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app.ipynb
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},
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"cell_type": "code",
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"execution_count":
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"metadata": {},
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"outputs": [
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Running on local URL: http://127.0.0.1:
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"\n",
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"To create a public link, set `share=True` in `launch()`.\n"
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]
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{
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"data": {
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"text/html": [
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"<div><iframe src=\"http://127.0.0.1:
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"text/plain": [
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"<IPython.core.display.HTML object>"
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"data": {
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"text/plain": []
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"execution_count":
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"metadata": {},
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"output_type": "execute_result"
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"source": [
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"\n",
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" with gr.Column(scale=1.8):\n",
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" with gr.Row():\n",
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" ],\n",
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" [chatbot, state],\n",
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" )\n",
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"iface.launch()\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count":
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"metadata": {},
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"outputs": [],
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"source": [
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{
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"cell_type": "code",
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"execution_count": 36,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Running on local URL: http://127.0.0.1:7871\n",
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"\n",
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"To create a public link, set `share=True` in `launch()`.\n"
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]
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{
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"data": {
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"text/html": [
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"<div><iframe src=\"http://127.0.0.1:7871/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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"data": {
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"text/plain": []
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},
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"execution_count": 36,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Traceback (most recent call last):\n",
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" File \"/Users/lewtun/miniconda3/envs/hf/lib/python3.8/site-packages/gradio/routes.py\", line 337, in run_predict\n",
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" output = await app.get_blocks().process_api(\n",
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" File \"/Users/lewtun/miniconda3/envs/hf/lib/python3.8/site-packages/gradio/blocks.py\", line 1018, in process_api\n",
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" data = self.postprocess_data(fn_index, result[\"prediction\"], state)\n",
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" File \"/Users/lewtun/miniconda3/envs/hf/lib/python3.8/site-packages/gradio/blocks.py\", line 924, in postprocess_data\n",
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" predictions = convert_component_dict_to_list(\n",
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" File \"/Users/lewtun/miniconda3/envs/hf/lib/python3.8/site-packages/gradio/blocks.py\", line 397, in convert_component_dict_to_list\n",
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" raise ValueError(\n",
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"ValueError: Returned component chatbot not specified as output of function.\n"
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]
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}
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],
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"source": [
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"\n",
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" with gr.Column(scale=1.8):\n",
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" with gr.Row():\n",
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" chatbot = gr.Chatbot(\n",
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" label=\"Chat Output\",\n",
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" )\n",
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"\n",
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" with gr.Row():\n",
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" chat_input = gr.Textbox(lines=1, label=\"Chat Input\")\n",
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" chat_input.submit(\n",
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" inference_chat,\n",
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" [\n",
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" model_id,\n",
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" prompt_template,\n",
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" chat_input,\n",
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" temperature,\n",
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" top_p,\n",
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" state,\n",
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" ],\n",
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" [chatbot, state],\n",
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" )\n",
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"\n",
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" with gr.Row():\n",
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" clear_button = gr.Button(value=\"Clear\", interactive=True)\n",
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" clear_button.click(\n",
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" lambda: (\"\", [], []),\n",
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" [],\n",
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" [chat_input, chatbot, state],\n",
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" queue=False,\n",
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" )\n",
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"\n",
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" submit_button = gr.Button(\n",
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" value=\"Submit\", interactive=True, variant=\"primary\"\n",
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" )\n",
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" submit_button.click(\n",
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" inference_chat,\n",
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" [\n",
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" model_id,\n",
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" prompt_template,\n",
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" chat_input,\n",
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" temperature,\n",
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" top_p,\n",
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" state,\n",
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" ],\n",
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" [chatbot, state],\n",
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" )\n",
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"iface.launch()\n"
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]
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},
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},
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{
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"cell_type": "code",
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"execution_count": 37,
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"metadata": {},
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"outputs": [],
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"source": [
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app.py
CHANGED
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top_p = gr.Slider(
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minimum=-0,
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maximum=1.0,
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value=0.
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step=0.05,
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interactive=True,
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label="Top-p (nucleus sampling)",
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with gr.Column(scale=1.8):
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with gr.Row():
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],
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[chatbot, state],
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)
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iface.launch()
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top_p = gr.Slider(
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minimum=-0,
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maximum=1.0,
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value=0.8,
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step=0.05,
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interactive=True,
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label="Top-p (nucleus sampling)",
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with gr.Column(scale=1.8):
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with gr.Row():
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chatbot = gr.Chatbot(
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label="Chat Output",
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)
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with gr.Row():
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chat_input = gr.Textbox(lines=1, label="Chat Input")
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chat_input.submit(
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inference_chat,
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[
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model_id,
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prompt_template,
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chat_input,
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temperature,
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top_p,
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state,
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],
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[chatbot, state],
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)
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with gr.Row():
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clear_button = gr.Button(value="Clear", interactive=True)
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clear_button.click(
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lambda: ("", [], []),
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[],
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[chat_input, chatbot, state],
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queue=False,
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)
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submit_button = gr.Button(
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value="Submit", interactive=True, variant="primary"
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)
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submit_button.click(
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inference_chat,
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[
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model_id,
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prompt_template,
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chat_input,
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temperature,
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top_p,
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state,
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],
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[chatbot, state],
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)
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iface.launch()
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