Update app.py

app.py

@@ -6,15 +6,29 @@ import torch
 from facenet_pytorch import MTCNN, InceptionResnetV1
 from keras.models import load_model
 from PIL import Image
-import pymongo
+import sqlite3
 import os
 import tempfile
 
-# MongoDB Atlas Connection String
-MONGO_URI = "mongodb+srv://test:test@cluster0.sxci1.mongodb.net/?retryWrites=true&w=majority"
-client = pymongo.MongoClient(MONGO_URI)
-db = client.get_database("emotion_detection")
-collection = db.get_collection("face_data")
+# SQLite Database Connection
+DB_NAME = "emotion_detection.db"
+
+# Initialize SQLite Database
+def initialize_database():
+    conn = sqlite3.connect(DB_NAME)
+    cursor = conn.cursor()
+    cursor.execute("""
+    CREATE TABLE IF NOT EXISTS face_data (
+        id INTEGER PRIMARY KEY AUTOINCREMENT,
+        name TEXT NOT NULL,
+        emotion TEXT NOT NULL,
+        timestamp TEXT NOT NULL
+    )
+    """)
+    conn.commit()
+    conn.close()
+
+initialize_database()
 
 # Page title
 st.markdown("<h1 style='text-align: center;'>Emotion Detection with Face Recognition</h1>", unsafe_allow_html=True)
@@ -97,16 +111,17 @@ def process_frame(frame):
 
             name = recognize_face(face_embedding)
 
-            # Save record in MongoDB
+            # Save record in SQLite
             if name != "Unknown":
                 timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-                document = {
-                    "name": name,
-                    "emotion": emotion,
-                    "timestamp": timestamp
-                }
-                if not collection.find_one({"name": name, "timestamp": timestamp}):
-                    collection.insert_one(document)
+                conn = sqlite3.connect(DB_NAME)
+                cursor = conn.cursor()
+                cursor.execute("""
+                INSERT INTO face_data (name, emotion, timestamp)
+                VALUES (?, ?, ?)
+                """, (name, emotion, timestamp))
+                conn.commit()
+                conn.close()
 
             # Display result
             cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
@@ -157,11 +172,16 @@ elif upload_choice == "Upload Video":
             video_source = cv2.VideoCapture(tfile.name)
             video_feed(video_source)
 
-# Display recent MongoDB records
+# Display recent SQLite records
 st.markdown("### Recent Records")
-records = collection.find().sort("timestamp", -1).limit(5)
+conn = sqlite3.connect(DB_NAME)
+cursor = conn.cursor()
+cursor.execute("SELECT name, emotion, timestamp FROM face_data ORDER BY timestamp DESC LIMIT 5")
+records = cursor.fetchall()
+conn.close()
+
 for record in records:
     col1, col2, col3 = st.columns(3)
-    col1.write(f"**Name**: {record['name']}")
-    col2.write(f"**Emotion**: {record['emotion']}")
-    col3.write(f"**Timestamp**: {record['timestamp']}")
+    col1.write(f"**Name**: {record[0]}")
+    col2.write(f"**Emotion**: {record[1]}")
+    col3.write(f"**Timestamp**: {record[2]}")