app.py

import gradio as gr
import pytesseract
import cv2
import pandas as pd
import re
from PIL import Image
import numpy as np

def extract_fields(image):
    try:
        # -------------------- Image Preparation --------------------
        img = np.array(image.convert("RGB"))[:, :, ::-1]  # PIL to BGR (OpenCV)
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        bw = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
                                   cv2.THRESH_BINARY_INV, 25, 15)
        inverted = cv2.bitwise_not(bw)
        pil_img = Image.fromarray(inverted)

        # -------------------- OCR Pass 1: Name by "Title" --------------------
        ocr_df2 = pytesseract.image_to_data(image, output_type=pytesseract.Output.DATAFRAME)
        ocr_df2 = ocr_df2.dropna(subset=["text"])
        ocr_df2 = ocr_df2[ocr_df2["text"].str.strip() != ""]

        name = "Not found"
        neighbors = []
        
        # ✅ Fix - Add these lines BEFORE you use `ocr_df`
        ocr_df = pytesseract.image_to_data(pil_img, output_type=pytesseract.Output.DATAFRAME)
        ocr_df = ocr_df.dropna(subset=["text"])
        ocr_df = ocr_df[ocr_df["text"].str.strip() != ""]
        title_matches = ocr_df[ocr_df['text'].str.lower().str.contains("tit", na=False)]

        if not title_matches.empty:
            title_info = title_matches.iloc[0]

            if 'line_num' in title_info and 'block_num' in title_info:
                line_num = title_info['line_num']
                block_num = title_info['block_num']

                same_line = ocr_df[
                    (ocr_df['line_num'] == line_num) &
                    (ocr_df['block_num'] == block_num)
                ].copy().sort_values(by='left').reset_index(drop=True)

                tit_indices = same_line[same_line['text'].str.lower().str.contains("tit")].index
                if not tit_indices.empty:
                    idx = tit_indices[0]
                    if idx + 1 < len(same_line):
                        neighbors.append(same_line.iloc[idx + 1]['text'])
                    if idx + 2 < len(same_line):
                        neighbors.append(same_line.iloc[idx + 2]['text'])

        def clean_name(words):
            cleaned = []
            for w in words:
                w_clean = re.sub(r'^[^a-zA-Z]+|[^a-zA-Z]+$', '', w)
                if w_clean:
                    cleaned.append(w_clean)
            return ' '.join(cleaned)

        if neighbors:
            name = clean_name(neighbors)

        # -------------------- OCR Pass 2: For Other Fields --------------------
        ocr_df2 = pytesseract.image_to_data(image, output_type=pytesseract.Output.DATAFRAME)
        ocr_df2 = ocr_df2.dropna(subset=["text"])
        ocr_df2 = ocr_df2[ocr_df2["text"].str.strip() != ""]

        def get_value_next_to(keyword, direction="right", max_dist=200):
            match = ocr_df2[ocr_df2['text'].str.lower() == keyword.lower()]
            if match.empty:
                return None
            row = match.iloc[0]
            if 'line_num' not in row or 'left' not in row:
                return None
            line = row['line_num']
            x = row['left']
            if direction == "right":
                candidates = ocr_df2[
                    (ocr_df2['line_num'] == line) &
                    (ocr_df2['left'] > x) &
                    (ocr_df2['left'] < x + max_dist)
                ].sort_values('left')
                return candidates['text'].tolist()[0] if not candidates.empty else None
            return None

        text = " ".join(ocr_df2['text'])
        email_match = re.search(r'[\w\.-]+@[\w\.-]+', text)
        phone_match = re.search(r'\+\d{2}\s?\d{2,3}\s?\d{3}\s?\d{2}\s?\d{2}', text)

        raw_text = pytesseract.image_to_string(image, config='--psm 6')

        dob_match = re.search(r'\d{2}\.\d{2}\.\d{4}', raw_text)
        dob = dob_match.group(0) if dob_match else "Not found"

        postcode = None
        postcode_after_ch = None
        ch_exists = bool(re.search(r'\bCH\b', raw_text))

        lines = raw_text.splitlines()
        for line in lines:
            if re.search(r'\bCH\b', line):
                match = re.search(r'\bCH\b.*?(\d{4})(?![\d/])', line)
                if match:
                    postcode_after_ch = match.group(1)
                    break

        if postcode_after_ch:
            postcode = postcode_after_ch
        else:
            matches = re.findall(r'(?<!\d|\w)[0-9]{4}(?!\d|\w)', raw_text)
            if matches:
                postcode = matches[0]

        if not postcode:
            postcode = "Not found"

        # -------------------- Function List Extraction --------------------
        def extract_functions_block():
            #
            func_match = ocr_df2[ocr_df2['text'].str.lower().str.contains("function")]
            if func_match.empty:
                return []
    
            base_y = func_match.iloc[0]['top']
    
            func_words = ocr_df2[ 
            (ocr_df2['top'] > base_y + 10) & (ocr_df2['top'] < base_y + 120)
            ]
    
            # Sort by line_num and left to maintain correct reading order
            func_words = func_words.sort_values(by=["line_num", "left"])

            grouped_lines = func_words.groupby('line_num')['text'].apply(lambda x: ' '.join(x)).tolist()
    
            clean_funcs = []
            for line in grouped_lines:
                #
                cleaned = re.sub(r'[^a-zA-Z0-9\s]', '', line).strip()
                if len(cleaned) > 1:
                    clean_funcs.append(cleaned)
            return clean_funcs

        functions = extract_functions_block()
        # -------------------- Final Output --------------------
        return [
            name if name else "Not found",
            email_match.group(0) if email_match else "Not found",
            phone_match.group(0) if phone_match else "Not found",
            dob,
            postcode,
            get_value_next_to("CurBase") or "Not found",
            get_value_next_to("hourly") or get_value_next_to("rate") or "Not found",
            "\n".join(functions) if functions else "Not found"
        ]

    except Exception as e:
        return [f"Error: {str(e)}"] + ["Not found"] * 8


# -------------------- Gradio Interface --------------------

with gr.Blocks() as demo:
    gr.Markdown("## 📄 Image OCR Field Extractor")
    gr.Markdown("Upload a document image to extract structured data fields.")

    with gr.Row():
        with gr.Column():
            image_input = gr.Image(type="pil", label=" Upload Your Document")
            submit_btn = gr.Button(" Run Extraction")

            gr.Examples(
                examples=["example_doc.jpeg"],
                inputs=[image_input],
                label=" Example Image (Click to load into uploader)"
            )

        with gr.Column():
            name = gr.Text(label="Name")
            email = gr.Text(label="Email")
            phone = gr.Text(label="Phone")
            dob = gr.Text(label="DOB")
            postcode = gr.Text(label="Postcode")
            prem = gr.Text(label="Prem (CurBase)")
            rate = gr.Text(label="Temp (Hourly Rate)")
            functions = gr.Textbox(label="Functions", lines=4)

    submit_btn.click(fn=extract_fields, inputs=image_input,
                     outputs=[name, email, phone, dob, postcode, prem, rate, functions])

if __name__ == "__main__":
    demo.launch()