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app.py

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+import streamlit as st
+import cv2
+from deepface import DeepFace
+import pandas as pd
+import os
+from datetime import datetime
+
+# Initialize face detector
+faceCascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+
+# Prepare CSV file
+csv_file = 'face_emotions.csv'
+csv_data = []
+
+# Define emotion to product mapping
+emotion_to_products = {
+  "happy": ["Joyful Juice", "Cheerful Chocolate", "Happy Hoodie"],
+  "sad": ["Comfort Blanket", "Warm Tea", "Inspirational Book"],
+  "angry": ["Stress Ball", "Calming Tea", "Meditation App"],
+  "surprised": ["Exciting Gadgets", "Adventure Gear", "Surprise Box"],
+  "neutral": ["Laptop- www.google.com ❤️‍🔥", "Healthy Snacks", "Relaxing Music"],
+  "fear": ["Safety Kit", "Comfort Food", "Stress Relief Kit"],
+  "disgust": ["Refreshing Drink", "Cleanser", "Aromatherapy Kit"]
+}
+
+def detect_emotion(frame):
+  emotion = "Unknown"
+  products = []
+  try:
+    rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+    result = DeepFace.analyze(rgb_frame, actions=['emotion'])
+    emotion = result[0]['dominant_emotion']  # Extract dominant emotion
+    products = emotion_to_products.get(emotion, ["No products available"])
+  except Exception as e:
+    st.error(f"Error analyzing frame: {e}")
+  return emotion, products
+
+def detect_faces_and_emotions():
+  # Create a directory to store images if it doesn't exist
+  if not os.path.exists('saved_faces'):
+    os.makedirs('saved_faces')
+
+  # Start video capture
+  cap = cv2.VideoCapture(0)
+  if not cap.isOpened():
+    st.error("Cannot open webcam!")
+    return
+
+  stframe = st.empty()
+  while True:
+    ret, frame = cap.read()
+    if not ret:
+      break
+
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    faces = faceCascade.detectMultiScale(gray, 1.1, 4)
+
+    emotion, products = detect_emotion(frame)
+
+    for (x, y, w, h) in faces:
+      cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
+
+    font = cv2.FONT_HERSHEY_SIMPLEX
+    cv2.putText(frame, emotion, (50, 50), font, 1, (0, 0, 255), 2, cv2.LINE_4)
+    stframe.image(frame, channels="BGR")
+
+    st.write(f"*Emotion Detected:* {emotion}")
+    st.write("*Recommended Products:*")
+    st.write(", ".join(products))
+
+    # Button for saving image with unique key
+    if st.button("Save Image", key=f"save_image_{datetime.now().strftime('%Y%m%d%H%M%S')}"):
+      timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+      face_filename = f'saved_faces/face_{timestamp}.jpg'
+      cv2.imwrite(face_filename, frame)
+
+      csv_data.append({
+          'Timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
+          'Emotion': emotion,
+          'File Path': face_filename
+      })
+      print(f"Image saved and data logged to CSV: {face_filename}")
+
+    if st.button("Quit", key="quit_app_button"):
+      break
+
+  cap.release()
+  cv2.destroyAllWindows()
+
+  # Save CSV file with emotion and product data
+  df = pd.DataFrame(csv_data)
+  df.to_csv(csv_file, index=False)
+  st.success(f"Data saved to {csv_file}")
+
+def display_csv_data():
+  if os.path.exists(csv_file):
+    # Check if the file is empty
+    if os.path.getsize(csv_file) > 0:
+      try:
+        df = pd.read_csv(csv_file)
+        st.write("### Logged Emotions and Product Recommendations")
+        st.dataframe(df)
+      except pd.errors.EmptyDataError:
+        st.write("The file is empty or cannot be read.")
+    else:
+      st.write("The file is empty.")
+  else:
+    st.write("No data available. Start detection to log emotions.")
+
+st.title("Welcome To NeuroSphere!!")
+st.write("This web app detects emotions from a live webcam feed and suggests products based on the detected emotion. To make your shopping experience happy and more reliable")
+
+if st.button("Start Detection", key="start_detection_button"):
+  detect_faces_and_emotions()
+

requirements.txt

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+streamlit
+numpy
+pandas
+opencv-python
+deepface
+tf-keras