Create app.py

app.py

@@ -0,0 +1,57 @@
+import base64
+import io
+import cv2
+import requests
+import json
+import gradio as gr
+import os
+from PIL import Image
+
+# Accessing a specific environment variable
+api_key = os.environ.get('PXiVision')
+
+# Checking if the environment variable exists
+if not api_key:
+    print("PXiVision environment variable is not set.")
+    exit()
+
+# Define a function to call the API and get the results
+def get_results(image):
+    threshold = 0.5
+    
+    # Convert the NumPy array to PIL image
+    image = Image.fromarray(image)
+
+    # Convert the image to base64 string
+    with io.BytesIO() as output:
+        image.save(output, format="JPEG")
+        base64str = base64.b64encode(output.getvalue()).decode("utf-8")
+
+    # Prepare the payload
+    payload = json.dumps({"base64str": base64str, "UID": "huggingfacetestsayed"})
+
+    # Send the request to the API
+    response = requests.put(api_key, data=payload)
+
+    # Parse the JSON response
+    data = response.json()
+    # data = json.loads(data)
+
+
+    # Access the values
+    num = data['num']
+    char = data['char']
+    timeOfResponse = data['time']
+    requestInfo = data['info']
+    return [num, char, timeOfResponse, requestInfo]
+
+# Define the input component for Gradio
+image_input = gr.inputs.Image()  # Adjust the shape according to your requirements
+
+# Define the output components for Gradio
+output_components = []
+for label in ["Numbers", "char", "Time of Response", "Request Info"]:
+    output_components.append(gr.outputs.Textbox(label=label))
+
+# Launch the Gradio interface
+gr.Interface(fn=get_results,examples=["test.jpg"], inputs=image_input, outputs=output_components).launch(share=False)