Update app.py

app.py

@@ -1,176 +1,177 @@
-import gradio as gr
-import cv2
-import numpy as np
-import pytesseract
-import re
-import google.generativeai as genai
-from rapidfuzz.distance import Levenshtein
-import os
-
-# Configure Generative AI
-OPENAI_API_KEY = os.getenv("API_KEY")
-genai.configure(api_key=OPENAI_API_KEY)
-model = genai.GenerativeModel("gemini-1.5-flash")
-
-# Image processing functions
-def threshold_image(img, threshold_value=None):
-    if threshold_value is None:  # Adaptive thresholding
-        thresholded_image = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
-                                                  cv2.THRESH_BINARY, 11, 2)
-    else:  # Manual thresholding
-        _, thresholded_image = cv2.threshold(img, threshold_value, 255, cv2.THRESH_BINARY)
-    return thresholded_image
-
-def bm3d_denoising(img, sigma_psd=55):
-    return cv2.fastNlMeansDenoising(img, None, sigma_psd)
-
-def remove_noise(img, kernel_size=3):
-    kernel = np.ones((kernel_size, kernel_size), np.float32) / (kernel_size**2)
-    denoised = cv2.filter2D(img, -1, kernel)
-    return cv2.medianBlur(denoised, 3)
-
-def sharpen_image(img):
-    kernel = np.array([[-1, -1, -1], [-1, 9, -1], [-1, -1, -1]])
-    return cv2.filter2D(img, -1, kernel)
-
-def remove_extra_spaces_and_lines(text):
-    text = re.sub(r'\s+', ' ', text).strip()
-    text = re.sub(r'\n\s*\n', '\n\n', text)
-    return text
-
-def calculate_accuracy(text1, text2):
-    # matcher = difflib.SequenceMatcher(None, generated_text, transcribed_text)
-    # return matcher.ratio()
-    distance = Levenshtein.distance(text1, text2)
-    max_length = max(len(text1), len(text2))
-    accuracy = (1 - (distance / max_length))
-    return accuracy
-
-# Gradio app
-def process_image(image, threshold_value=None, correct_transcription=None):
-    img = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-
-    # Process the image
-    thresholded = threshold_image(img, threshold_value)
-    bm3d_denoised_image = bm3d_denoising(thresholded)
-    denoised = remove_noise(thresholded)
-    sharpened_image = sharpen_image(bm3d_denoised_image)
-
-    # OCR
-    original_text = pytesseract.image_to_string(img)
-    thresholded_text = pytesseract.image_to_string(thresholded)
-    bm3d_denoised_text = pytesseract.image_to_string(bm3d_denoised_image)
-    denoised_text = pytesseract.image_to_string(denoised)
-    sharpened_text = pytesseract.image_to_string(sharpened_image)
-    
-    # Clean up text
-    original_text = remove_extra_spaces_and_lines(original_text)
-    thresholded_text = remove_extra_spaces_and_lines(thresholded_text)
-    bm3d_denoised_text = remove_extra_spaces_and_lines(bm3d_denoised_text)
-    denoised_text = remove_extra_spaces_and_lines(denoised_text)
-    sharpened_text = remove_extra_spaces_and_lines(sharpened_text)
-
-    # Generative AI model response
-    user_prompt = user_prompt = f"""
-    below are the output texts of OCR on multiple image processing techniques of a faded letter written in English, can you predict the original text, provide only the original text.
-    Pre-Processing Image Text:
-    {original_text}
-    Sharpened Image Text:
-    {sharpened_text}
-    Thresholded Image Text:
-    {thresholded_text}
-    BM3D Denoised Image Text:
-    {bm3d_denoised_text}
-    Denoised Image Text:
-    {denoised_text}
-    """  
-    response = model.generate_content(user_prompt)
-    model_text = response.text
-
-    if not correct_transcription:
-        correct_transcription = model_text
-    # Accuracy metrics
-    if correct_transcription:
-        original_accuracy = calculate_accuracy(original_text, correct_transcription)
-        thresholded_accuracy = calculate_accuracy(thresholded_text, correct_transcription)
-        bm3d_denoised_accuracy = calculate_accuracy(bm3d_denoised_text, correct_transcription)
-        denoised_accuracy = calculate_accuracy(denoised_text, correct_transcription)
-        sharpened_accuracy = calculate_accuracy(sharpened_text, correct_transcription)
-        model_accuracy = calculate_accuracy(model_text, correct_transcription)
-        accuracy_metrics = f"""
-        Original Image Accuracy: {original_accuracy:.2%}
-        Thresholded Image Accuracy: {thresholded_accuracy:.2%}
-        BM3D Denoised Image Accuracy: {bm3d_denoised_accuracy:.2%}
-        Denoised Image Accuracy: {denoised_accuracy:.2%}
-        Sharpened Image Accuracy: {sharpened_accuracy:.2%}
-        Model Response Accuracy: {model_accuracy:.2%}
-        """
-    else:
-        accuracy_metrics = "No correct transcription provided."
-
-    # Return results
-    return (
-        image, thresholded, bm3d_denoised_image, denoised, sharpened_image,
-        original_text, thresholded_text, bm3d_denoised_text, denoised_text, sharpened_text,
-        model_text, accuracy_metrics
-    )
-
-# Interface
-with gr.Blocks() as demo:
-    with gr.Row():
-        gr.Markdown("## Image Preprocessing and OCR App")
-    with gr.Row():
-        gr.Markdown("""
-        ### Legend
-        - **Model Response**: Text generated by the Generative AI model.
-        - **Accuracy Metrics**: Comparison of OCR results with the provided correct transcription if provided, otherwise with the model response.
-        """)
-    with gr.Row():
-        with gr.Column():
-            image_input = gr.Image(label="Upload Image", type="numpy")
-            threshold_slider = gr.Slider(label="Threshold Value", minimum=0, maximum=255, step=1, value=242)
-            adaptive_checkbox = gr.Checkbox(label="Use Adaptive Thresholding", value=False)
-            transcription_input = gr.Textbox(label="Correct Transcription (Optional)")
-            process_button = gr.Button("Process Image")
-
-        with gr.Column():
-            tabs = gr.Tabs()
-            with tabs:
-                with gr.TabItem("Original"):
-                    original_image_display = gr.Image(label="Original Image")
-                    original_text_display = gr.Textbox(label="Original Image Text")
-                with gr.TabItem("Thresholded"):
-                    thresholded_image_display = gr.Image(label="Thresholded Image")
-                    thresholded_text_display = gr.Textbox(label="Thresholded Image Text", lines=1)
-                with gr.TabItem("BM3D Denoised"):
-                    bm3d_denoised_image_display = gr.Image(label="BM3D Denoised Image")
-                    bm3d_denoised_text_display = gr.Textbox(label="BM3D Denoised Image Text")
-                with gr.TabItem("Denoised"):
-                    denoised_image_display = gr.Image(label="Denoised Image")
-                    denoised_text_display = gr.Textbox(label="Denoised Image Text")
-                with gr.TabItem("Sharpened"):
-                    sharpened_image_display = gr.Image(label="Sharpened Image")
-                    sharpened_text_display = gr.Textbox(label="Sharpened Image Text")
-            accuracy_output = gr.Textbox(label="Accuracy Metrics")
-            model_text_display = gr.Textbox(label="Model Response Text")
-
-    # Link button to processing function
-    def update_process(image, threshold_value, use_adaptive, correct_transcription):
-        threshold_value = None if use_adaptive else threshold_value
-        return process_image(image, threshold_value, correct_transcription)
-
-    process_button.click(
-        update_process,
-        inputs=[image_input, threshold_slider, adaptive_checkbox, transcription_input],
-        outputs=[
-            original_image_display, thresholded_image_display,
-            bm3d_denoised_image_display, denoised_image_display, 
-            sharpened_image_display, original_text_display,
-            thresholded_text_display, bm3d_denoised_text_display,
-            denoised_text_display, sharpened_text_display,
-            model_text_display, accuracy_output
-            ],
-    )
-
-# Launch app
+import gradio as gr
+import cv2
+import numpy as np
+import pytesseract
+import re
+import google.generativeai as genai
+from rapidfuzz.distance import Levenshtein
+import os
+
+os.system('apt-get update && apt-get install -y tesseract-ocr')
+# Configure Generative AI
+OPENAI_API_KEY = os.getenv("API_KEY")
+genai.configure(api_key=OPENAI_API_KEY)
+model = genai.GenerativeModel("gemini-1.5-flash")
+
+# Image processing functions
+def threshold_image(img, threshold_value=None):
+    if threshold_value is None:  # Adaptive thresholding
+        thresholded_image = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
+                                                  cv2.THRESH_BINARY, 11, 2)
+    else:  # Manual thresholding
+        _, thresholded_image = cv2.threshold(img, threshold_value, 255, cv2.THRESH_BINARY)
+    return thresholded_image
+
+def bm3d_denoising(img, sigma_psd=55):
+    return cv2.fastNlMeansDenoising(img, None, sigma_psd)
+
+def remove_noise(img, kernel_size=3):
+    kernel = np.ones((kernel_size, kernel_size), np.float32) / (kernel_size**2)
+    denoised = cv2.filter2D(img, -1, kernel)
+    return cv2.medianBlur(denoised, 3)
+
+def sharpen_image(img):
+    kernel = np.array([[-1, -1, -1], [-1, 9, -1], [-1, -1, -1]])
+    return cv2.filter2D(img, -1, kernel)
+
+def remove_extra_spaces_and_lines(text):
+    text = re.sub(r'\s+', ' ', text).strip()
+    text = re.sub(r'\n\s*\n', '\n\n', text)
+    return text
+
+def calculate_accuracy(text1, text2):
+    # matcher = difflib.SequenceMatcher(None, generated_text, transcribed_text)
+    # return matcher.ratio()
+    distance = Levenshtein.distance(text1, text2)
+    max_length = max(len(text1), len(text2))
+    accuracy = (1 - (distance / max_length))
+    return accuracy
+
+# Gradio app
+def process_image(image, threshold_value=None, correct_transcription=None):
+    img = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+    # Process the image
+    thresholded = threshold_image(img, threshold_value)
+    bm3d_denoised_image = bm3d_denoising(thresholded)
+    denoised = remove_noise(thresholded)
+    sharpened_image = sharpen_image(bm3d_denoised_image)
+
+    # OCR
+    original_text = pytesseract.image_to_string(img)
+    thresholded_text = pytesseract.image_to_string(thresholded)
+    bm3d_denoised_text = pytesseract.image_to_string(bm3d_denoised_image)
+    denoised_text = pytesseract.image_to_string(denoised)
+    sharpened_text = pytesseract.image_to_string(sharpened_image)
+    
+    # Clean up text
+    original_text = remove_extra_spaces_and_lines(original_text)
+    thresholded_text = remove_extra_spaces_and_lines(thresholded_text)
+    bm3d_denoised_text = remove_extra_spaces_and_lines(bm3d_denoised_text)
+    denoised_text = remove_extra_spaces_and_lines(denoised_text)
+    sharpened_text = remove_extra_spaces_and_lines(sharpened_text)
+
+    # Generative AI model response
+    user_prompt = user_prompt = f"""
+    below are the output texts of OCR on multiple image processing techniques of a faded letter written in English, can you predict the original text, provide only the original text.
+    Pre-Processing Image Text:
+    {original_text}
+    Sharpened Image Text:
+    {sharpened_text}
+    Thresholded Image Text:
+    {thresholded_text}
+    BM3D Denoised Image Text:
+    {bm3d_denoised_text}
+    Denoised Image Text:
+    {denoised_text}
+    """  
+    response = model.generate_content(user_prompt)
+    model_text = response.text
+
+    if not correct_transcription:
+        correct_transcription = model_text
+    # Accuracy metrics
+    if correct_transcription:
+        original_accuracy = calculate_accuracy(original_text, correct_transcription)
+        thresholded_accuracy = calculate_accuracy(thresholded_text, correct_transcription)
+        bm3d_denoised_accuracy = calculate_accuracy(bm3d_denoised_text, correct_transcription)
+        denoised_accuracy = calculate_accuracy(denoised_text, correct_transcription)
+        sharpened_accuracy = calculate_accuracy(sharpened_text, correct_transcription)
+        model_accuracy = calculate_accuracy(model_text, correct_transcription)
+        accuracy_metrics = f"""
+        Original Image Accuracy: {original_accuracy:.2%}
+        Thresholded Image Accuracy: {thresholded_accuracy:.2%}
+        BM3D Denoised Image Accuracy: {bm3d_denoised_accuracy:.2%}
+        Denoised Image Accuracy: {denoised_accuracy:.2%}
+        Sharpened Image Accuracy: {sharpened_accuracy:.2%}
+        Model Response Accuracy: {model_accuracy:.2%}
+        """
+    else:
+        accuracy_metrics = "No correct transcription provided."
+
+    # Return results
+    return (
+        image, thresholded, bm3d_denoised_image, denoised, sharpened_image,
+        original_text, thresholded_text, bm3d_denoised_text, denoised_text, sharpened_text,
+        model_text, accuracy_metrics
+    )
+
+# Interface
+with gr.Blocks() as demo:
+    with gr.Row():
+        gr.Markdown("## Image Preprocessing and OCR App")
+    with gr.Row():
+        gr.Markdown("""
+        ### Legend
+        - **Model Response**: Text generated by the Generative AI model.
+        - **Accuracy Metrics**: Comparison of OCR results with the provided correct transcription if provided, otherwise with the model response.
+        """)
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(label="Upload Image", type="numpy")
+            threshold_slider = gr.Slider(label="Threshold Value", minimum=0, maximum=255, step=1, value=242)
+            adaptive_checkbox = gr.Checkbox(label="Use Adaptive Thresholding", value=False)
+            transcription_input = gr.Textbox(label="Correct Transcription (Optional)")
+            process_button = gr.Button("Process Image")
+
+        with gr.Column():
+            tabs = gr.Tabs()
+            with tabs:
+                with gr.TabItem("Original"):
+                    original_image_display = gr.Image(label="Original Image")
+                    original_text_display = gr.Textbox(label="Original Image Text")
+                with gr.TabItem("Thresholded"):
+                    thresholded_image_display = gr.Image(label="Thresholded Image")
+                    thresholded_text_display = gr.Textbox(label="Thresholded Image Text", lines=1)
+                with gr.TabItem("BM3D Denoised"):
+                    bm3d_denoised_image_display = gr.Image(label="BM3D Denoised Image")
+                    bm3d_denoised_text_display = gr.Textbox(label="BM3D Denoised Image Text")
+                with gr.TabItem("Denoised"):
+                    denoised_image_display = gr.Image(label="Denoised Image")
+                    denoised_text_display = gr.Textbox(label="Denoised Image Text")
+                with gr.TabItem("Sharpened"):
+                    sharpened_image_display = gr.Image(label="Sharpened Image")
+                    sharpened_text_display = gr.Textbox(label="Sharpened Image Text")
+            accuracy_output = gr.Textbox(label="Accuracy Metrics")
+            model_text_display = gr.Textbox(label="Model Response Text")
+
+    # Link button to processing function
+    def update_process(image, threshold_value, use_adaptive, correct_transcription):
+        threshold_value = None if use_adaptive else threshold_value
+        return process_image(image, threshold_value, correct_transcription)
+
+    process_button.click(
+        update_process,
+        inputs=[image_input, threshold_slider, adaptive_checkbox, transcription_input],
+        outputs=[
+            original_image_display, thresholded_image_display,
+            bm3d_denoised_image_display, denoised_image_display, 
+            sharpened_image_display, original_text_display,
+            thresholded_text_display, bm3d_denoised_text_display,
+            denoised_text_display, sharpened_text_display,
+            model_text_display, accuracy_output
+            ],
+    )
+
+# Launch app
 demo.launch()