Create app.py

app.py

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+
+import streamlit as st
+import os
+import pandas as pd
+from PIL import Image
+import io
+import datetime
+from huggingface_hub import HfApi, hf_hub_download, upload_file
+from attendance_system import AttendanceSystem
+from record_attendance import record_attendance_new
+from huggingface_hub import create_repo  
+
+# Function to create a new Hugging Face repository  
+def create_new_hf_repo(repo_id, repo_type="model", private=True):  
+    try:  
+        create_repo(repo_id, repo_type=repo_type, private=private)  
+        st.success(f"New Hugging Face repository created: {repo_id}")  
+        return repo_id  
+    except Exception as e:  
+        st.error(f"Failed to create Hugging Face repository: {e}")  
+        return None  
+
+# Add a new section in your Streamlit app  
+st.header("Create New Hugging Face Repository")  
+
+# Set the repository details  
+new_repo_id = "PRIYANSHUDHAKED/attendance-embeddings"  
+repo_type = "model"  
+is_private = True  
+
+# Create the new repository  
+new_repo_id = create_new_hf_repo(new_repo_id, repo_type=repo_type, private=is_private)  
+if new_repo_id:  
+    # Update the HF_REPO_ID variable with the new repository ID  
+    HF_REPO_ID = new_repo_id  
+
+
+# Set up AttendanceSystem
+DATABASE_DIR = "database"
+os.makedirs(DATABASE_DIR, exist_ok=True)
+attendance_system = AttendanceSystem(DATABASE_DIR)
+
+# Function to save embeddings to Hugging Face
+def save_embeddings_to_hf(roll_number, embedding):
+    filename = f"{roll_number}_embedding.npy"
+    with io.BytesIO() as buffer:
+        np.save(buffer, embedding)
+        buffer.seek(0)
+        upload_file(
+            path_or_fileobj=buffer,
+            path_in_repo=f"embeddings/{filename}",
+            repo_id=HF_REPO_ID,
+            repo_type=HF_REPO_TYPE,
+        )
+
+# Function to load embeddings from Hugging Face
+def load_embeddings_from_hf():
+    embeddings = {}
+    files = hf_api.list_repo_files(repo_id=HF_REPO_ID, repo_type=HF_REPO_TYPE)
+    for file in files:
+        if file.startswith("embeddings/") and file.endswith("_embedding.npy"):
+            roll_number = file.split("/")[-1].split("_")[0]
+            file_content = hf_hub_download(repo_id=HF_REPO_ID, filename=file, repo_type=HF_REPO_TYPE)
+            embedding = np.load(file_content)
+            embeddings[roll_number] = embedding
+    return embeddings
+
+# Function to save CSV to Hugging Face
+def save_csv_to_hf(subject):
+    filename = f"{subject}.csv"
+    upload_file(
+        path_or_fileobj=filename,
+        path_in_repo=f"attendance/{filename}",
+        repo_id=HF_REPO_ID,
+        repo_type=HF_REPO_TYPE,
+    )
+
+# Function to load CSV from Hugging Face
+def load_csv_from_hf(subject):
+    filename = f"{subject}.csv"
+    try:
+        file_content = hf_hub_download(repo_id=HF_REPO_ID, filename=f"attendance/{filename}", repo_type=HF_REPO_TYPE)
+        return pd.read_csv(file_content)
+    except:
+        return None
+
+# Load embeddings from Hugging Face
+attendance_system.database = load_embeddings_from_hf()
+
+st.title("Attendance System")
+
+# Sidebar for navigation
+page = st.sidebar.selectbox("Choose a page", ["Take Attendance", "Add to Database", "View Attendance"])
+
+if page == "Take Attendance":
+    st.header("Take Attendance")
+
+    # Course selection
+    courses = [f.split('.')[0] for f in hf_api.list_repo_files(repo_id=HF_REPO_ID, repo_type=HF_REPO_TYPE) if f.endswith('.csv')]
+    course = st.selectbox("Select Course", courses)
+
+    # Date selection
+    date = st.date_input("Select Date", datetime.date.today())
+
+    # Image upload
+    uploaded_file = st.file_uploader("Upload Classroom Image", type=["jpg", "jpeg", "png"])
+
+    if st.button("Take Attendance"):
+        if uploaded_file is not None:
+            # Save the uploaded image temporarily
+            image = Image.open(uploaded_file)
+            img_path = "temp_classroom.jpg"
+            image.save(img_path)
+
+            # Process attendance
+            present_students = attendance_system.record_attendance(course, str(date), img_path)
+
+            # Display results
+            st.subheader("Present Students:")
+            st.write(", ".join(present_students))
+
+            # Option to mark attendance in sheet
+            if st.button("Mark Attendance in Sheet"):
+                record_attendance_new(present_students, course)
+                save_csv_to_hf(course)
+                st.success("Attendance marked successfully!")
+
+            # Clean up temporary file
+            os.remove(img_path)
+        else:
+            st.error("Please upload an image.")
+
+elif page == "Add to Database":
+    st.header("Add to Database")
+
+    roll_number = st.text_input("Enter Roll Number")
+    uploaded_file = st.file_uploader("Upload Student Image", type=["jpg", "jpeg", "png"])
+
+    if st.button("Add to Database"):
+        if uploaded_file is not None and roll_number:
+            # Save the uploaded image temporarily
+            image = Image.open(uploaded_file)
+            img_path = f"temp_{roll_number}.jpg"
+            image.save(img_path)
+
+            # Add to database
+            if attendance_system.add_to_database(roll_number, img_path):
+                # Save embeddings to Hugging Face
+                embedding = attendance_system.database[roll_number]
+                save_embeddings_to_hf(roll_number, embedding)
+                st.success("Student added to database successfully!")
+            else:
+                st.error("Failed to add student to database.")
+
+            # Clean up temporary file
+            os.remove(img_path)
+        else:
+            st.error("Please enter a roll number and upload an image.")
+
+elif page == "View Attendance":
+    st.header("View Attendance")
+
+    # Course selection for viewing attendance
+    courses = [f.split('.')[0] for f in hf_api.list_repo_files(repo_id=HF_REPO_ID, repo_type=HF_REPO_TYPE) if f.endswith('.csv')]
+    selected_course = st.selectbox("Select Course to View Attendance", courses)
+
+    if selected_course:
+        # Read the CSV file from Hugging Face
+        df = load_csv_from_hf(selected_course)
+        
+        if df is not None:
+            # Display the attendance data
+            st.dataframe(df)
+
+            # Option to download the CSV
+            csv = df.to_csv(index=False)
+            st.download_button(
+                label="Download CSV",
+                data=csv,
+                file_name=f"{selected_course}_attendance.csv",
+                mime="text/csv",
+            )
+        else:
+            st.error("Failed to load attendance data.")