Update app.py

app.py

@@ -1,12 +1,35 @@
 import cv2
 import easyocr
 import gradio as gr
-import base64
+import numpy as np
+import requests
+
+API_URL = "https://api-inference.huggingface.co/models/dima806/facial_emotions_image_detection"
+headers = {"Authorization": "Bearer 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"}
 
 # Instance text detector
 reader = easyocr.Reader(['en'], gpu=False)
 
+
+def query(image):
+    image_data = np.array(image, dtype=np.uint8)
+
+    # Convert the image data to binary format (JPEG)
+    _, buffer = cv2.imencode('.jpg', image_data)
+
+    # Convert the binary data to bytes
+    binary_data = buffer.tobytes()
+
+    response = requests.post(API_URL, headers=headers, data=binary_data)
+    return response.json()
+
 def text_extraction(image):
+
+    # Facial Expression Detection
+    global text_content
+    text_content = ''
+    facial_data = query(image)
+
     text_ = reader.readtext(image)
 
     threshold = 0.25
@@ -14,19 +37,19 @@ def text_extraction(image):
     for t_, t in enumerate(text_):
         bbox, text, score = t
 
-        if score > threshold:
-            cv2.rectangle(image, tuple(map(int, bbox[0])), tuple(map(int, bbox[2])), (255, 0, 0), 2)
+        text_content = text_content + ' ' + ' '.join(text)
 
-    retval, buffer = cv2.imencode('.jpg', image)
-    img_base64 = base64.b64encode(buffer).decode('utf-8')
+        if score > threshold:
+            cv2.rectangle(image, tuple(map(int, bbox[0])), tuple(map(int, bbox[2])), (0, 255, 0), 5)
 
-    return img_base64
+    #output the image
+    return image, text_content, facial_data
 
 # Define Gradio interface
 iface = gr.Interface(
     fn=text_extraction,
     inputs=gr.Image(),
-    outputs=["image"]
+    outputs=[gr.Image(), gr.Textbox(label="Text Content"), gr.JSON(label="Facial Data")]
 )
 
 # Launch the Gradio interface