Upload app.py

app.py

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+import gradio as gr
+from gradio_client import Client
+import os
+from PIL import Image
+import io
+import base64
+
+hf_token = os.environ.get("HF_TKN")
+
+def convert_base64_to_img(image_string):
+    # Split the input string to separate the metadata header and the base64-encoded data
+    header, encoded_data = image_string.split(",", 1)
+    
+    # Now, encoded_data contains the base64-encoded image data
+    image_data = base64.b64decode(encoded_data)
+    
+    # Create a BytesIO object to store the image data
+    image_file = io.BytesIO(image_data)
+    
+    # Open the image using the BytesIO object
+    img = Image.open(image_file)
+    
+    # Save the image as a JPEG file
+    img.save('output.png', 'PNG')
+    
+    return "output.png"
+
+def infer(image_string, question):
+    image_in = convert_base64_to_img(image_string)
+    client = Client("https://fffiloni-moondream1.hf.space/", hf_token=hf_token)
+    result = client.predict(
+    		image_in,	# filepath  in 'image' Image component
+    		question,	# str  in 'Question' Textbox component
+    		api_name="/predict"
+    )
+    print(result)
+    return result
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column():
+            image_string = gr.Textbox(interactive=False)
+            question = gr.Textbox(interactive=False)
+            submit_btn = gr.Button("Submit", interactive=False)
+        with gr.Column():
+            answer = gr.Textbox(interactive=False)
+            
+    submit_btn.click(
+        fn=infer,
+        inputs=[image_string, question],
+        outputs=[answer]
+    )
+
+demo.launch()