Update app.py

app.py

@@ -1,43 +1,20 @@
 import streamlit as st
 from transformers import pipeline
 
-classifier = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning")
+classifier = pipeline("token-classification", model="samrawal/medical-sentence-tokenizer")
 def main():
-    st.title("image-to-text")
+    st.title("Token classification")
 
-    with st.form("image"):
-        image = st.file_uploader('Choose a file')
+    with st.form("text_field"):
+        text = st.text_area('enter some text:')
         # clicked==True only when the button is clicked
         clicked = st.form_submit_button("Submit")
         if clicked:
-          results = classifier([image])
+          results = classifier([text])
           st.json(results)
-from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer
-
-model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
-feature_extractor = ViTFeatureExtractor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
-tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model.to(device)
-max_length = 16
-num_beams = 4
-gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
-def predict_step(image_paths):
-  images = []
-  for image_path in image_paths:
-    i_image = Image.open(image_path)
-    if i_image.mode != "RGB":
-      i_image = i_image.convert(mode="RGB")
-    images.append(i_image)
-  pixel_values = feature_extractor(images=images, return_tensors="pt").pixel_values
-  pixel_values = pixel_values.to(device)
-  output_ids = model.generate(pixel_values, **gen_kwargs)
-  preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
-  preds = [pred.strip() for pred in preds]
-  return preds
-predict_step(['doctor.e16ba4e4.jpg']
 
 if __name__ == "__main__":
     main()
     
+    
 """'audio-classification', 'automatic-speech-recognition', 'conversational', 'document-question-answering', 'feature-extraction', 'fill-mask', 'image-classification', 'image-segmentation', 'image-to-text', 'ner', 'object-detection', 'question-answering', 'sentiment-analysis', 'summarization', 'table-question-answering', 'text-classification', 'text-generation', 'text2text-generation', 'token-classification', 'translation', 'visual-question-answering', 'vqa', 'zero-shot-classification', 'zero-shot-image-classification', 'translation_XX_to_YY'"""