decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids
+    outputs = model.generate(
+        pixel_values.to(device),
+        decoder_input_ids=decoder_input_ids.to(device),
+        max_length=model.decoder.config.max_position_embeddings,
+        early_stopping=True,
+        pad_token_id=processor.tokenizer.pad_token_id,
+        eos_token_id=processor.tokenizer.eos_token_id,
+        use_cache=True,
+        num_beams=2,
+        bad_words_ids=[[processor.tokenizer.unk_token_id]],
+        return_dict_in_generate=True,
+    )
+    # process output
+    prediction = processor.batch_decode(outputs.sequences)[0]
+    prediction = processor.token2json(prediction)
+    return prediction, outputs
+
+def split_and_expand(row):
+    if row['Key'] == "33_Missing_Teeth":
+        keys = [row['Key']]
+        values = row['Value'].split(';')[0]
+    else:
+        keys = [row['Key']] * len(row['Value'].split(';'))
+        values = row['Value'].split(';')
+    return pd.DataFrame({'Key': keys, 'Value': values})
+
+
+uploaded_file = st.file_uploader("Choose a file")
+if uploaded_file is not None:
+    content = uploaded_file.read()
+    st.image(uploaded_file)
+    image = Image.open(uploaded_file).convert("RGB")
+    prediction, output = run_prediction(image, model, processor)
+
+    st.dataframe(prediction, width=600)