Add application file

app.py

@@ -0,0 +1,34 @@
+
+import streamlit as st
+from transformers import pipeline
+from PIL import Image
+checkpoint = "openai/clip-vit-large-patch14"
+classifier = pipeline(model=checkpoint, task="zero-shot-image-classification")
+def get_best_label(predictions):
+    max_score = 0
+    label = ""
+    for p in predictions:
+        if p['score'] > max_score:
+            max_score = p['score']
+            label = p['label']
+    return label, max_score
+
+st.markdown('<h1 style="color:black;">Document Classifier</h1>', unsafe_allow_html=True)
+st.markdown('<h2 style="color:gray;">This model can classify input image to the following categories:</h2>', unsafe_allow_html=True)
+st.markdown('<h3 style="color:gray;"> <ul> <li>Invoice</li> <li>Bank statement</li> <li>Credit bureau</li> </ul> </h3>', unsafe_allow_html=True)
+
+
+
+upload= st.file_uploader('Insert image for classification', type=['png','jpg'])
+c1, c2= st.columns(2)
+if upload is not None:
+  image = Image.open(upload)
+  c1.header('Input Image')
+  c1.image(image)
+  print("c1", c1)
+  print("c2", c2)
+  c2.header('Output')
+  c2.subheader('Predicted class :')
+  predictions = classifier(image, candidate_labels=["invoice, receipt", "bank statement, financial statement", "credit report"])
+  c2.subheader('Predicted class :' + str(get_best_label(predictions)))
+  c2.write(str(predictions))