Create app.py

app.py

@@ -0,0 +1,45 @@
+import gradio as gr
+import cv2
+import base64
+import requests
+
+def capture_video():
+        # Open the default camera
+    cap = cv2.VideoCapture(0)
+
+    # Loop through frames
+    while True:
+        # Read the current frame
+        ret, frame = cap.read()
+
+        # Encode the frame as a jpeg
+        _, buffer = cv2.imencode('.jpg', frame)
+
+        # Convert the buffer to a Base64 string
+        encoded_image = base64.b64encode(buffer).decode('utf-8')
+        # print("~~~~~~~OK~~~~~~~~~~")
+        # print(type(encoded_image))
+
+        # Do something with the encoded image, for example, send it to an API endpoint
+        # print(encoded_image)
+
+        response = requests.post("https://abidlabs-pytorch-image-classifier.hf.space/api/predict", json={
+            "data": [(encoded_image)
+                
+            ]
+        }).json()
+        print(response)
+
+        # Display the frame
+        cv2.imshow('frame', frame)
+
+        # Exit the loop if 'q' is pressed
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
+
+        # Release the capture and destroy the window
+        cap.release()
+        cv2.destroyAllWindows()
+
+iface = gr.Interface(fn=capture_video, inputs="webcam", outputs="text")
+iface.launch()