Update app.py

app.py

@@ -1,22 +1,14 @@
 import streamlit as st
-import cv2
 import os
-import numpy as np
-from keras.models import load_model
 from PIL import Image
-import sqlite3
 from huggingface_hub import HfApi
-from datetime import datetime
 
 # Constants
 KNOWN_FACES_DIR = "known_faces"  # Directory to save user images
-DATABASE = "students.db"  # SQLite database file to store student information
-EMOTION_MODEL_FILE = "CNN_Model_acc_75.h5"
-EMOTION_LABELS = ["Angry", "Disgust", "Fear", "Happy", "Sad", "Surprise", "Neutral"]
 REPO_NAME = "face_and_emotion_detection"
 REPO_ID = f"LovnishVerma/{REPO_NAME}"
 
-# Ensure the directories exist
+# Ensure the directory exists
 os.makedirs(KNOWN_FACES_DIR, exist_ok=True)
 
 # Retrieve Hugging Face token from environment variable
@@ -27,54 +19,17 @@ if not hf_token:
 
 # Initialize Hugging Face API
 api = HfApi()
-try:
-    api.create_repo(repo_id=REPO_ID, repo_type="space", space_sdk="streamlit", 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}")
-
-# Load the emotion detection model
-try:
-    emotion_model = load_model(EMOTION_MODEL_FILE)
-except Exception as e:
-    st.error(f"Error loading emotion model: {e}")
-    st.stop()
-
-# Database Functions
-def initialize_database():
-    """ Initializes the SQLite database by creating the students table if it doesn't exist. """
-    with sqlite3.connect(DATABASE) as conn:
-        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()
-
-def save_to_database(name, roll_no, image_path):
-    """ Saves the student's data to the database. """
-    with sqlite3.connect(DATABASE) as conn:
-        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!")
 
 def save_image_to_hugging_face(image, name, roll_no):
     """ Saves the image locally to the KNOWN_FACES_DIR and uploads it to Hugging Face. """
+    # Ensure the directory exists
+    if not os.path.exists(KNOWN_FACES_DIR):
+        os.makedirs(KNOWN_FACES_DIR)
+
+    # Construct the local file path
     filename = f"{name}_{roll_no}.jpg"
     local_path = os.path.join(KNOWN_FACES_DIR, filename)
-
+    
     try:
         # Convert image to RGB if necessary
         if image.mode != "RGB":
@@ -82,7 +37,7 @@ def save_image_to_hugging_face(image, name, roll_no):
         
         # Save the image to the known_faces directory
         image.save(local_path)
-
+        
         # Upload the saved file to Hugging Face
         api.upload_file(
             path_or_fileobj=local_path,
@@ -97,11 +52,7 @@ def save_image_to_hugging_face(image, name, roll_no):
     
     return local_path
 
-
-# Initialize the database when the app starts
-initialize_database()
-
-# Streamlit user interface (UI)
+# Streamlit UI
 st.title("Student Registration with Hugging Face Image Upload")
 
 # Input fields for student details
@@ -133,68 +84,5 @@ if st.button("Register"):
             
             # Save the image locally and upload it to Hugging Face
             image_path = save_image_to_hugging_face(image, name, roll_no)
-            save_to_database(name, roll_no, image_path)
         except Exception as e:
             st.error(f"An error occurred: {e}")
-
-# Display registered student data
-if st.checkbox("Show registered students"):
-    with sqlite3.connect(DATABASE) as conn:
-        cursor = conn.cursor()
-        cursor.execute("SELECT name, roll_no, image_path, timestamp FROM students")
-        rows = cursor.fetchall()
-    
-    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)
-
-# Face and Emotion Detection Function
-def detect_faces_and_emotions(image):
-    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
-    faces = face_cascade.detectMultiScale(gray_image, scaleFactor=1.3, minNeighbors=5)
-    
-    emotion_labels = []
-    for (x, y, w, h) in faces:
-        face = gray_image[y:y+h, x:x+w]
-        resized_face = cv2.resize(face, (48, 48))  # Resize face to 48x48
-        rgb_face = cv2.cvtColor(resized_face, cv2.COLOR_GRAY2RGB)
-        normalized_face = rgb_face / 255.0
-        reshaped_face = np.reshape(normalized_face, (1, 48, 48, 3))
-        
-        # Predict the emotion
-        emotion_prediction = emotion_model.predict(reshaped_face)
-        emotion_label = np.argmax(emotion_prediction)
-        emotion_labels.append(EMOTION_LABELS[emotion_label])
-    
-    return emotion_labels if emotion_labels else None
-
-# UI for Emotion Detection
-if st.sidebar.selectbox("Menu", ["Register Student", "Face Recognition and Emotion Detection", "View Attendance"]) == "Face Recognition and Emotion Detection":
-    st.subheader("Recognize Faces and Detect Emotions")
-    action = st.radio("Choose Action", ["Upload Image", "Use Webcam"])
-
-    if action == "Upload Image":
-        uploaded_file = st.file_uploader("Upload Image", type=["jpg", "jpeg", "png"])
-        if uploaded_file:
-            img = Image.open(uploaded_file)
-            img_array = np.array(img)
-            emotion_labels = detect_faces_and_emotions(img_array)
-            if emotion_labels:
-                st.success(f"Emotions Detected: {', '.join(emotion_labels)}")
-            else:
-                st.warning("No face detected.")
-
-    elif action == "Use Webcam":
-        st.info("Use the camera input widget to capture an image.")
-        camera_image = st.camera_input("Take a picture")
-        if camera_image:
-            img = Image.open(camera_image)
-            img_array = np.array(img)
-            emotion_labels = detect_faces_and_emotions(img_array)
-            if emotion_labels:
-                st.success(f"Emotions Detected: {', '.join(emotion_labels)}")
-            else:
-                st.warning("No face detected.")