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Update app.py
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app.py
CHANGED
@@ -1,13 +1,9 @@
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import gradio as gr
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import os
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import json
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import base64
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import copy
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import time
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from PIL import Image
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from io import BytesIO
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from specklepy.api.client import SpeckleClient
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from specklepy.api.credentials import get_default_account
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from specklepy.transports.server import ServerTransport
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from specklepy.api import operations
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from specklepy.objects import Base
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@@ -15,16 +11,36 @@ from specklepy.objects import Base
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# Initialize Speckle client with your token
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speckle_token = os.getenv('SPECKLE_TOKEN')
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speckle_branch = os.getenv('SPECKLE_BRANCH')
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CLIENT = SpeckleClient(host="https://speckle.xyz/")
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CLIENT.authenticate_with_token(token=speckle_token)
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def process_data(image, text):
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# Process image: Resize and convert to base64
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img_str = ""
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if image is not None:
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image = Image.open(image)
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# Resize image, maintaining aspect ratio, and max width 1024 pixels
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base_width = 1024
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w_percent = (base_width / float(image.size[0]))
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@@ -36,49 +52,22 @@ def process_data(image, text):
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image.save(buffered, format="JPEG")
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img_str = base64.b64encode(buffered.getvalue()).decode('utf-8')
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# Prepare
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obj = Base()
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obj["image"] = img_str
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obj["text"] = text
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# Send data to Speckle
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send_to_speckle(data)
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# For now, we'll just return the JSON to display it
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return json.dumps(data)
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def updateSpeckleStream(stream_id,
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branch_name,
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client,
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data_object,
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commit_message="Updated the data object",
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):
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# set stream and branch
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branch = client.branch.get(stream_id, branch_name)
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# Get transport
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transport = ServerTransport(client=client, stream_id=stream_id)
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# Send the data object to the speckle stream
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object_id = operations.send(data_object, [transport])
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# Create a new commit with the new object
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commit_id = client.commit.create(
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stream_id,
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object_id= object_id,
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message=commit_message,
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branch_name=branch_name,
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)
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return commit_id
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with gr.Blocks() as demo:
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gr.Markdown("### Upload Image and
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with gr.Row():
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image = gr.Image(type="
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submit_btn = gr.Button("Submit")
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output = gr.Textbox(label="
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submit_btn.click(fn=process_data, inputs=[image,
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demo.launch()
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import gradio as gr
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import os
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import base64
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from PIL import Image
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from io import BytesIO
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from specklepy.api.client import SpeckleClient
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from specklepy.transports.server import ServerTransport
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from specklepy.api import operations
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from specklepy.objects import Base
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# Initialize Speckle client with your token
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speckle_token = os.getenv('SPECKLE_TOKEN')
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speckle_branch = os.getenv('SPECKLE_BRANCH')
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speckle_stream_id = os.getenv('SPECKLE_STREAM_ID')
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CLIENT = SpeckleClient(host="https://speckle.xyz/")
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CLIENT.authenticate_with_token(token=speckle_token)
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def send_to_speckle(stream_id, branch_name, client, data_object):
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try:
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# Get transport
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transport = ServerTransport(client=client, stream_id=stream_id)
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# Send the data object to the speckle stream
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object_id = operations.send(data_object, [transport])
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# Create a new commit with the new object
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commit_id = client.commit.create(
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stream_id=stream_id,
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object_id=object_id,
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branch_name=branch_name,
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message="Updated the data object from Gradio app",
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)
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return f"Success! Data sent to Speckle stream. Commit ID: {commit_id}"
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except Exception as e:
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return f"An error occurred: {e}"
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def process_data(image, text):
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# Process image: Resize and convert to base64
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img_str = ""
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if image is not None:
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image = Image.open(BytesIO(image))
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# Resize image, maintaining aspect ratio, and max width 1024 pixels
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base_width = 1024
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w_percent = (base_width / float(image.size[0]))
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image.save(buffered, format="JPEG")
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img_str = base64.b64encode(buffered.getvalue()).decode('utf-8')
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# Prepare data object
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obj = Base()
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obj["image"] = img_str
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obj["text"] = text
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# Send data to Speckle
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return send_to_speckle(speckle_stream_id, speckle_branch, CLIENT, obj)
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with gr.Blocks() as demo:
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gr.Markdown("### Upload Image and Enter Text")
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with gr.Row():
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image = gr.Image(type="pil", label="Add Image")
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text = gr.TextArea(label="Enter Text or Speak")
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submit_btn = gr.Button("Submit")
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output = gr.Textbox(label="Status", visible=False) # Set visible to False to hide output
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submit_btn.click(fn=process_data, inputs=[image, text], outputs=output)
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demo.launch()
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