Update app.py

app.py

@@ -4,28 +4,28 @@ from transformers import ViTFeatureExtractor, ViTModel
 from PIL import Image
 from transformers import AutoTokenizer, AutoModel
 import torch
+from pdf2image import convert_from_path
 
-
-
-# Define custom CSS styles
-button_style = gr.style(
-    padding="10px 20px",
-    background="#007BFF",
-    color="white",
-    border="none",
-    cursor="pointer",
-    font_size="16px",
-    margin="10px"
-)
+# CSS styles
+css = """
+.button { 
+    padding: 10px 20px; 
+    background: #007BFF; 
+    color: white; 
+    border: none; 
+    cursor: pointer; 
+    font-size: 16px; 
+    margin: 10px;
+}
+"""
 
 # Define layout with custom styles
 layout = [
     gr.Row([gr.File(label="Upload PDF", type="file")]),
-    gr.Row([gr.Button("Generate Insights", type="submit", style=button_style)]),
+    gr.Row([gr.Button("Generate Insights", type="submit")]),
     gr.Row([gr.Textbox("Placeholder for PDF insights", label="Insights", type="text")])
 ]
 
-
 # Function to get image embeddings using ViT
 def get_image_embeddings(image_path, model_name='google/vit-base-patch16-224'):
     feature_extractor = ViTFeatureExtractor.from_pretrained(model_name)
@@ -38,8 +38,6 @@ def get_image_embeddings(image_path, model_name='google/vit-base-patch16-224'):
     return embeddings
 
 # Function to convert PDF to images
-from pdf2image import convert_from_path
-
 def pdf_to_images(pdf_file, img_dir):
     images = convert_from_path(pdf_file)
     
@@ -52,7 +50,6 @@ def pdf_to_images(pdf_file, img_dir):
 
     print(f"Converted {len(images)} pages to images and saved in {img_dir}")
 
-
 # Function to get text embeddings using a transformer model
 def get_text_embeddings(text, model_name='bert-base-uncased'):
     tokenizer = AutoTokenizer.from_pretrained(model_name)
@@ -90,8 +87,9 @@ iface = gr.Interface(
     inputs=gr.File(label="Upload PDF", type="file"),
     outputs=gr.Textbox("Placeholder for PDF insights", label="Insights", type="text"),
     title="pdf-chatbot",
-    description="Upload a PDF and receive insights based on its content."
+    description="Upload a PDF and receive insights based on its content.",
+    css=css  # Add the CSS styles here
 )
 
 if __name__ == "__main__":
-    iface.launch()
+    iface.launch()