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import streamlit as st |
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from streamlit_webrtc import webrtc_streamer |
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import av |
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import cv2 |
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import numpy as np |
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import mediapipe as mp |
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from keras.models import load_model |
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import webbrowser |
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model = load_model("model.h5") |
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label = np.load("labels.npy") |
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holistic = mp.solutions.holistic |
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hands = mp.solutions.hands |
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holis = holistic.Holistic() |
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drawing = mp.solutions.drawing_utils |
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st.header("Emotion Based Music Recommender") |
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if "run" not in st.session_state: |
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st.session_state["run"] = "true" |
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try: |
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emotion = np.load("emotion.npy")[0] |
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except: |
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emotion="" |
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if not(emotion): |
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st.session_state["run"] = "true" |
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else: |
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st.session_state["run"] = "false" |
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class EmotionProcessor: |
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def recv(self, frame): |
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frm = frame.to_ndarray(format="bgr24") |
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frm = cv2.flip(frm, 1) |
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res = holis.process(cv2.cvtColor(frm, cv2.COLOR_BGR2RGB)) |
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lst = [] |
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if res.face_landmarks: |
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for i in res.face_landmarks.landmark: |
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lst.append(i.x - res.face_landmarks.landmark[1].x) |
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lst.append(i.y - res.face_landmarks.landmark[1].y) |
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if res.left_hand_landmarks: |
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for i in res.left_hand_landmarks.landmark: |
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lst.append(i.x - res.left_hand_landmarks.landmark[8].x) |
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lst.append(i.y - res.left_hand_landmarks.landmark[8].y) |
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else: |
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for i in range(42): |
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lst.append(0.0) |
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if res.right_hand_landmarks: |
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for i in res.right_hand_landmarks.landmark: |
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lst.append(i.x - res.right_hand_landmarks.landmark[8].x) |
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lst.append(i.y - res.right_hand_landmarks.landmark[8].y) |
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else: |
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for i in range(42): |
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lst.append(0.0) |
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lst = np.array(lst).reshape(1,-1) |
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pred = label[np.argmax(model.predict(lst))] |
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print(pred) |
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cv2.putText(frm, pred, (50,50),cv2.FONT_ITALIC, 1, (255,0,0),2) |
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np.save("emotion.npy", np.array([pred])) |
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drawing.draw_landmarks(frm, res.face_landmarks, holistic.FACEMESH_TESSELATION, |
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landmark_drawing_spec=drawing.DrawingSpec(color=(0,0,255), thickness=-1, circle_radius=1), |
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connection_drawing_spec=drawing.DrawingSpec(thickness=1)) |
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drawing.draw_landmarks(frm, res.left_hand_landmarks, hands.HAND_CONNECTIONS) |
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drawing.draw_landmarks(frm, res.right_hand_landmarks, hands.HAND_CONNECTIONS) |
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return av.VideoFrame.from_ndarray(frm, format="bgr24") |
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lang = st.text_input("Language") |
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singer = st.text_input("singer") |
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if lang and singer and st.session_state["run"] != "false": |
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webrtc_streamer(key="key", desired_playing_state=True, |
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video_processor_factory=EmotionProcessor) |
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btn = st.button("Recommend me songs") |
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if btn: |
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if not(emotion): |
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st.warning("Please let me capture your emotion first") |
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st.session_state["run"] = "true" |
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else: |
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webbrowser.open(f"https://www.youtube.com/results?search_query={lang}+{emotion}+song+{singer}") |
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np.save("emotion.npy", np.array([""])) |
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st.session_state["run"] = "false" |
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