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| from PIL import Image | |
| import numpy as np | |
| import mediapipe as mp | |
| import tensorflow as tf | |
| import webbrowser | |
| import streamlit as st | |
| # Load model and labels | |
| model = tf.keras.models.load_model("model.h5") | |
| label = np.load("labels.npy") | |
| # MediaPipe setup | |
| holistic = mp.solutions.holistic | |
| holis = holistic.Holistic() | |
| drawing = mp.solutions.drawing_utils | |
| # Streamlit UI setup | |
| st.header("Fun Zone ! Capture your Emote !😁🙁😒😏") | |
| # Session state for managing emotion detection | |
| if "emotion" not in st.session_state: | |
| st.session_state["emotion"] = "" | |
| # Function to process image and predict emotion | |
| def process_image(image): | |
| res = holis.process(np.array(image)) | |
| lst = [] | |
| if res.face_landmarks: | |
| for i in res.face_landmarks.landmark: | |
| lst.append(i.x - res.face_landmarks.landmark[1].x) | |
| lst.append(i.y - res.face_landmarks.landmark[1].y) | |
| if res.left_hand_landmarks: | |
| for i in res.left_hand_landmarks.landmark: | |
| lst.append(i.x - res.left_hand_landmarks.landmark[8].x) | |
| lst.append(i.y - res.left_hand_landmarks.landmark[8].y) | |
| else: | |
| for _ in range(42): | |
| lst.append(0.0) | |
| if res.right_hand_landmarks: | |
| for i in res.right_hand_landmarks.landmark: | |
| lst.append(i.x - res.right_hand_landmarks.landmark[8].x) | |
| lst.append(i.y - res.right_hand_landmarks.landmark[8].y) | |
| else: | |
| for _ in range(42): | |
| lst.append(0.0) | |
| lst = np.array(lst).reshape(1, -1) | |
| pred = label[np.argmax(model.predict(lst))] | |
| return res, pred | |
| return res, "" | |
| lang = st.text_input("Language") | |
| singer = st.text_input("Cast") | |
| # Upload button for capturing an image | |
| uploaded_file = st.file_uploader("Capture your image", type=["jpg", "jpeg", "png"]) | |
| if uploaded_file: | |
| # Process uploaded image using Pillow | |
| img = Image.open(uploaded_file) | |
| # Convert Pillow image to numpy array | |
| image_np = np.array(img) | |
| # Process image and predict emotion | |
| res, emotion = process_image(image_np) | |
| # Draw landmarks onto the image | |
| if res.face_landmarks: | |
| drawing.draw_landmarks(image_np, res.face_landmarks, holistic.FACEMESH_TESSELATION, | |
| landmark_drawing_spec=drawing.DrawingSpec(color=(0, 0, 255), thickness=1, circle_radius=1), | |
| connection_drawing_spec=drawing.DrawingSpec(color=(0, 255, 0), thickness=1)) | |
| if res.left_hand_landmarks: | |
| drawing.draw_landmarks(image_np, res.left_hand_landmarks, mp.solutions.hands.HAND_CONNECTIONS) | |
| if res.right_hand_landmarks: | |
| drawing.draw_landmarks(image_np, res.right_hand_landmarks, mp.solutions.hands.HAND_CONNECTIONS) | |
| # Display processed image | |
| st.image(image_np, caption='Uploaded Image', use_column_width=True) | |
| st.session_state["emotion"] = emotion | |
| if emotion: | |
| st.success(f"Emotion detected: {emotion}") | |
| btn = st.button("Recommend me movies") | |
| if btn: | |
| if not st.session_state["emotion"]: | |
| st.warning("Please upload an image to capture your emotion first") | |
| else: | |
| emotion = st.session_state["emotion"] | |
| youtube_link = f"https://www.youtube.com/results?search_query={lang}+{emotion}+movie+{singer}" | |
| st.info(f"Recommended movies for {emotion}:") | |
| st.write(youtube_link) | |
| if st.button("Open YouTube"): | |
| webbrowser.open(youtube_link) | |
| st.session_state["emotion"] = "" # Reset emotion after recommendation | |