Update app.py

app.py

@@ -1,98 +1,124 @@
 import os
-import cv2
+import sqlite3
+from huggingface_hub import HfApi
 import streamlit as st
 from datetime import datetime
+from PIL import Image
 
-# Constants
+# Directories and Hugging Face Token
 KNOWN_FACES_DIR = "known_faces"
 os.makedirs(KNOWN_FACES_DIR, exist_ok=True)
 
-# Global State
-if "activity_log" not in st.session_state:
-    st.session_state.activity_log = []
-
-# Streamlit App Title
-st.title("Face Detection and Registration App")
-
-st.sidebar.header("Navigation")
-page = st.sidebar.radio("Choose an option:", ["Real-time Face Detection", "Register New Face", "View Registered Faces", "Recent Activity"])
-
-# Initialize Cascade Classifier
-face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
-
-# Helper Function for Real-time Face Detection
-def start_camera(register_mode=False, face_name=None):
-    cap = cv2.VideoCapture(0)  # Open the camera
-
-    if not cap.isOpened():
-        st.error("Unable to access the camera. Please check your webcam or permissions.")
-        return
-
-    stframe = st.empty()  # Streamlit placeholder for video frames
-
-    while cap.isOpened():
-        ret, frame = cap.read()
-        if not ret:
-            st.error("Failed to grab frame from camera.")
-            break
-
-        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-        faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(50, 50))
-
-        for (x, y, w, h) in faces:
-            # Draw rectangle
-            cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
-
-            if register_mode and face_name:
-                # Save the cropped face image
-                face_img = frame[y:y + h, x:x + w]
-                face_filename = f"{face_name}_{datetime.now().strftime('%Y%m%d%H%M%S')}.jpg"
-                face_path = os.path.join(KNOWN_FACES_DIR, face_filename)
-                cv2.imwrite(face_path, face_img)
-
-                st.session_state.activity_log.append(
-                    f"Registered face: {face_name} at {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
-                )
-                st.success(f"Face registered successfully as {face_name}!")
-                return  # Stop after registering
-
-        stframe.image(frame, channels="BGR", use_column_width=True)
-
-    cap.release()
-    cv2.destroyAllWindows()
-
-# Pages
-if page == "Real-time Face Detection":
-    st.header("Real-time Face Detection")
-    if st.button("Start Camera"):
-        start_camera()
-
-elif page == "Register New Face":
-    st.header("Register New Face")
-    name = st.text_input("Enter a name for the face:")
-
-    if st.button("Start Registration"):
-        if name.strip():
-            st.info(f"Looking for face to register as '{name}'...")
-            start_camera(register_mode=True, face_name=name)
-        else:
-            st.error("Please enter a valid name!")
-
-elif page == "View Registered Faces":
-    st.header("Registered Faces")
-    faces = os.listdir(KNOWN_FACES_DIR)
-
-    if faces:
-        st.write(f"*Total Registered Faces: {len(faces)}*")
-        for face_file in faces:
-            st.write(face_file)
+# Retrieve the token from the environment
+hf_token = os.getenv("token")  # Ensure the key matches the name in Hugging Face secrets
+if not hf_token:
+    raise ValueError("Hugging Face token not found. Ensure it's set as a secret in the Hugging Face Space.")
+
+# Initialize the Hugging Face API
+api = HfApi()
+api.set_access_token(hf_token)
+
+# Database setup
+DATABASE = "students.db"
+
+def initialize_database():
+    conn = sqlite3.connect(DATABASE)
+    cursor = conn.cursor()
+    cursor.execute("""
+        CREATE TABLE IF NOT EXISTS students (
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            name TEXT NOT NULL,
+            roll_no TEXT NOT NULL UNIQUE,
+            image_path TEXT NOT NULL,
+            timestamp DATETIME DEFAULT CURRENT_TIMESTAMP
+        )
+    """)
+    conn.commit()
+    conn.close()
+
+def save_to_database(name, roll_no, image_path):
+    conn = sqlite3.connect(DATABASE)
+    cursor = conn.cursor()
+    try:
+        cursor.execute("""
+            INSERT INTO students (name, roll_no, image_path)
+            VALUES (?, ?, ?)
+        """, (name, roll_no, image_path))
+        conn.commit()
+        st.success("Data saved successfully!")
+    except sqlite3.IntegrityError:
+        st.error("Roll number already exists!")
+    finally:
+        conn.close()
+
+def save_image_to_hugging_face(image, roll_no):
+    filename = f"{roll_no}_{datetime.now().strftime('%Y%m%d%H%M%S')}.jpg"
+    local_path = os.path.join(KNOWN_FACES_DIR, filename)
+    image.save(local_path)
+
+    # Upload to Hugging Face
+    try:
+        repo_name = "LovnishVerma/face_and_emotion_detection"  # Replace with your repo details
+        api.upload_file(
+            path_or_fileobj=local_path,
+            path_in_repo=filename,
+            repo_id=repo_name,
+            repo_type="dataset"
+        )
+        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
+
+# Initialize database
+initialize_database()
+
+# Streamlit app
+st.title("Student Registration with Hugging Face Image Upload")
+
+# Input details
+name = st.text_input("Enter your name")
+roll_no = st.text_input("Enter your roll number")
+
+# Choose image input method
+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")
+
+elif capture_mode == "Upload File":
+    picture = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+
+# Save and process data
+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:
-        st.write("No registered faces found.")
-
-elif page == "Recent Activity":
-    st.header("Recent Activity Log")
-    if st.session_state.activity_log:
-        for log in reversed(st.session_state.activity_log):
-            st.write(log)
-    else:
-        st.write("No recent activity to display.")
+        try:
+            if capture_mode == "Use Webcam":
+                image = Image.open(picture)
+            else:
+                image = Image.open(picture)
+            
+            # Save locally and upload to Hugging Face
+            image_path = save_image_to_hugging_face(image, roll_no)
+            save_to_database(name, roll_no, image_path)
+        except Exception as e:
+            st.error(f"An error occurred: {e}")
+
+# Display stored data
+if st.checkbox("Show registered students"):
+    conn = sqlite3.connect(DATABASE)
+    cursor = conn.cursor()
+    cursor.execute("SELECT name, roll_no, image_path, timestamp FROM students")
+    rows = cursor.fetchall()
+    conn.close()
+    
+    st.write("### Registered Students")
+    for row in rows:
+        name, roll_no, image_path, timestamp = row
+        st.write(f"**Name:** {name}, **Roll No:** {roll_no}, **Timestamp:** {timestamp}")
+        st.image(image_path, caption=f"{name} ({roll_no})", use_column_width=True)