Create app.py

app.py

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+from pdf2image import convert_from_path
+import pandas as pd, numpy as np
+from PIL import Image
+import pickle
+import gradio as gr
+from transformers import pipeline
+import pandas as pd
+from sentence_transformers import SentenceTransformer, util
+import pickle
+
+model = SentenceTransformer('clip-ViT-B-32')
+with open('preesmefiletype.h5', 'rb') as file:
+    pipe=pickle.load(file)
+    
+def findpdftype(file):
+    images = convert_from_path(file.name)
+    encodings=model.encode(images)
+    predictions=pipe.predict(encodings)
+    return pd.DataFrame(predictions, columns=['label']).label.value_counts().sort_values(ascending=False).index[0]
+
+gr.Interface(fn=findpdftype, inputs="file", outputs="label").launch()