Update app.py

app.py

@@ -1,62 +1,93 @@
 import streamlit as st
+import cv2
 import os
+import numpy as np
+from keras.models import load_model
 from PIL import Image
 from huggingface_hub import HfApi
+from datetime import datetime
 
 # Constants
 KNOWN_FACES_DIR = "known_faces"  # Directory to save user images
+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 directory exists
+# Ensure the directories exist
 os.makedirs(KNOWN_FACES_DIR, exist_ok=True)
 
-# Retrieve Hugging Face token from environment variable
-hf_token = os.getenv("upload")  # Replace with your actual Hugging Face token
-if not hf_token:
-    st.error("Hugging Face token not found. Please set the environment variable.")
-    st.stop()
-
 # Initialize Hugging Face API
+hf_token = os.getenv("upload")  # Replace with your actual Hugging Face token
 api = HfApi()
 
-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)
+# Load 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()
 
-    # Construct the local file path
-    filename = f"{name}_{roll_no}.jpg"
-    local_path = os.path.join(KNOWN_FACES_DIR, filename)
+# Face and Emotion Detection Function
+def detect_faces_and_emotions(image):
+    """Detect faces and emotions in the 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)
     
-    try:
-        # Convert image to RGB if necessary
-        if image.mode != "RGB":
-            image = image.convert("RGB")
+    emotion_label = None
+    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))
         
-        # Save the image to the known_faces directory
-        image.save(local_path)
-        
-        # Debug: Print the path where the image is saved
-        st.write(f"Image saved locally at: {local_path}")
-        
-        # Upload the saved file to Hugging Face
-        api.upload_file(
-            path_or_fileobj=local_path,
-            path_in_repo=filename,
-            repo_id=REPO_ID,
-            repo_type="space",
-            token=hf_token,
-        )
-        st.success(f"Image saved to {KNOWN_FACES_DIR} and uploaded to Hugging Face as {filename}.")
-    except Exception as e:
-        st.error(f"Error saving or uploading image: {e}")
+        # Predict the emotion
+        emotion_prediction = emotion_model.predict(reshaped_face)
+        emotion_label = np.argmax(emotion_prediction)
+    
+    return faces, EMOTION_LABELS[emotion_label] if emotion_label else None
+
+# Face Recognition Function
+def recognize_face(image):
+    """Recognize the face in the uploaded image by comparing with known faces"""
+    recognizer = cv2.face.LBPHFaceRecognizer_create()
     
-    return local_path
+    known_faces = []
+    labels = []
+    
+    # Load known faces from the directory
+    for filename in os.listdir(KNOWN_FACES_DIR):
+        if filename.endswith(".jpg"):
+            image_path = os.path.join(KNOWN_FACES_DIR, filename)
+            known_image = cv2.imread(image_path)
+            gray_image = cv2.cvtColor(known_image, cv2.COLOR_BGR2GRAY)
+            faces = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml').detectMultiScale(gray_image, scaleFactor=1.3, minNeighbors=5)
+            
+            for (x, y, w, h) in faces:
+                face = gray_image[y:y+h, x:x+w]
+                known_faces.append(face)
+                labels.append(filename.split(".")[0])  # Use image name as label
+    
+    if known_faces:
+        recognizer.train(known_faces, np.array(range(len(labels))))  # Train recognizer with known faces
+    
+    # Detect faces in the uploaded image
+    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    faces = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml').detectMultiScale(gray_image, scaleFactor=1.3, minNeighbors=5)
+    
+    recognized_name = "Unknown"
+    for (x, y, w, h) in faces:
+        face = gray_image[y:y+h, x:x+w]
+        label, confidence = recognizer.predict(face)
+        if confidence < 100:  # Confidence threshold
+            recognized_name = labels[label]  # Get the name from labels
+    
+    return recognized_name
 
 # Streamlit UI
-st.title("Student Registration with Hugging Face Image Upload")
+st.title("Student Registration with Face Recognition and Emotion Detection")
 
 # Input fields for student details
 name = st.text_input("Enter your name")
@@ -85,7 +116,19 @@ if st.button("Register"):
             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)
+            # Convert the image to numpy array for processing
+            img_array = np.array(image)
+            
+            # Detect faces and emotions
+            faces, emotion_label = detect_faces_and_emotions(img_array)
+            if faces:
+                st.success(f"Emotion Detected: {emotion_label}")
+            else:
+                st.warning("No face detected.")
+            
+            # Perform face recognition
+            recognized_name = recognize_face(img_array)
+            st.success(f"Face Recognized as: {recognized_name}")
+        
         except Exception as e:
             st.error(f"An error occurred: {e}")