scaffold

app.py

@@ -1,7 +1,141 @@
 import gradio as gr
+import cv2
+import pytesseract
+import numpy as np
+import re
+import requests
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Align image using OpenCV
+def align_form_from_image(image):
+    img = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    blurred = cv2.GaussianBlur(gray, (5, 5), 0)
+    edged = cv2.Canny(blurred, 75, 200)
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+    contours, _ = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
+    contours = sorted(contours, key=cv2.contourArea, reverse=True)[:5]
+    doc_cnts = None
+    for c in contours:
+        peri = cv2.arcLength(c, True)
+        approx = cv2.approxPolyDP(c, 0.02 * peri, True)
+        if len(approx) == 4:
+            doc_cnts = approx
+            break
+
+    if doc_cnts is not None:
+        pts = doc_cnts.reshape(4, 2)
+        rect = order_points(pts)
+        dst = np.array([[0, 0], [800, 0], [800, 1000], [0, 1000]], dtype="float32")
+        M = cv2.getPerspectiveTransform(rect, dst)
+        aligned = cv2.warpPerspective(img, M, (800, 1000))
+    else:
+        aligned = img
+
+    rgb = cv2.cvtColor(aligned, cv2.COLOR_BGR2RGB)
+    text = pytesseract.image_to_string(rgb)
+    return rgb, text
+
+def order_points(pts):
+    rect = np.zeros((4, 2), dtype="float32")
+    s = pts.sum(axis=1)
+    rect[0] = pts[np.argmin(s)]
+    rect[2] = pts[np.argmax(s)]
+    diff = np.diff(pts, axis=1)
+    rect[1] = pts[np.argmin(diff)]
+    rect[3] = pts[np.argmax(diff)]
+    return rect
+
+# Extract fields from driver payout form
+def parse_driver_payout_custom(text: str):
+    def find_checkbox(label_yes, label_no):
+        yes = re.search(label_yes + r"\s*[:]?[\s\S]{0,20}?✓", text)
+        no = re.search(label_no + r"\s*[:]?[\s\S]{0,20}?✓", text)
+        if yes and not no:
+            return "Yes"
+        elif no and not yes:
+            return "No"
+        elif yes and no:
+            return "Both Checked"
+        return "Unchecked"
+
+    data = {
+        "date": re.search(r"Date[:\s]*([\d/]+)", text),
+        "time": re.search(r"Time[:\s]*([\d:]+)", text),
+        "name": re.search(r"Name[:\s]*([A-Za-z ]+)", text),
+        "email": re.search(r"Email[:\s]*(\S+@\S+)?", text),
+        "phone": re.search(r"Phone Number[:\s]*(\d{3}[- ]\d{3}[- ]\d{4})", text),
+        "service_type": None,
+        "w9_filled_out": find_checkbox("Yes", "No"),
+        "payment_received": re.search(r"\$\s?([\d.]+)", text),
+        "payout": "Payout Now" if "Payout Now" in text and "✓" in text.split("Payout Now")[1][:10] else (
+                    "Payout Later" if "Payout Later" in text and "✓" in text.split("Payout Later")[1][:10] else None),
+        "team_member": re.search(r"Team Member's Name[:\s]*([A-Za-z]+)", text),
+        "uploaded_to_drive": re.search(r"Uploaded to the Drive\?\s*(Yes|No)", text, re.IGNORECASE),
+    }
+
+    for service in ["Taxi", "Limo", "Uber", "Lyft", "Other"]:
+        if f"{service}" in text and "✓" in text.split(service)[1][:10]:
+            data["service_type"] = service
+
+    return {k: v.group(1).strip() if v else v for k, v in data.items()}
+
+# Send to webhook
+def send_to_webhook(webhook, *field_values):
+    data = {f"field_{i}": val for i, val in enumerate(field_values)}
+    try:
+        resp = requests.post(webhook, json=data)
+        return f"✅ Sent! Status: {resp.status_code}"
+    except Exception as e:
+        return f"❌ Failed: {str(e)}"
+
+# Launch Gradio app
+with gr.Blocks() as demo:
+    gr.Markdown("# 📄 Driver Payout Form OCR → Webhook")
+
+    webhook_url = gr.State("https://example.com/webhook")
+
+    with gr.Row():
+        image_input = gr.Image(type="pil", label="Upload or Take Photo", source="upload")
+        aligned_image = gr.Image(type="numpy", label="Aligned Image")
+
+    form_type = gr.Radio(["Driver Payout"], label="Form Type", value="Driver Payout")
+    raw_text_output = gr.Textbox(label="OCR Text", lines=8)
+    parsed_json = gr.JSON(label="Parsed Fields")
+
+    editable_fields_group = gr.Group(visible=False)
+    editable_fields = []
+    for i in range(12):  # max 12 fields
+        tb = gr.Textbox(label=f"Field {i+1}")
+        editable_fields.append(tb)
+    editable_fields_group.children = editable_fields
+
+    status_output = gr.Textbox(label="Webhook Response")
+
+    def process_image(image, form_type):
+        aligned, text = align_form_from_image(image)
+        parsed = parse_driver_payout_custom(text)
+        visible = gr.update(visible=True)
+        values = list(parsed.values()) + [""] * (12 - len(parsed))
+        return aligned, text, parsed, visible, values[:12]
+
+    process_btn = gr.Button("OCR + Parse")
+    process_btn.click(
+        fn=process_image,
+        inputs=[image_input, form_type],
+        outputs=[aligned_image, raw_text_output, parsed_json, editable_fields_group] + editable_fields
+    )
+
+    send_btn = gr.Button("Send to Webhook")
+    send_btn.click(
+        fn=send_to_webhook,
+        inputs=[webhook_url] + editable_fields,
+        outputs=status_output
+    )
+
+    with gr.Accordion("Admin Settings", open=False):
+        webhook_input = gr.Textbox(label="Set Webhook URL")
+        set_webhook_btn = gr.Button("Save Webhook")
+        set_webhook_btn.click(lambda url: url, inputs=webhook_input, outputs=webhook_url)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,6 @@
+gradio>=4.0.0
+opencv-python
+pytesseract
+numpy
+requests
+Pillow