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Upload app.py
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app.py
CHANGED
@@ -4,14 +4,15 @@ import os
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import shutil
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import time
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import traceback
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import gradio as gr
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import pandas as pd
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from dotenv import load_dotenv
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from langchain.chat_models import ChatOpenAI
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from langchain.embeddings import OpenAIEmbeddings
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import utils
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from csv_agent import CSVAgent
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from grader import Grader
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from grader_qa import GraderQA
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@@ -107,13 +108,21 @@ def start_grading(history):
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return history, disabled, enabled, enabled, enabled
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def start_downloading():
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#
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#
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#
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#
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# else:
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# return "File not found"
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print(grader.csv)
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return grader.csv, gr.update(visible=True), gr.update(value=process_csv_text(), visible=True)
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@@ -122,6 +131,8 @@ def get_headers():
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return list(df.columns)
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def get_first_message(history):
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global grader_qa
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history = [(None,
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@@ -179,27 +190,27 @@ def enable_fields(url_status, canvas_api_key_status, submit_status, grade_status
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def reset_data():
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# Use shutil.rmtree() to delete output, docs, and vector_stores folders, reset grader and grader_qa, and get_grading_status, reset and return history
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global grader, grader_qa
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#If there's data in docs/output folder during grading [During Grading]
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if os.path.isdir('output') and len(glob.glob("output/*.csv")) > 0
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glob.glob("docs/*.html")) > 0:
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reset_folder('output')
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reset_folder('docs')
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grader = None
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grader_qa = None
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history = [(None, 'Data reset successfully')]
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history = [(None, 'Data reset successfully')]
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return history, enabled, enabled, enabled, disabled, disabled, disabled
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#If there's data in vector_stores folder
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elif len(glob.glob("vector_stores/*.faiss")) > 0 or len(glob.glob("vector_stores/*.pkl")) > 0:
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reset_folder('vector_stores')
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history = [(None, 'Data reset successfully')]
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return history, enabled, enabled, enabled, disabled, disabled, disabled
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def get_output_dir(orig_name):
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script_dir = os.path.dirname(os.path.abspath(__file__))
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output_dir = os.path.join(script_dir, 'output', orig_name)
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@@ -227,13 +238,16 @@ def upload_grading_results(file, history):
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def bot(history):
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return history
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def process_csv_text():
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file_path = utils.get_csv_file_name()
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df = pd.read_csv(file_path)
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return df
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with gr.Blocks() as demo:
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gr.Markdown(f"<h2><center>{'Canvas Discussion Grading With Feedback'}</center></h2>")
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with gr.Row():
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url = gr.Textbox(
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@@ -245,33 +259,34 @@ with gr.Blocks() as demo:
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label="Canvas API Key",
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placeholder="Enter your Canvas API Key", type="password"
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)
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submit = gr.Button(value="Step 1: Submit", variant="secondary", )
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with gr.Row():
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table = gr.Dataframe(label ='Canvas CSV Output', type="pandas", overflow_row_behaviour="paginate", visible = False, wrap=True)
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with gr.Row():
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file = gr.components.File(label="CSV Output", container=False, visible=False).style(height=100)
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reset = gr.ClearButton(value="Reset")
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chatbot = gr.Chatbot([], label="Chat with grading results", elem_id="chatbot", height=400)
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with gr.Row():
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with gr.Column(scale=3):
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txt = gr.Textbox(
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label="Ask questions about how students did on the discussion",
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placeholder="Enter text and press enter, or upload an image", lines=1
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)
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upload = gr.UploadButton(label="Upload grading results", type="file", file_types=["csv"], scale=
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chatbot.value = get_first_message([])
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with gr.Row():
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table = gr.Dataframe(label ='Canvas CSV Output', type="pandas", overflow_row_behaviour="paginate", visible = False, wrap=True)
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submit.click(ingest, inputs=[url, canvas_api_key, chatbot], outputs=[chatbot, url, canvas_api_key, submit, grade],
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postprocess=False).then(
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bot, chatbot, chatbot
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@@ -285,7 +300,8 @@ with gr.Blocks() as demo:
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download.click(start_downloading, inputs=[], outputs=[file, file, table]).then(
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bot, chatbot, chatbot
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)
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txt.submit(add_text, [chatbot, txt], [chatbot, txt], postprocess=False).then(
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bot, chatbot, chatbot
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)
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@@ -294,7 +310,7 @@ with gr.Blocks() as demo:
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bot, chatbot, chatbot
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)
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reset.click(reset_data, inputs=[], outputs=[chatbot, url, canvas_api_key, submit, table, grade, download]).success(
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bot, chatbot, chatbot)
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upload.upload(upload_grading_results, inputs=[upload, chatbot], outputs=[chatbot], postprocess=False, ).then(
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import shutil
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import time
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import traceback
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import pandas as pd
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import utils
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from zipfile import ZipFile
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import gradio as gr
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from dotenv import load_dotenv
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from langchain.chat_models import ChatOpenAI
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from langchain.embeddings import OpenAIEmbeddings
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from csv_agent import CSVAgent
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from grader import Grader
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from grader_qa import GraderQA
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return history, disabled, enabled, enabled, enabled
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# def start_downloading(history):
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# files = glob.glob("output/*.csv")
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# if files:
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# file = files[0]
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# download.interactive = True
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# return gr.components.File(file)
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# else:
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# return " No file found"
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def start_downloading():
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# with ZipFile("output/*.csv", "w") as zipObj:
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# for idx, file in enumerate(files):
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# zipObj.write(file.name, file.name.split("/")[-1])
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# return "outputfiles.zip"
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print(grader.csv)
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return grader.csv, gr.update(visible=True), gr.update(value=process_csv_text(), visible=True)
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return list(df.columns)
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#file_input = glob.glob("output/*.csv") #[gr.File(file_count="multiple", file_types=["text", ".json", ".csv"])]
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def get_first_message(history):
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global grader_qa
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history = [(None,
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def reset_data():
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# Use shutil.rmtree() to delete output, docs, and vector_stores folders, reset grader and grader_qa, and get_grading_status, reset and return history
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global grader, grader_qa
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# reset_folder('output')
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# reset_folder('docs')
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# reset_folder('vector_stores')
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# grader = None
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# grader_qa = None
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# history = [(None, 'Data reset successfully')]
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# return history
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#disha
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#If there's data in docs/output folder during grading [During Grading]
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if os.path.isdir('output') and len(glob.glob("output/*.csv")) > 0 or len(glob.glob("docs/*.json")) > 0 or len(
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glob.glob("docs/*.html")) > 0 or len(glob.glob("vector_stores/*.faiss")) > 0 or len(glob.glob("vector_stores/*.pkl")) > 0:
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reset_folder('output')
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reset_folder('docs')
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reset_folder('vector_stores')
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grader = None
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grader_qa = None
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history = [(None, 'Data reset successfully')]
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url.placeholder = [(None, 'Enter your Canvas Discussion URL')]
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canvas_api_key.placeholder = [(None, 'Enter your Canvas API Key')]
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return history, enabled, enabled, enabled, disabled, disabled, disabled, disabled, disabled, disabled, disabled, disabled
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def get_output_dir(orig_name):
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script_dir = os.path.dirname(os.path.abspath(__file__))
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output_dir = os.path.join(script_dir, 'output', orig_name)
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def bot(history):
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return history
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def process_csv_text():
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file_path = utils.get_csv_file_name()
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df = pd.read_csv(file_path)
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return df
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with gr.Blocks() as demo:
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gr.Markdown(f"<h2><center>{'Canvas Discussion Grading With Feedback'}</center></h2>")
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with gr.Row():
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url = gr.Textbox(
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label="Canvas API Key",
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placeholder="Enter your Canvas API Key", type="password"
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)
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submit = gr.Button(value="Submit", variant="secondary", )
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with gr.Row():
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table = gr.Dataframe(label ='Canvas CSV Output', type="pandas", overflow_row_behaviour="paginate", visible = False, wrap=True)
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with gr.Row(equal_height=True):
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grade = gr.Button(value="Grade", variant="secondary")
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download = gr.Button(value="Generate Output", variant="secondary")
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file = gr.components.File(label="CSV Output", container=False, visible=False).style(height=100)
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#reset = gr.ClearButton(value="Reset", components=[url, canvas_api_key, submit, table, grade, download])
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reset = gr.ClearButton(value="Reset")
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chatbot = gr.Chatbot([], label="Chat with grading results", elem_id="chatbot", height=400)
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with gr.Row():
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with gr.Column(scale=3):
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txt = gr.Textbox(
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label="Ask questions about how students did on the discussion",
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placeholder="Enter text and press enter, or upload an image", lines=1
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)
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upload = gr.UploadButton(label="Upload grading results", type="file", file_types=["csv"], scale=1)
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ask = gr.Button(value="Ask", variant="secondary", scale=1)
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chatbot.value = get_first_message([])
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with gr.Row():
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table = gr.Dataframe(label ='Canvas CSV Output', type="pandas", overflow_row_behaviour="paginate", visible = False, wrap=True)
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submit.click(ingest, inputs=[url, canvas_api_key, chatbot], outputs=[chatbot, url, canvas_api_key, submit, grade],
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postprocess=False).then(
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bot, chatbot, chatbot
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download.click(start_downloading, inputs=[], outputs=[file, file, table]).then(
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bot, chatbot, chatbot
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)
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txt.submit(add_text, [chatbot, txt], [chatbot, txt], postprocess=False).then(
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bot, chatbot, chatbot
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)
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bot, chatbot, chatbot
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)
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reset.click(reset_data, inputs=[], outputs=[chatbot, url, canvas_api_key, submit, table, grade, download, txt, file, upload, ask]).success(
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bot, chatbot, chatbot)
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upload.upload(upload_grading_results, inputs=[upload, chatbot], outputs=[chatbot], postprocess=False, ).then(
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