Upload app.py

app.py

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+import gradio as gr
+from gradio_client import Client, handle_file
+import re
+from thefuzz import fuzz
+
+# hugging face clients for both OCR options
+surya_ocr_client = Client("artificialguybr/Surya-OCR")
+got_ocr_client = Client("stepfun-ai/GOT_official_online_demo")
+
+# Global variable to store the extracted OCR text
+extracted_text = ""
+
+def ocr_extraction(image, ocr_model):
+    global extracted_text
+    if image is None:
+        return "Please upload an image first."
+    
+    try:
+        if ocr_model == "Surya OCR":
+            client = surya_ocr_client
+            result = client.predict(
+                image=handle_file(image),
+                langs="en",
+                api_name="/ocr_workflow"
+            )
+            text_matches = re.findall(r"text='(.*?)'", str(result))
+            extracted_text = "\n".join(text_matches)
+        elif ocr_model == "GOT OCR":
+            client = got_ocr_client
+            result = client.predict(
+                image=handle_file(image),
+                got_mode="plain texts OCR",
+				fine_grained_mode="box",
+                ocr_color="red",
+                ocr_box="Hello!!",
+                api_name="/run_GOT"
+            )
+            extracted_text = result[0]
+        else:
+            return "Invalid OCR model selected."
+        
+        return extracted_text
+    except Exception as e:
+        return f"An error occurred: {str(e)}"
+
+def search_keyword(keyword, search_type):
+    global extracted_text
+    if not extracted_text:
+        return "No OCR text found. Please extract text from an image first."
+    if not keyword:
+        return extracted_text
+    
+    if search_type == "Direct Search":
+        highlighted_text = re.sub(f"({re.escape(keyword)})", r'<span style="background-color: yellow;">\1</span>', extracted_text, flags=re.IGNORECASE)
+    else:  # Nearest Search
+        words = extracted_text.split()
+        highlighted_words = []
+        for word in words:
+            if fuzz.ratio(word.lower(), keyword.lower()) >= 80:  # Adjust threshold as needed
+                highlighted_words.append(f'<span style="background-color: yellow;">{word}</span>')
+            else:
+                highlighted_words.append(word)
+        highlighted_text = " ".join(highlighted_words)
+    
+    return highlighted_text
+
+with gr.Blocks(theme=gr.themes.Soft()) as gr_interface:
+    gr.Markdown("# 📷 OCR Text Extraction and Advanced Keyword Search 🔍")
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            image_input = gr.Image(type="filepath", label="Upload Image")
+            ocr_model_dropdown = gr.Dropdown(
+                choices=["Surya OCR", "GOT OCR"],
+                value="Surya OCR",
+                label="Select OCR Model"
+            )
+            ocr_button = gr.Button("Extract Text", variant="primary")
+        
+        with gr.Column(scale=2):
+            extracted_text_output = gr.Textbox(
+                label="Extracted Text",
+                placeholder="Text extracted from the image will appear here.",
+                lines=10
+            )
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            keyword_input = gr.Textbox(label="Enter keyword to search")
+            search_type = gr.Radio(["Direct Search", "Nearest Search"], label="Search Type", value="Direct Search")
+            search_button = gr.Button("Search Keyword", variant="secondary")
+        
+        with gr.Column(scale=2):
+            highlighted_output = gr.HTML(label="Highlighted Text")
+    
+    ocr_button.click(
+        fn=ocr_extraction,
+        inputs=[image_input, ocr_model_dropdown],
+        outputs=extracted_text_output
+    )
+    
+    search_button.click(
+        fn=search_keyword,
+        inputs=[keyword_input, search_type],
+        outputs=highlighted_output
+    )
+
+gr_interface.launch(share=True)