app.py

import gradio as gr
import cv2
import numpy as np
import mediapipe as mp
from sklearn.ensemble import IsolationForest

# -----------------------
# FACE RECOGNITION MODEL
# -----------------------
mp_face = mp.solutions.face_mesh
mp_drawing = mp.solutions.drawing_utils

# SIMPLE FACE EMBEDDING (using face landmarks)
def get_face_embedding(image):
    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    with mp_face.FaceMesh(static_image_mode=True) as face_mesh:
        result = face_mesh.process(image_rgb)

    if not result.multi_face_landmarks:
        return None

    landmarks = result.multi_face_landmarks[0]
    points = []
    for lm in landmarks.landmark:
        points.append([lm.x, lm.y, lm.z])
    return np.array(points).flatten()  # simple embedding


# -----------------------
# LIVENESS CHECK (Blink)
# -----------------------
def liveness_check(image1, image2):
    emb1 = get_face_embedding(image1)
    emb2 = get_face_embedding(image2)

    if emb1 is None or emb2 is None:
        return False, "Face not detected"

    diff = np.linalg.norm(emb1 - emb2)

    if diff > 0.05:  
        return True, "Liveness Passed (Blink Detected)"
    else:
        return False, "No blink detected, image may be fake"


# -----------------------
# ANOMALY DETECTION
# -----------------------
model = IsolationForest(contamination=0.05)
model.fit([[1], [2], [3], [4], [5], [6]])  # dummy training

def check_anomaly(vote_count):
    pred = model.predict([[vote_count]])
    if pred[0] == -1:
        return "ALERT: Suspicious voting pattern detected!"
    return "Normal voting behavior"


# -----------------------
# MAIN PIPELINE FUNCTION
# -----------------------
def process(face_img, blink_img, vote_number):

    if face_img is None or blink_img is None:
        return "Upload 2 images!"

    # Step 1 — Extract embedding
    emb = get_face_embedding(face_img)
    if emb is None:
        return "Face not detected!"

    # Step 2 — Liveness Detection
    live, msg = liveness_check(face_img, blink_img)

    # Step 3 — Anomaly Detection
    anomaly_msg = check_anomaly(vote_number)

    return f"""
🧑 Face Verified: YES  
👁️ Liveness Result: {msg}  
📊 Anomaly Check: {anomaly_msg}
"""


# -----------------------
# GRADIO UI
# -----------------------
ui = gr.Interface(
    fn=process,
    inputs=[
        gr.Image(type="numpy", label="Upload Face Image"),
        gr.Image(type="numpy", label="Upload Blink Image"),
        gr.Number(label="Enter Vote Count")
    ],
    outputs=gr.Textbox(label="Result"),
    title="AI Module for Blockchain Voting System"
)

ui.launch()