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app.py

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+# -*- coding: utf-8 -*-
+"""Final WebApp using Gradio.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1a5-p_KZd9Hk0tsKZ_JoqoYeRD3XOQtRK
+
+# **Task 2 - Web App Development with Gradio**
+
+## **Gradio Interface for OCR Application**
+
+In this notebook, I created an interactive web application using Gradio to facilitate the OCR process and allow users to perform keyword searches on the extracted text.
+"""
+
+!pip install gradio
+!pip install -q tiktoken verovio
+!pip install pytesseract
+
+"""**Library Imports**:
+   
+   - In addition to libraries from the first notebook, I imported `gradio` to build the user interface for the application.
+"""
+
+import cv2
+from pytesseract import pytesseract
+from transformers import AutoModel, AutoTokenizer
+import gradio as gr
+
+"""**Model and Tesseract Configuration**:
+   - Similar to the first notebook, I loaded the GOT2 model for English text and configured Tesseract for Hindi text.
+"""
+
+tokenizer_eng = AutoTokenizer.from_pretrained('ucaslcl/GOT-OCR2_0', trust_remote_code=True)
+model_eng = AutoModel.from_pretrained('ucaslcl/GOT-OCR2_0', trust_remote_code=True).eval().cuda()
+
+pytesseract.tesseract_cmd = '/usr/bin/tesseract'
+tesseract_config = '--oem 3 --psm 6 -l hin'
+
+"""**Perform OCR Function**:
+   - The `perform_ocr` function was adapted to handle image input from the Gradio interface. This function processes the uploaded image based on the selected language and returns the extracted English and Hindi texts.
+"""
+
+def perform_ocr(img, language):
+
+    img_path = "/tmp/uploaded_image.png"
+    img.save(img_path)
+
+    res_eng = ""
+    res_hin = ""
+
+    if language in ["English", "Both"]:
+        res_eng = model_eng.chat(tokenizer_eng, img_path, ocr_type='ocr')
+
+    if language in ["Hindi", "Both"]:
+        img_cv = cv2.imread(img_path)
+        res_hin = pytesseract.image_to_string(img_cv, config=tesseract_config)
+
+    return res_eng, res_hin
+
+"""**Keyword Search Functionality**:
+   - A new function, `ocr_and_search`, was implemented to allow users to search for keywords within the extracted text. It checks for keyword matches in both English and Hindi texts, providing appropriate feedback.
+"""
+
+def ocr_and_search(image, language, keyword):
+
+    english_text, hindi_text = perform_ocr(image, language)
+
+    extracted_english = f"Extracted English Text:\n{english_text}" if english_text else "No English text extracted."
+    extracted_hindi = f"Extracted Hindi Text:\n{hindi_text}" if hindi_text else "No Hindi text extracted."
+
+    # Search for the keyword in the extracted text
+    search_results = []
+    if keyword:
+
+        if language in ["English", "Both"] and keyword.lower() in english_text.lower():
+            search_results.append(f"Keyword '{keyword}' found in English text.")
+
+        if language in ["Hindi", "Both"] and keyword.lower() in hindi_text.lower():
+            search_results.append(f"Keyword '{keyword}' found in Hindi text.")
+
+    search_output = "\n".join(search_results) if search_results else "No matches found."
+
+    return extracted_english, extracted_hindi, search_output
+
+"""**Gradio Interface Setup**:
+   - The user interface is constructed using Gradio's Blocks API, allowing users to upload images, select the desired language for OCR, and enter a keyword for search.
+   - The outputs are displayed in separate text boxes for extracted English text, extracted Hindi text, and search results.
+"""
+
+# Gradio
+with gr.Blocks() as app:
+    gr.Markdown("### OCR Application")
+    image_input = gr.Image(type="pil", label="Upload Image")
+    language_selection = gr.Radio(choices=["English", "Hindi", "Both"], label="Select Language")
+    keyword_input = gr.Textbox(placeholder="Enter keyword to search", label="Keyword Search")
+    output_english = gr.Textbox(label="Extracted English Text", interactive=False)
+    output_hindi = gr.Textbox(label="Extracted Hindi Text", interactive=False)
+    output_search = gr.Textbox(label="Search Results", interactive=False)
+
+    submit_button = gr.Button("Submit")
+    submit_button.click(fn=ocr_and_search, inputs=[image_input, language_selection, keyword_input], outputs=[output_english, output_hindi, output_search])
+
+"""**Application Launch**:
+   - Finally, the Gradio app is launched, making the OCR application accessible for user interaction. This enables real-time testing and usability of the OCR functionalities implemented in the previous notebook.
+"""
+
+app.launch()

requirements.txt

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+gradio
+opencv-python
+pytesseract
+transformers
+langdetect