Update app.py

app.py

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+import gdown
+import cv2
+import numpy as np
+import gradio as gr
+import matplotlib.pyplot as plt
+from gradio_client import Client, handle_file
+
+# ✅ Dictionary of public Google Drive links for reference signatures
+drive_links = {
+    1: "https://drive.google.com/file/d/1xtzL6-TpN4EVyaFUF4MM4ssjAZqZutF8/view?usp=drive_link",
+    2: "https://drive.google.com/file/d/1UpPfOlDXoWwB5Ub530uhUrOVnUnWYvpQ/view?usp=drive_link",
+    3: "https://drive.google.com/file/d/1-M_PND4PK3tSLY705olnsswOk5bNoOFa/view?usp=drive_link",
+    4: "https://drive.google.com/file/d/1FL1uLEXlWW-nQYNoaBARiVs0N0XAwsvW/view?usp=drive_link",
+    5: "https://drive.google.com/file/d/1nZhl1CkvuH-KA4ErAslD-91W2QnBajhx/view?usp=drive_link",
+    6: "https://drive.google.com/file/d/1SHEgykTZN9lGdDaR6PTl9P01-Zlpu6cZ/view?usp=drive_link",
+    7: "https://drive.google.com/file/d/1gRE9SmvT7OBw8JYCyx7ehMs3lBpiX-Bp/view?usp=drive_link"
+}
+
+# ✅ Function to extract file ID from Google Drive link
+def extract_file_id(drive_url):
+    return drive_url.split("/d/")[1].split("/view")[0]
+
+# ✅ Function to download a file from Google Drive
+def download_from_drive(file_id, save_path):
+    gdown.download(f"https://drive.google.com/uc?id={file_id}", save_path, quiet=False)
+    return save_path
+
+# ✅ Function to extract the signature from a document image
+def extract_signature(document_image_path):
+    client = Client("tech4humans/signature-detection")
+    result = client.predict(
+        image=handle_file(document_image_path),
+        conf_thres=0.25,
+        iou_thres=0.5,
+        api_name="/process_image"
+    )
+
+    extracted_signature_info = result[0]
+    extracted_signature_path = (
+        extracted_signature_info.get("path") if isinstance(extracted_signature_info, dict)
+        else extracted_signature_info if isinstance(extracted_signature_info, str)
+        else None
+    )
+
+    if extracted_signature_path:
+        image = cv2.imread(extracted_signature_path)
+        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+        thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
+                                       cv2.THRESH_BINARY_INV, 11, 2)
+        contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+        valid_contours = []
+        for cnt in contours:
+            x, y, w, h = cv2.boundingRect(cnt)
+            area = w * h
+            aspect_ratio = w / float(h)
+            if 500 < area < 50000 and 0.2 < aspect_ratio < 5.0:
+                valid_contours.append((x, y, w, h))
+
+        if valid_contours:
+            x, y, w, h = max(valid_contours, key=lambda b: b[2] * b[3])
+            cropped_signature = image[y:y+h, x:x+w]
+            return cropped_signature
+    return None
+
+# ✅ ORB Feature Matching for Signature Comparison
+def orb_similarity(img1, img2, distance_threshold=50):
+    gray1, gray2 = [
+        cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) if len(img.shape) == 3 else img
+        for img in [img1, img2]
+    ]
+
+    orb = cv2.ORB_create()
+    kp1, des1 = orb.detectAndCompute(gray1, None)
+    kp2, des2 = orb.detectAndCompute(gray2, None)
+
+    if des1 is None or des2 is None:
+        return 0, None
+
+    bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
+    matches = sorted(bf.match(des1, des2), key=lambda x: x.distance)
+
+    good_matches = [m for m in matches if m.distance < distance_threshold]
+    similarity = len(good_matches) / len(matches) if matches else 0
+    return similarity, (kp1, kp2, good_matches, matches)
+
+# ✅ Function to process the uploaded document image and selected reference number
+def verify_signature(document_image, reference_number):
+    if reference_number not in drive_links:
+        return "Invalid reference number selected.", None
+
+    # Download reference signature
+    file_id = extract_file_id(drive_links[reference_number])
+    reference_image_path = f"reference_signature_{reference_number}.jpg"
+    download_from_drive(file_id, reference_image_path)
+
+    # Extract signature from the document
+    cropped_signature = extract_signature(document_image)
+    if cropped_signature is None:
+        return "Signature extraction failed.", None
+
+    # Load reference signature
+    reference_img = cv2.imread(reference_image_path)
+    if reference_img is None:
+        return "Error: Could not load the reference image.", None
+
+    # Compute similarity
+    similarity, details = orb_similarity(cropped_signature, reference_img)
+    similarity_percentage = round(similarity * 100, 2)
+
+    # Classification based on similarity score
+    if similarity_percentage > 55:
+        classification = "✅ Matched"
+    elif 40 <= similarity_percentage <= 55:
+        classification = "⚠️ Manual Check Recommended"
+    else:
+        classification = "❌ Not Matched"
+
+    # Generate visualization of matches
+    matched_img = None
+    if details is not None:
+        kp1, kp2, good_matches, _ = details
+        matched_img = cv2.drawMatches(cropped_signature, kp1, reference_img, kp2, good_matches, None, flags=2)
+
+    return f"🔍 Similarity Score: {similarity_percentage}%\n📌 {classification}", matched_img
+
+# ✅ Gradio Interface
+interface = gr.Interface(
+    fn=verify_signature,
+    inputs=[
+        gr.Image(type="filepath", label="Upload Document Image"),
+        gr.Number(label="Enter Reference Signature Number (1-7)", precision=0)
+    ],
+    outputs=[
+        gr.Textbox(label="Verification Result"),
+        gr.Image(label="Signature Matching Visualization")
+    ],
+    title="🖊️ Signature Verification System",
+    description="Upload a document with a signature, select a reference number (1-7), and verify its authenticity.",
+    theme="compact"
+)
+
+# ✅ Launch Gradio App
+interface.launch()