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app.py

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+import streamlit as st
+import cv2
+import torch
+from PIL import Image
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+from io import BytesIO
+import numpy as np
+
+# Load processor and model
+processor = AutoImageProcessor.from_pretrained("RickyIG/emotion_face_image_classification")
+model = AutoModelForImageClassification.from_pretrained("RickyIG/emotion_face_image_classification")
+
+# Title of the Streamlit app
+st.title("Emotion Detection App")
+
+# Option to choose between uploading image or using live camera
+option = st.radio("Select an option", ("Upload Image", "Use Live Camera"))
+
+if option == "Upload Image":
+    # Upload image
+    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+    
+    if uploaded_file is not None:
+        # Display the uploaded image
+        image = Image.open(uploaded_file)
+        st.image(image, caption="Uploaded Image", use_column_width=True)
+
+        # Preprocess the image
+        inputs = processor(images=image, return_tensors="pt")
+        
+        # Make predictions
+        with torch.no_grad():
+            outputs = model(**inputs)
+        logits = outputs.logits  # raw model outputs (before softmax)
+        predicted_class_idx = logits.argmax(-1).item()  # predicted class index
+        
+        # Get the label of the predicted class
+        label = model.config.id2label[predicted_class_idx]
+
+        # Display the result
+        st.write(f"Predicted Emotion: {label}")
+
+elif option == "Use Live Camera":
+    # Use OpenCV to capture video from the front camera
+    cap = cv2.VideoCapture(0)
+
+    if not cap.isOpened():
+        st.error("Error: Could not open webcam.")
+    else:
+        stframe = st.empty()  # Placeholder to display live camera feed
+        
+        while True:
+            # Capture frame-by-frame
+            ret, frame = cap.read()
+            
+            if not ret:
+                st.error("Error: Failed to capture frame.")
+                break
+            
+            # Convert frame (BGR) to RGB (PIL format)
+            image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+            
+            # Preprocess the image
+            inputs = processor(images=image, return_tensors="pt")
+            
+            # Make predictions
+            with torch.no_grad():
+                outputs = model(**inputs)
+            logits = outputs.logits  # raw model outputs (before softmax)
+            predicted_class_idx = logits.argmax(-1).item()  # predicted class index
+            
+            # Get the label of the predicted class
+            label = model.config.id2label[predicted_class_idx]
+
+            # Display the result
+            cv2.putText(frame, f"Emotion: {label}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2, cv2.LINE_AA)
+            
+            # Convert the frame to RGB for Streamlit
+            frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            stframe.image(frame_rgb, channels="RGB", use_column_width=True)
+
+    # Release the capture when finished
+    cap.release()

requirements.txt

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+streamlit==1.41.1
+opencv-python==4.10.0.84
+torch==2.5.1
+transformers==4.33.2
+Pillow==11.0.0
+torch==2.5.1
+tokenizers==0.13.3