Update app.py

app.py

@@ -5,23 +5,35 @@ import numpy as np
 from keras.models import load_model
 from PIL import Image
 import sqlite3
-import requests
-from io import BytesIO
+from huggingface_hub import HfApi
 
 # Constants
 DB_FILE = "students.db"
 KNOWN_FACES_DIR = "known_faces"
 EMOTION_MODEL_FILE = "CNN_Model_acc_75.h5"
-HUGGING_FACE_TOKEN = os.getenv("HUGGING_FACE_TOKEN")
-HUGGING_FACE_REPO = "username/repo_name"
-
-# Emotion Labels
 EMOTION_LABELS = ["Angry", "Disgust", "Fear", "Happy", "Sad", "Surprise", "Neutral"]
 
-# Create directories
+# Hugging Face Repository Details
+REPO_NAME = "face_and_emotion_detection"
+REPO_ID = f"LovnishVerma/{REPO_NAME}"  # Replace with your Hugging Face username and repository name
+
+# Ensure Directories
 os.makedirs(KNOWN_FACES_DIR, exist_ok=True)
 
-# Load models
+# Load Hugging Face Token
+hf_token = os.getenv("HUGGING_FACE_TOKEN")
+if not hf_token:
+    st.error("Hugging Face token not found. Please set the environment variable.")
+
+# 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 Emotion Model
 try:
     emotion_model = load_model(EMOTION_MODEL_FILE)
 except Exception as e:
@@ -30,8 +42,7 @@ except Exception as e:
 # Database Functions
 def create_table():
     with sqlite3.connect(DB_FILE) as conn:
-        cursor = conn.cursor()
-        cursor.execute("""
+        conn.execute("""
             CREATE TABLE IF NOT EXISTS students (
                 id INTEGER PRIMARY KEY AUTOINCREMENT,
                 name TEXT NOT NULL,
@@ -44,33 +55,13 @@ def create_table():
 def insert_student(name, roll_number, image_path):
     try:
         with sqlite3.connect(DB_FILE) as conn:
-            cursor = conn.cursor()
-            cursor.execute("INSERT INTO students (name, roll_number, image_path) VALUES (?, ?, ?)",
-                           (name, roll_number, image_path))
+            conn.execute("INSERT INTO students (name, roll_number, image_path) VALUES (?, ?, ?)",
+                         (name, roll_number, image_path))
             conn.commit()
     except sqlite3.IntegrityError:
         st.warning("Roll number already exists!")
 
-# Hugging Face Functions
-def upload_to_hugging_face(file_path, file_name):
-    if not HUGGING_FACE_TOKEN:
-        st.error("Hugging Face token not found.")
-        return
-
-    url = f"https://huggingface.co/api/repos/{HUGGING_FACE_REPO}/uploads/{file_name}"
-    headers = {"Authorization": f"Bearer {HUGGING_FACE_TOKEN}"}
-
-    try:
-        with open(file_path, "rb") as file:
-            response = requests.post(url, headers=headers, files={"file": file})
-        if response.status_code == 200:
-            st.success(f"Uploaded {file_name} to Hugging Face!")
-        else:
-            st.error(f"Failed to upload: {response.content}")
-    except Exception as e:
-        st.error(f"Error uploading file: {e}")
-
-# Image Processing Functions
+# Face and Emotion Detection
 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')
@@ -84,7 +75,6 @@ def detect_faces_and_emotions(image):
         emotion_prediction = emotion_model.predict(reshaped_face)
         emotion_label = np.argmax(emotion_prediction)
         return EMOTION_LABELS[emotion_label]
-
     return None
 
 # UI Design
@@ -110,7 +100,6 @@ if choice == "Register Student":
                     img_path = os.path.join(KNOWN_FACES_DIR, f"{roll_number}.png")
                     img.save(img_path)
                     insert_student(name, roll_number, img_path)
-                    upload_to_hugging_face(img_path, f"{roll_number}.png")
                     st.success("Student Registered Successfully!")
                 except Exception as e:
                     st.error(f"Error: {e}")
@@ -129,7 +118,7 @@ elif choice == "Face Recognition and Emotion Detection":
                 img = Image.open(uploaded_file)
                 img_array = np.array(img)
                 emotion_label = detect_faces_and_emotions(img_array)
-                if emotion_label is not None:
+                if emotion_label:
                     st.success(f"Emotion Detected: {emotion_label}")
                 else:
                     st.warning("No face detected.")
@@ -144,7 +133,7 @@ elif choice == "Face Recognition and Emotion Detection":
                 img = Image.open(camera_image)
                 img_array = np.array(img)
                 emotion_label = detect_faces_and_emotions(img_array)
-                if emotion_label is not None:
+                if emotion_label:
                     st.success(f"Emotion Detected: {emotion_label}")
                 else:
                     st.warning("No face detected.")