Update app.py

app.py

@@ -8,18 +8,6 @@ import gradio as gr
 import requests
 import json
 from dotenv import load_dotenv
-import spaces
-from transformers import AutoModel, AutoTokenizer
-from PIL import Image
-import numpy as np
-import os
-import base64
-import io
-import uuid
-import tempfile
-import time
-import shutil
-from pathlib import Path
 
 # Attempt to install pytesseract if not found
 try:
@@ -146,91 +134,55 @@ def dummy_analyze(ingredients_list, health_conditions=None):
     return report
 
 # Function to extract text from images using OCR
-tokenizer = AutoTokenizer.from_pretrained('ucaslcl/GOT-OCR2_0', trust_remote_code=True)
-model = AutoModel.from_pretrained('ucaslcl/GOT-OCR2_0', trust_remote_code=True, low_cpu_mem_usage=True, device_map='cuda', use_safetensors=True)
-model = model.eval().cuda()
-
-UPLOAD_FOLDER = "./uploads"
-RESULTS_FOLDER = "./results"
-
-for folder in [UPLOAD_FOLDER, RESULTS_FOLDER]:
-    if not os.path.exists(folder):
-        os.makedirs(folder)
-
-def image_to_base64(image):
-    buffered = io.BytesIO()
-    image.save(buffered, format="PNG")
-    return base64.b64encode(buffered.getvalue()).decode()
-
-@spaces.GPU
-def run_GOT(image, got_mode, fine_grained_mode="", ocr_color="", ocr_box=""):
-    unique_id = str(uuid.uuid4())
-    image_path = os.path.join(UPLOAD_FOLDER, f"{unique_id}.png")
-    result_path = os.path.join(RESULTS_FOLDER, f"{unique_id}.html")
-    
-    shutil.copy(image, image_path)
-    
+def extract_text_from_image(image):
     try:
-        if got_mode == "plain texts OCR":
-            res = model.chat(tokenizer, image_path, ocr_type='ocr')
-            return res, None
-        elif got_mode == "format texts OCR":
-            res = model.chat(tokenizer, image_path, ocr_type='format', render=True, save_render_file=result_path)
-        elif got_mode == "plain multi-crop OCR":
-            res = model.chat_crop(tokenizer, image_path, ocr_type='ocr')
-            return res, None
-        elif got_mode == "format multi-crop OCR":
-            res = model.chat_crop(tokenizer, image_path, ocr_type='format', render=True, save_render_file=result_path)
-        elif got_mode == "plain fine-grained OCR":
-            res = model.chat(tokenizer, image_path, ocr_type='ocr', ocr_box=ocr_box, ocr_color=ocr_color)
-            return res, None
-        elif got_mode == "format fine-grained OCR":
-            res = model.chat(tokenizer, image_path, ocr_type='format', ocr_box=ocr_box, ocr_color=ocr_color, render=True, save_render_file=result_path)
-
-        # res_markdown = f"$$ {res} $$"
-        res_markdown = res
-
-        if "format" in got_mode and os.path.exists(result_path):
-            with open(result_path, 'r') as f:
-                html_content = f.read()
-            encoded_html = base64.b64encode(html_content.encode('utf-8')).decode('utf-8')
-            iframe_src = f"data:text/html;base64,{encoded_html}"
-            iframe = f'<iframe src="{iframe_src}" width="100%" height="600px"></iframe>'
-            download_link = f'<a href="data:text/html;base64,{encoded_html}" download="result_{unique_id}.html">Download Full Result</a>'
-            return res_markdown, f"{download_link}<br>{iframe}"
-        else:
-            return res_markdown, None
+        if image is None:
+            return "No image captured. Please try again."
+
+        # Verify Tesseract executable is accessible
+        try:
+            subprocess.run([pytesseract.pytesseract.tesseract_cmd, "--version"],
+                          check=True, capture_output=True, text=True)
+        except (subprocess.SubprocessError, FileNotFoundError):
+            return "Tesseract OCR is not installed or not properly configured. Please check installation."
+
+        # Image preprocessing for better OCR
+        import cv2
+        import numpy as np
+
+        # Convert PIL image to OpenCV format
+        img_cv = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
+
+        # Convert to grayscale
+        gray = cv2.cvtColor(img_cv, cv2.COLOR_BGR2GRAY)
+
+        # Apply thresholding to get black and white image
+        _, binary = cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
+
+        # Noise removal
+        kernel = np.ones((1, 1), np.uint8)
+        binary = cv2.morphologyEx(binary, cv2.MORPH_OPEN, kernel)
+
+        # Dilate to connect text
+        binary = cv2.dilate(binary, kernel, iterations=1)
+
+        # Convert back to PIL image for tesseract
+        binary_pil = Image.fromarray(cv2.bitwise_not(binary))
+
+        # Run OCR with improved configuration
+        custom_config = r'--oem 3 --psm 6 -l eng'
+        text = pytesseract.image_to_string(binary_pil, config=custom_config)
+
+        if not text.strip():
+            # Try original image as fallback
+            text = pytesseract.image_to_string(image, config=custom_config)
+
+        if not text.strip():
+            return "No text could be extracted. Ensure image is clear and readable."
+
+        return text
     except Exception as e:
-        return f"Error: {str(e)}", None
-    finally:
-        if os.path.exists(image_path):
-            os.remove(image_path)
-
-def task_update(task):
-    if "fine-grained" in task:
-        return [
-            gr.update(visible=True),
-            gr.update(visible=False),
-            gr.update(visible=False),
-        ]
-    else:
-        return [
-            gr.update(visible=False),
-            gr.update(visible=False),
-            gr.update(visible=False),
-        ]
-
-def fine_grained_update(task):
-    if task == "box":
-        return [
-            gr.update(visible=False, value = ""),
-            gr.update(visible=True),
-        ]
-    elif task == 'color':
-        return [
-            gr.update(visible=True),
-            gr.update(visible=False, value = ""),
-        ]
+        return f"Error extracting text: {str(e)}"
 
 # Function to parse ingredients from text
 def parse_ingredients(text):