Create app.py

app.py

@@ -0,0 +1,73 @@
+import gradio as gr
+import boto3
+from botocore.exceptions import BotoCoreError, ClientError
+from PIL import Image
+import numpy as np
+import io
+
+aws_key_id = os.environ['aws_access_key_id']
+aws_secret = os.environ['aws_secret_access_key']
+
+
+
+
+
+# Initialize AWS Rekognition client
+try:
+    client = boto3.client('rekognition', 
+                      region_name='us-east-1',
+                      aws_access_key_id= aws_key_id, 
+                      aws_secret_access_key = aws_secret)
+except (BotoCoreError, ClientError) as error:
+    print('Error: ', error)
+
+
+def recognize_emotions(image):
+    """
+    This function takes an image as input, and returns the emotion with the highest confidence level in the face using AWS Rekognition
+    """
+    # Convert the NumPy array to PIL image
+    pil_image = Image.fromarray(np.uint8(image))
+    
+    # Convert the PIL image to bytes
+    with io.BytesIO() as output:
+        pil_image.save(output, format="JPEG")
+        contents = output.getvalue()
+    
+    # Perform detection on the image using AWS Rekognition
+    response = client.detect_faces(
+        Image={
+            'Bytes': contents
+        },
+        Attributes=['ALL']
+    )
+    
+    # If no faces are detected, return None
+    if not response['FaceDetails']:
+        return None
+    
+    # Extract the emotions detected in the face
+    emotions = response['FaceDetails'][0]['Emotions']
+    
+    # Find the emotion with the highest confidence level
+    max_confidence = 0
+    max_emotion = ''
+    for emotion in emotions:
+        if emotion['Confidence'] > max_confidence:
+            max_confidence = emotion['Confidence']
+            max_emotion = emotion['Type']
+    
+    # Return the emotion with the highest confidence level as a string
+    return str(max_emotion)
+
+
+
+# Create Gradio interface
+iface = gr.Interface(recognize_emotions, 
+                     inputs=gr.Image(source="webcam", streaming=True),
+                     outputs="text",
+                     title="How does this person feel?",
+                     description="Helping you understand what others think")
+
+# Launch the interface
+iface.launch()