Create app.py

app.py

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+import base64
+import io
+import cv2
+import requests
+import json
+import gradio as gr
+import os
+from PIL import Image
+import numpy as np
+from PIL import ImageOps
+
+# Accessing a specific environment variable
+api_key = os.environ.get('devisionx')
+
+# Checking if the environment variable exists
+if not api_key:
+    print("devisionx environment variable is not set.")
+    exit()
+
+# Define a function to call the API and get the results
+
+def base64str_to_PILImage(base64str):
+    base64_img_bytes = base64str.encode('utf-8')
+    base64bytes = base64.b64decode(base64_img_bytes)
+    bytesObj = io.BytesIO(base64bytes)
+    return ImageOps.exif_transpose(Image.open(bytesObj))
+
+def get_results(image, prompt):
+    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 (Adjust this part according to the API requirements)
+    payload = json.dumps({"base64str": base64str, "classes": prompt})
+
+    # Send the request to the API
+    response = requests.put(api_key, data=payload)
+
+    # Parse the JSON response
+    data = response.json()
+    print(response.status_code)
+    print(data)
+
+    # Access the values (Adjust this part according to the API response format)
+    output_image_base64 = data['firstName']  # Assuming the API returns the output image as base64
+    
+
+    # Convert the output image from base64 to PIL and then to NumPy array
+    output_image = base64str_to_PILImage(output_image_base64)
+    output_image = np.array(output_image)
+
+    return output_image
+
+# Define the input components for Gradio (adding a new input for the prompt)
+image_input = gr.inputs.Image()
+text_input = gr.inputs.Textbox(label="Prompt")  # New input for the text prompt
+
+# Define the output components for Gradio (including both image and text)
+outputs = gr.Image(type="numpy", label="Output Image")
+
+# Launch the Gradio interface
+gr.Interface(fn=get_results, inputs=[image_input, text_input], outputs=outputs).launch(share=False)