Update app.py

app.py

@@ -1,13 +1,12 @@
 import os
 import sqlite3
-import cv2
 import streamlit as st
-from datetime import datetime
 from PIL import Image
 import numpy as np
+import cv2
 from keras.models import load_model
+from datetime import datetime
 from huggingface_hub import HfApi
-import time
 
 # Constants
 KNOWN_FACES_DIR = "known_faces"  # Directory to save user images
@@ -17,7 +16,7 @@ DATABASE = "students.db"  # SQLite database file to store student information
 os.makedirs(KNOWN_FACES_DIR, exist_ok=True)
 
 # Initialize Hugging Face API
-hf_token = os.getenv("upload")  # The key must match the secret name set in Hugging Face
+hf_token = os.getenv("upload")  # Ensure this is set correctly as a secret in Hugging Face
 if not hf_token:
     raise ValueError("Hugging Face token not found. Ensure it's set as a secret in Hugging Face")
 api = HfApi()
@@ -64,13 +63,18 @@ def save_to_database(name, roll_no, image_path):
     finally:
         conn.close()
 
-# Save the captured image to Hugging Face and return the local path
+# Save the captured image locally in known_faces directory and upload to Hugging Face
 def save_image_to_hugging_face(image, name, roll_no):
+    # Create a filename based on the student name and roll number
     filename = f"{name}_{roll_no}.jpg"
     local_path = os.path.join(KNOWN_FACES_DIR, filename)
+    
+    # Save the image locally
     image.save(local_path)
-
+    st.success(f"Image saved locally to {local_path}")
+    
     try:
+        # Upload the image to Hugging Face repository
         api.upload_file(
             path_or_fileobj=local_path,
             path_in_repo=filename,
@@ -81,7 +85,7 @@ def save_image_to_hugging_face(image, name, roll_no):
         st.success(f"Image uploaded to Hugging Face: {filename}")
     except Exception as e:
         st.error(f"Error uploading image to Hugging Face: {e}")
-
+    
     return local_path
 
 # Process each frame for emotion detection
@@ -103,101 +107,61 @@ def process_frame(frame):
         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)
 
-    return frame, faces  # Return the frame and the detected faces
-
-# Attendance recording
-def record_attendance(name, roll_no, emotion):
-    conn = sqlite3.connect(DATABASE)
-    cursor = conn.cursor()
-    cursor.execute("""
-        INSERT INTO students (name, roll_no, image_path, timestamp)
-        VALUES (?, ?, ?, ?)
-    """, (name, roll_no, f"known_faces/{name}_{roll_no}.jpg", datetime.now()))
-    conn.commit()
-    conn.close()
-
-# User Interface - Streamlit tabs
-st.title("Student Registration and Attendance System")
-
-tabs = st.selectbox("Choose Functionality", ["Register Student", "Record Attendance", "View Attendance"])
-
-if tabs == "Register Student":
-    st.subheader("Student Registration")
-
-    # Choose input method for the image (webcam or file upload)
-    capture_mode = st.radio("Choose an option to upload your image", ["Use Webcam", "Upload File"])
-
-    if capture_mode == "Use Webcam":
-        picture = st.camera_input("Take a picture")  # Capture image using webcam
-    elif capture_mode == "Upload File":
-        picture = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
-
-    # Input fields for student details
-    name = st.text_input("Enter your name")
-    roll_no = st.text_input("Enter your roll number")
-
-    # Handle image upload or webcam capture
-    if st.button("Register"):
-        if not name or not roll_no:
-            st.error("Please fill in both name and roll number.")
-        elif not picture:
-            st.error("Please upload or capture an image.")
-        else:
-            try:
-                # Open the image based on capture mode
-                if capture_mode == "Use Webcam" and picture:
-                    image = Image.open(picture)
-                elif capture_mode == "Upload File" and picture:
-                    image = Image.open(picture)
-
-                # Save the image locally and upload it to Hugging Face
-                image_path = save_image_to_hugging_face(image, name, roll_no)
-
-                # Save user data to the database
-                save_to_database(name, roll_no, image_path)
-
-                st.success("Registration Successful!")
-
-            except Exception as e:
-                st.error(f"An error occurred: {e}")
-
-elif tabs == "Record Attendance":
-    st.subheader("Record Attendance")
-
-    # Initialize the webcam for face detection and emotion recognition
-    cap = cv2.VideoCapture(0)
-
-    while True:
-        ret, frame = cap.read()
-        if not ret:
-            break
-
-        # Process the frame to detect faces and emotions
-        frame, faces = process_frame(frame)
-
-        # Show the image with detected faces and emotions
-        st.image(frame, channels="BGR", use_column_width=True)
-
-        # When a face is detected, assume it's the person for attendance
-        if len(faces) > 0:
-            # Assume the first face detected corresponds to the student
-            x, y, w, h = faces[0]
-            # Extract the emotion from the frame (for simplicity, use the first detected face)
-            name = "John Doe"  # Replace with the actual name after face recognition
-            roll_no = "12345"  # Replace with the actual roll number
-            emotion = emotion_labels[np.argmax(model.predict(cv2.resize(frame[y:y+h, x:x+w], (48, 48)).reshape(1, 48, 48, 3)))]  # Get emotion of detected face
-
-            # Record attendance based on the detected information
-            if st.button("Record Attendance"):
-                record_attendance(name, roll_no, emotion)
-                st.success(f"Attendance recorded for {name} ({roll_no}) with emotion: {emotion}")
+    return frame
+
+# User Interface for registration
+st.title("Student Registration and Attendance")
+
+# Choose input method for the image (webcam or file upload)
+capture_mode = st.radio("Choose an option to upload your image", ["Use Webcam", "Upload File"])
+
+if capture_mode == "Use Webcam":
+    picture = st.camera_input("Take a picture")  # Capture image using webcam
+elif capture_mode == "Upload File":
+    picture = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+
+# Input fields for student details
+name = st.text_input("Enter your name")
+roll_no = st.text_input("Enter your roll number")
+
+# Handle image upload or webcam capture
+if st.button("Register"):
+    if not name or not roll_no:
+        st.error("Please fill in both name and roll number.")
+    elif not picture:
+        st.error("Please upload or capture an image.")
+    else:
+        try:
+            # Open the image based on capture mode
+            if capture_mode == "Use Webcam" and picture:
+                image = Image.open(picture)
+            elif capture_mode == "Upload File" and picture:
+                image = Image.open(picture)
+
+            # Save the image locally and upload it to Hugging Face
+            image_path = save_image_to_hugging_face(image, name, roll_no)
+
+            # Save user data to the database
+            save_to_database(name, roll_no, image_path)
+
+            # Detect faces and emotions
+            cap = cv2.VideoCapture(0)
+            while True:
+                ret, frame = cap.read()
+                if not ret:
+                    break
+
+                frame = process_frame(frame)
+                st.image(frame, channels="BGR", use_column_width=True)
                 break  # Stop after capturing one frame
 
-    cap.release()
+            cap.release()
 
-elif tabs == "View Attendance":
-    st.subheader("View Attendance Records")
+        except Exception as e:
+            st.error(f"An error occurred: {e}")
 
+# Display registered students and attendance history
+if st.checkbox("Show registered students"):
     conn = sqlite3.connect(DATABASE)
     cursor = conn.cursor()
     cursor.execute("SELECT name, roll_no, image_path, timestamp FROM students")