Create app.py

app.py

@@ -0,0 +1,97 @@
+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"]
+        webbrowser.open(f"https://www.youtube.com/results?search_query={lang}+{emotion}+movie+{singer}")
+        st.session_state["emotion"] = ""  # Reset emotion after recommendation