Update app.py

app.py

@@ -2,96 +2,135 @@ import streamlit as st
 import cv2
 import numpy as np
 import time
-# Larger title
-st.markdown("<h1 style='text-align: center;'>Emotion Detection</h1>", unsafe_allow_html=True)
-
-# Smaller subtitle
-st.markdown("<h3 style='text-align: center;'>angry, fear, happy, neutral, sad, surprise</h3>", unsafe_allow_html=True)
-start = time.time()
 from keras.models import load_model
-import tempfile
 from PIL import Image
+from huggingface_hub import HfApi, Repository
+import os
+import tempfile
+
+# Page configuration
+st.set_page_config(page_title="Emotion Detection", layout="centered")
+
+# Title and Subtitle
+st.markdown("<h1 style='text-align: center;'>Emotion Detection</h1>", unsafe_allow_html=True)
+st.markdown("<h3 style='text-align: center;'>angry, fear, happy, neutral, sad, surprise</h3>", unsafe_allow_html=True)
 
+# Load Model
 @st.cache_resource
 def load_emotion_model():
     model = load_model('CNN_Model_acc_75.h5')
     return model
 
-# Load the model
+start_time = time.time()
 model = load_emotion_model()
-print("time taken to load model : " , time.time() - start)
-img_shape = 48
+st.write(f"Model loaded in {time.time() - start_time:.2f} seconds.")
+
+# Emotion labels and constants
 emotion_labels = ['angry', 'fear', 'happy', 'neutral', 'sad', 'surprise']
+img_shape = 48
 face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
 
-
 def process_frame(frame):
+    """Detect faces and predict emotions."""
     gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
     faces = face_cascade.detectMultiScale(gray_frame, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
-
     for (x, y, w, h) in faces:
         roi_gray = gray_frame[y:y+h, x:x+w]
         roi_color = frame[y:y+h, x:x+w]
-
         face_roi = cv2.resize(roi_color, (img_shape, img_shape))
         face_roi = np.expand_dims(face_roi, axis=0)
         face_roi = face_roi / float(img_shape)
         predictions = model.predict(face_roi)
         emotion = emotion_labels[np.argmax(predictions[0])]
 
+        # Draw rectangle and emotion label
         cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
-        cv2.putText(frame, emotion, (x, y+h), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
-
+        cv2.putText(frame, emotion, (x, y + h + 20), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)
     return frame
 
-# def video_feed(video_source):
-#     # Read and process video frames
-#     while True:
-#         ret, frame = video_source.read()
-#         if not ret:
-#             break
-#         frame = process_frame(frame)
-#         st.image(frame, channels="BGR")
-
-def video_feed(video_source):
-    # Create a placeholder to display the frames
-    frame_placeholder = st.empty()  # This placeholder will be used to replace frames in-place
-
-    while True:
-        ret, frame = video_source.read()
-        if not ret:
-            break
-
-        frame = process_frame(frame)
-
-        # Display the frame in the placeholder
-        frame_placeholder.image(frame, channels="BGR", use_column_width=True)
-
-
-
-# Sidebar for video or image upload
-upload_choice = st.sidebar.radio("Choose input source", [ "Upload Video", "Upload Image" ,"Camera"])
+# Sidebar for input selection
+st.sidebar.title("Choose Input Source")
+upload_choice = st.sidebar.radio("Select:", ["Camera", "Upload Video", "Upload Image", "Upload to Hugging Face"])
 
 if upload_choice == "Camera":
-    # Access camera
-    video_source = cv2.VideoCapture(0)
-    video_feed(video_source)
+    # Use Streamlit's camera input widget
+    st.sidebar.info("Click a picture to analyze emotion.")
+    picture = st.camera_input("Take a picture")
+    if picture:
+        image = Image.open(picture)
+        frame = np.array(image)
+        frame = process_frame(frame)
+        st.image(frame, caption="Processed Image", use_column_width=True)
 
 elif upload_choice == "Upload Video":
     uploaded_video = st.file_uploader("Upload Video", type=["mp4", "mov", "avi", "mkv", "webm"])
     if uploaded_video:
-        # Temporarily save the video to disk
         with tempfile.NamedTemporaryFile(delete=False) as tfile:
             tfile.write(uploaded_video.read())
             video_source = cv2.VideoCapture(tfile.name)
-            video_feed(video_source)
+            frame_placeholder = st.empty()
+            while video_source.isOpened():
+                ret, frame = video_source.read()
+                if not ret:
+                    break
+                frame = process_frame(frame)
+                frame_placeholder.image(frame, channels="BGR", use_column_width=True)
+            video_source.release()
 
 elif upload_choice == "Upload Image":
-    uploaded_image = st.file_uploader("Upload Image", type=["png", "jpg", "jpeg", "gif"])
+    uploaded_image = st.file_uploader("Upload Image", type=["png", "jpg", "jpeg"])
     if uploaded_image:
         image = Image.open(uploaded_image)
         frame = np.array(image)
         frame = process_frame(frame)
-        st.image(frame, caption='Processed Image', use_column_width=True)
-
-st.sidebar.write("Emotion Labels: Angry, Fear, Happy, Neutral, Sad, Surprise")
+        st.image(frame, caption="Processed Image", use_column_width=True)
+
+elif upload_choice == "Upload to Hugging Face":
+    st.sidebar.info("Upload images to the 'known_faces' directory in the Hugging Face repository.")
+
+    # Configure Hugging Face Repository
+    REPO_NAME = "face_emotion_detection2"
+    REPO_ID = "LovnishVerma/" + REPO_NAME
+    hf_token = os.getenv("uploadphoto1")  # Set your Hugging Face token as an environment variable
+
+    if not hf_token:
+        st.error("Hugging Face token not found. Please set it as an environment variable named 'HF_TOKEN'.")
+        st.stop()
+
+    # Initialize Hugging Face API
+    api = HfApi()
+
+    def create_hugging_face_repo():
+        """Create or verify the Hugging Face repository."""
+        try:
+            api.create_repo(repo_id=REPO_ID, repo_type="dataset", token=hf_token, exist_ok=True)
+            st.success(f"Repository '{REPO_NAME}' is ready on Hugging Face!")
+        except Exception as e:
+            st.error(f"Error creating Hugging Face repository: {e}")
+
+    def upload_to_hugging_face(file):
+        """Upload a file to the Hugging Face repository."""
+        try:
+            with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_file:
+                temp_file.write(file.read())
+                temp_file_path = temp_file.name
+
+            api.upload_file(
+                path_or_fileobj=temp_file_path,
+                path_in_repo=f"known_faces/{os.path.basename(temp_file_path)}",
+                repo_id=REPO_ID,
+                token=hf_token,
+            )
+            st.success("File uploaded successfully to Hugging Face!")
+        except Exception as e:
+            st.error(f"Error uploading file to Hugging Face: {e}")
+
+    # Create the repository if it doesn't exist
+    create_hugging_face_repo()
+
+    # Upload image file
+    hf_uploaded_image = st.file_uploader("Upload Image to Hugging Face", type=["png", "jpg", "jpeg"])
+    if hf_uploaded_image:
+        upload_to_hugging_face(hf_uploaded_image)
+
+st.sidebar.write("Emotion Labels: Angry, Fear, Happy, Neutral, Sad, Surprise")