Update app.py

app.py

@@ -1,115 +1,53 @@
 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"]
+import numpy as np
+
+# Streamlit app title
+st.title("Face Emotion Detection App")
+
+# Upload an image or video
+uploaded_file = st.file_uploader("Upload an image or video", type=["jpg", "jpeg", "png", "mp4"])
+
+# Product suggestions based on emotions
+emotion_to_product = {
+    "happy": "Product A - Happiness Booster",
+    "sad": "Product B - Comfort Blanket",
+    "angry": "Product C - Stress Relief Ball",
+    "surprise": "Product D - Mystery Box",
+    "fear": "Product E - Confidence Potion",
+    "disgust": "Product F - Aroma Diffuser",
+    "neutral": "Product G - Everyday Essentials",
+    "Unknown": "Product H - General Item"
 }
 
-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
+if uploaded_file is not None:
+    # If the uploaded file is an image
+    if uploaded_file.type in ["image/jpeg", "image/png"]:
+        image = np.array(bytearray(uploaded_file.read()), dtype=np.uint8)
+        image = cv2.imdecode(image, cv2.IMREAD_COLOR)
+        
+        # Convert frame to RGB for DeepFace
+        rgb_frame = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
 
-  cap.release()
-  cv2.destroyAllWindows()
+        # Analyze emotions using DeepFace
+        result = DeepFace.analyze(rgb_frame, actions=['emotion'], enforce_detection=False)
+        emotion = result[0]['dominant_emotion']  # Extract dominant emotion
 
-  # 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}")
+        # Display the uploaded image
+        st.image(rgb_frame, channels="RGB")
 
-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.")
+        # Display the detected emotion
+        st.subheader(f"Detected Emotion: {emotion}")
 
-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")
+        # Display the suggested product
+        product = emotion_to_product.get(emotion, "Product H - General Item")
+        st.subheader(f"Recommended Product: {product}")
 
-if st.button("Start Detection", key="start_detection_button"):
-  detect_faces_and_emotions()
+    # If the uploaded file is a video
+    elif uploaded_file.type == "video/mp4":
+        st.video(uploaded_file)
+        # You can add code to analyze frames from the video if needed
 
+else:
+    st.info("Please upload an image or video file.")