app.py

import os

import cv2
import gradio as gr
import mediapipe as mp
import numpy as np
import tensorflow as tf
import tensorflow.lite as tflite

index_to_class = {
    "TV": 0, "after": 1, "airplane": 2, "all": 3, "alligator": 4, "animal": 5, "another": 6, "any": 7, "apple": 8,
    "arm": 9, "aunt": 10, "awake": 11, "backyard": 12, "bad": 13, "balloon": 14, "bath": 15, "because": 16, "bed": 17,
    "bedroom": 18, "bee": 19, "before": 20, "beside": 21, "better": 22, "bird": 23, "black": 24, "blow": 25, "blue": 26,
    "boat": 27, "book": 28, "boy": 29, "brother": 30, "brown": 31, "bug": 32, "bye": 33, "callonphone": 34, "can": 35,
    "car": 36, "carrot": 37, "cat": 38, "cereal": 39, "chair": 40, "cheek": 41, "child": 42, "chin": 43,
    "chocolate": 44, "clean": 45, "close": 46, "closet": 47, "cloud": 48, "clown": 49, "cow": 50, "cowboy": 51,
    "cry": 52, "cut": 53, "cute": 54, "dad": 55, "dance": 56, "dirty": 57, "dog": 58, "doll": 59, "donkey": 60,
    "down": 61, "drawer": 62, "drink": 63, "drop": 64, "dry": 65, "dryer": 66, "duck": 67, "ear": 68, "elephant": 69,
    "empty": 70, "every": 71, "eye": 72, "face": 73, "fall": 74, "farm": 75, "fast": 76, "feet": 77, "find": 78,
    "fine": 79, "finger": 80, "finish": 81, "fireman": 82, "first": 83, "fish": 84, "flag": 85, "flower": 86,
    "food": 87, "for": 88, "frenchfries": 89, "frog": 90, "garbage": 91, "gift": 92, "giraffe": 93, "girl": 94,
    "give": 95, "glasswindow": 96, "go": 97, "goose": 98, "grandma": 99, "grandpa": 100, "grass": 101, "green": 102,
    "gum": 103, "hair": 104, "happy": 105, "hat": 106, "hate": 107, "have": 108, "haveto": 109, "head": 110,
    "hear": 111, "helicopter": 112, "hello": 113, "hen": 114, "hesheit": 115, "hide": 116, "high": 117, "home": 118,
    "horse": 119, "hot": 120, "hungry": 121, "icecream": 122, "if": 123, "into": 124, "jacket": 125, "jeans": 126,
    "jump": 127, "kiss": 128, "kitty": 129, "lamp": 130, "later": 131, "like": 132, "lion": 133, "lips": 134,
    "listen": 135, "look": 136, "loud": 137, "mad": 138, "make": 139, "man": 140, "many": 141, "milk": 142,
    "minemy": 143, "mitten": 144, "mom": 145, "moon": 146, "morning": 147, "mouse": 148, "mouth": 149, "nap": 150,
    "napkin": 151, "night": 152, "no": 153, "noisy": 154, "nose": 155, "not": 156, "now": 157, "nuts": 158, "old": 159,
    "on": 160, "open": 161, "orange": 162, "outside": 163, "owie": 164, "owl": 165, "pajamas": 166, "pen": 167,
    "pencil": 168, "penny": 169, "person": 170, "pig": 171, "pizza": 172, "please": 173, "police": 174, "pool": 175,
    "potty": 176, "pretend": 177, "pretty": 178, "puppy": 179, "puzzle": 180, "quiet": 181, "radio": 182, "rain": 183,
    "read": 184, "red": 185, "refrigerator": 186, "ride": 187, "room": 188, "sad": 189, "same": 190, "say": 191,
    "scissors": 192, "see": 193, "shhh": 194, "shirt": 195, "shoe": 196, "shower": 197, "sick": 198, "sleep": 199,
    "sleepy": 200, "smile": 201, "snack": 202, "snow": 203, "stairs": 204, "stay": 205, "sticky": 206, "store": 207,
    "story": 208, "stuck": 209, "sun": 210, "table": 211, "talk": 212, "taste": 213, "thankyou": 214, "that": 215,
    "there": 216, "think": 217, "thirsty": 218, "tiger": 219, "time": 220, "tomorrow": 221, "tongue": 222, "tooth": 223,
    "toothbrush": 224, "touch": 225, "toy": 226, "tree": 227, "uncle": 228, "underwear": 229, "up": 230, "vacuum": 231,
    "wait": 232, "wake": 233, "water": 234, "wet": 235, "weus": 236, "where": 237, "white": 238, "who": 239, "why": 240,
    "will": 241, "wolf": 242, "yellow": 243, "yes": 244, "yesterday": 245, "yourself": 246, "yucky": 247, "zebra": 248,
    "zipper": 249
}

inv_index_to_class = {v: k for k, v in index_to_class.items()}

# Initialize MediaPipe solutions
mp_hands = mp.solutions.hands
mp_pose = mp.solutions.pose
mp_face_mesh = mp.solutions.face_mesh

hands = mp_hands.Hands()
pose = mp_pose.Pose()
face_mesh = mp_face_mesh.FaceMesh()

# Get the absolute path to the directory containing app.py
current_dir = os.path.dirname(os.path.abspath(__file__))
# Define the filename of the TFLite model
model_filename = "model.tflite"
# Construct the full path to the TFLite model file
model_path = os.path.join(current_dir, model_filename)
# Load the TFLite model using the interpreter
interpreter = tf.lite.Interpreter(model_path=model_path)
interpreter.allocate_tensors()


# Preprocess landmarks
def preprocess_landmarks(hand1_landmarks, hand2_landmarks, pose_landmarks, lip_landmarks):
    hand1_landmarks = [[landmark.x, landmark.y, landmark.z] for landmark in hand1_landmarks.landmark]
    hand2_landmarks = [[landmark.x, landmark.y, landmark.z] for landmark in hand2_landmarks.landmark]
    pose_landmarks = [[landmark.x, landmark.y, landmark.z] for landmark in pose_landmarks.landmark]
    lip_landmarks = [[landmark.x, landmark.y, landmark.z] for landmark in lip_landmarks]

    combined_landmarks = lip_landmarks + hand1_landmarks + hand2_landmarks + pose_landmarks

    return np.array(combined_landmarks, dtype=np.float32)


# Function to extract landmarks from the webcam frame
def extract_landmarks(frame):
    frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
    results = hands.process(frame_rgb)
    pose_results = pose.process(frame_rgb)
    face_results = face_mesh.process(frame_rgb)

    if not results.multi_hand_landmarks or not pose_results.pose_landmarks or not face_results.multi_face_landmarks:
        return None

    hand1_landmarks = results.multi_hand_landmarks[0]
    if len(results.multi_hand_landmarks) > 1:
        hand2_landmarks = results.multi_hand_landmarks[1]
    else:
        hand2_landmarks = hand1_landmarks

    pose_landmarks = pose_results.pose_landmarks
    face_landmarks = face_results.multi_face_landmarks[0]
    lip_landmarks = [face_landmarks.landmark[i] for i in LIPS_IDXS0 - START_IDX]

    print(f"Number of landmarks for hand1: {len(hand1_landmarks.landmark)}")
    print(f"Number of landmarks for hand2: {len(hand2_landmarks.landmark)}")
    print(f"Number of landmarks for pose: {len(pose_landmarks.landmark)}")
    print(f"Number of landmarks for lip: {len(lip_landmarks)}")

    return hand1_landmarks, hand2_landmarks, pose_landmarks, lip_landmarks


# Make prediction
def make_prediction(processed_landmarks):
    inputs = np.array(processed_landmarks, dtype=np.float32)

    # Set the input tensor for the TFLite model
    interpreter.set_tensor(input_details[0]['index'], inputs)

    # Invoke the TFLite interpreter to perform inference
    interpreter.invoke()

    # Get the output tensor of the TFLite model
    output_data = interpreter.get_tensor(output_details[0]['index'])

    # Find the index of the predicted class
    index = np.argmax(output_data)

    # Map the index to the corresponding class label using the index_to_class dictionary
    prediction = inv_index_to_class[index]

    return prediction


# Gradio Interface Function
def predict_with_webcam(frame):
    landmarks = extract_landmarks(frame)
    if landmarks is not None:
        processed_landmarks = preprocess_landmarks(*landmarks)
        prediction = make_prediction(processed_landmarks)
        return str(prediction)


# Define the Gradio interface with the Webcam input and Text output
# Modify the Gradio interface to use a "label" type output instead of "textbox"
webcam_interface = gr.Interface(
    fn=predict_with_webcam,
    inputs=gr.inputs.Image(shape=(480, 640), source="webcam"),
    outputs=gr.outputs.Label(),  # Use "Label" type instead of "Textbox"
    live=True,
    interpretation="default",
    title="Webcam Landmark Prediction",
    description="Make predictions using landmarks extracted from your webcam stream.",
)




# Launch the Gradio app with the webcam interface
if __name__ == "__main__":
    webcam_interface.launch()