Update app.py

app.py

@@ -9,17 +9,13 @@ from tensorflow.keras.models import load_model  # Importing load_model
 from datetime import datetime  # Importing datetime
 
 # Constants
-HOME_DIR = os.getcwd()  # Home directory to save images (root directory)
+HOME_DIR = os.getcwd()  # Home directory (root directory)
 DATABASE = "students.db"  # SQLite database to store student information
 REPO_NAME = "face-and-emotion-detection"
 REPO_ID = f"LovnishVerma/{REPO_NAME}"  # Hugging Face Repo
 EMOTION_MODEL_FILE = "CNN_Model_acc_75.h5"  # Emotion detection model file
 EMOTION_LABELS = ["Angry", "Disgust", "Fear", "Happy", "Sad", "Surprise", "Neutral"]
 
-# Ensure home directory exists
-if not os.path.exists(HOME_DIR):
-    os.makedirs(HOME_DIR)
-
 # Retrieve Hugging Face token from environment variable
 hf_token = os.getenv("upload")
 if not hf_token:
@@ -58,22 +54,22 @@ def initialize_database():
             id INTEGER PRIMARY KEY AUTOINCREMENT,
             name TEXT NOT NULL,
             roll_no TEXT NOT NULL UNIQUE,
-            image_path TEXT NOT NULL,
+            image_url TEXT NOT NULL,
             timestamp DATETIME DEFAULT CURRENT_TIMESTAMP
         )
     """)
     conn.commit()
     conn.close()
 
-def save_to_database(name, roll_no, image_path):
+def save_to_database(name, roll_no, image_url):
     """ Saves the student's data to the database. """
     conn = sqlite3.connect(DATABASE)
     cursor = conn.cursor()
     try:
         cursor.execute("""
-            INSERT INTO students (name, roll_no, image_path)
+            INSERT INTO students (name, roll_no, image_url)
             VALUES (?, ?, ?)
-        """, (name, roll_no, image_path))
+        """, (name, roll_no, image_url))
         conn.commit()
         st.success("Data saved successfully!")
     except sqlite3.IntegrityError:
@@ -103,11 +99,15 @@ def save_image_to_hugging_face(image, name, roll_no):
             repo_type="space",
             token=hf_token,
         )
-        st.success(f"Image saved to {HOME_DIR} and uploaded to Hugging Face as {filename}.")
+
+        # Construct the image URL for Hugging Face
+        image_url = f"https://{REPO_NAME}.hf.space/media/{filename}"
+        st.success(f"Image saved to Hugging Face as {filename}. URL: {image_url}")
+
     except Exception as e:
         st.error(f"Error saving or uploading image: {e}")
     
-    return local_path
+    return image_url
 
 # Initialize the database when the app starts
 initialize_database()
@@ -143,8 +143,8 @@ if st.button("Register"):
                 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_to_database(name, roll_no, image_path)
+            image_url = save_image_to_hugging_face(image, name, roll_no)
+            save_to_database(name, roll_no, image_url)
         except Exception as e:
             st.error(f"An error occurred: {e}")
 
@@ -152,15 +152,15 @@ if st.button("Register"):
 if st.checkbox("Show registered students"):
     conn = sqlite3.connect(DATABASE)
     cursor = conn.cursor()
-    cursor.execute("SELECT name, roll_no, image_path, timestamp FROM students")
+    cursor.execute("SELECT name, roll_no, image_url, timestamp FROM students")
     rows = cursor.fetchall()
     conn.close()
     
     st.write("### Registered Students")
     for row in rows:
-        name, roll_no, image_path, timestamp = row
+        name, roll_no, image_url, 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)
+        st.image(image_url, caption=f"{name} ({roll_no})", use_column_width=True)
 
 # Face and Emotion Detection Function
 def detect_faces_and_emotions(image):