Update app.py

app.py

@@ -1,7 +1,94 @@
 import gradio as 
 
-def greet(name):
-    return "Hello " + name + "!!"
+import gradio as gr
+from transformers import AutoProcessor, AutoModelForCausalLM
+from pdf2image import convert_from_path
+import base64
+import io
+from PIL import Image
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+# Load the OCR model and processor from Hugging Face
+processor = AutoProcessor.from_pretrained("allenai/olmOCR-7B-0225-preview")
+model = AutoModelForCausalLM.from_pretrained("allenai/olmOCR-7B-0225-preview")
+
+def process_pdf(pdf_file):
+ """
+ Process the uploaded PDF file, extract text from each page, and generate HTML
+ to display each page's image and text with copy buttons.
+ """
+ # Check if a PDF file was uploaded
+ if pdf_file is None:
+ 
+    return "<p>Please upload a PDF file.</p>"
+ 
+ # Convert PDF to images
+ try:
+ 
+    pages = convert_from_path(pdf_file.name)
+    except Exception as e:
+    return f"<p>Error converting PDF to images: {str(e)}</p>"
+ 
+    # Start building the HTML output
+    html = '<div><button onclick="copyAll()" style="margin-bottom: 10px;">Copy All</button></div><div id="pages">'
+ 
+    # Process each page
+    for i, page in enumerate(pages):
+    # Convert the page image to base64 for embedding in HTML
+        buffered = io.BytesIO()
+        page.save(buffered, format="PNG")
+        img_str = base64.b64encode(buffered.getvalue()).decode()
+        img_data = f"data:image/png;base64,{img_str}"
+ 
+ # Extract text from the page using the OCR model
+ try:
+    inputs = processor(text="Extract the text from this image.", images=page, return_tensors="pt")
+    outputs = model.generate(**inputs)
+    text = processor.decode(outputs[0], skip_special_tokens=True)
+    except Exception as e:
+        text = f"Error extracting text: {str(e)}"
+ 
+ # Generate HTML for this page's section
+ textarea_id = f"text{i+1}"
+ html += f'''
+ <div class="page" style="margin-bottom: 20px; border-bottom: 1px solid #ccc; padding-bottom: 20px;">
+ <h3>Page {i+1}</h3>
+ <div style="display: flex; align-items: flex-start;">
+ <img src="{img_data}" alt="Page {i+1}" style="max-width: 300px; margin-right: 20px;">
+ <div style="flex-grow: 1;">
+ <textarea id="{textarea_id}" rows="10" style="width: 100%;">{text}</textarea>
+ <button onclick="copyText('{textarea_id}')" style="margin-top: 5px;">Copy</button>
+ </div>
+ </div>
+ </div>
+ '''
+ 
+ # Close the pages div and add JavaScript for copy functionality
+ html += '</div>'
+ html += '''
+ <script>
+ function copyText(id) {
+ var text = document.getElementById(id);
+ text.select();
+ document.execCommand("copy");
+ }
+ function copyAll() {
+ var texts = document.querySelectorAll("#pages textarea");
+ var allText = Array.from(texts).map(t => t.value).join("\\n\\n");
+ navigator.clipboard.writeText(allText);
+ }
+ </script>
+ '''
+ return html
+
+# Define the Gradio interface
+with gr.Blocks(title="PDF Text Extractor") as demo:
+ gr.Markdown("# PDF Text Extractor")
+ gr.Markdown("Upload a PDF file and click 'Extract Text' to see each page's image and extracted text.")
+ with gr.Row():
+ pdf_input = gr.File(label="Upload PDF", file_types=[".pdf"])
+ submit_btn = gr.Button("Extract Text")
+ output_html = gr.HTML()
+ submit_btn.click(fn=process_pdf, inputs=pdf_input, outputs=output_html)
+
+# Launch the interface
 demo.launch()