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import gradio as gr |
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import requests |
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import pandas as pd |
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import ast |
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import boto3 |
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import re |
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import time |
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df= pd.DataFrame() |
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def _connect(): |
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''' |
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Creates and returns the s3 connection by passing the access key and secret key to boto3.client |
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''' |
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s3 = boto3.client("s3",aws_access_key_id='AKIA2BAL5ERM4EL4HRAE',aws_secret_access_key='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') |
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return s3 |
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def _upload(s3, tmp_filepath, s3_filepath, acl="public-read", content_type=None): |
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''' |
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This function uploads the files to specified bucket in s3 and returns the file url |
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''' |
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S3_LOCATION = 'https://{}.s3.ap-south-1.amazonaws.com/'.format('edwisely-academic-materials-v2') |
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_content_type = content_type |
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try: |
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with open(tmp_filepath, 'rb') as data: |
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s3.upload_fileobj(data, 'edwisely-academic-materials-v2', s3_filepath, |
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ExtraArgs={ |
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"ACL": acl, |
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"ContentType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet" |
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}) |
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return "{}{}".format(S3_LOCATION, s3_filepath) |
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except Exception as e: |
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print("Something Happened: ", e) |
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raise |
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def upload_file(tmp_filepath, faculty_name): |
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s3_obj = _connect() |
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s3_filepath = "{}/{}/{}.xlsx".format('q_gen', 'test',int(time.time())) |
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s3_uploaded_path = _upload(s3_obj, tmp_filepath, s3_filepath) |
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return s3_uploaded_path |
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def q_gen(topics_list, count, difficulty): |
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url = "https://d5cdtgvt04.execute-api.ap-south-1.amazonaws.com/test/q_gen" |
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topics_list = topics_list.split(",") |
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grand_topics, topics, sub_topics = "","","" |
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for i in topics_list: |
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if len(i.split("T")) == 2: |
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temp = i.strip() |
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if grand_topics != "": |
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grand_topics = grand_topics+","+temp |
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else: |
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grand_topics = i |
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elif len(i.split("T")) == 3: |
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temp = i.strip() |
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if topics != "": |
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topics = topics+","+temp |
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else: |
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topics = temp |
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elif len(i.split("T")) == 4: |
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temp = i.strip() |
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if sub_topics != "": |
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sub_topics = sub_topics+","+temp |
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else: |
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sub_topics = temp |
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params = { |
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"grand_topic_ids": grand_topics, |
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"topic_ids": topics, |
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"sub_topic_ids": sub_topics, |
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"count":count, |
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"difficulty":difficulty, |
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"test_id":1, |
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"faculty_id":1 |
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} |
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df = pd.DataFrame() |
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path = "" |
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try: |
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response = requests.get(url, params=params) |
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response.raise_for_status() |
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content = response.text |
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df = pd.DataFrame(ast.literal_eval(content)) |
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if df.columns[0] == "Not enough questions": |
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return "Not enough questions", df |
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options_df = pd.DataFrame(df['questions_options']) |
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columns_shape = pd.DataFrame(df['questions_options'].tolist()).shape[1] |
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for i in range(columns_shape): |
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options_df[f"option_{i+1}"] = pd.DataFrame(df['questions_options'].tolist())[i].apply(pd.Series)['name'] |
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df['com'] = range(df.shape[0]) |
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options_df['com'] = range(df.shape[0]) |
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df = pd.merge(df, options_df, on=['com']) |
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df = df[['id','name', 'media','blooms_level','type_name', 'type_code', 'option_1', 'option_2', 'option_3', 'option_4']] |
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df.to_excel('temp.xlsx') |
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file_path = "temp.xlsx" |
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path = upload_file(file_path, 'krushna') |
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except requests.exceptions.RequestException as e: |
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print("Error occurred:", e) |
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return path, df |
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demo = gr.Interface(fn=q_gen, inputs=[ |
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gr.Textbox(value="D04B01GT02,D12B190GT12, D13B10GT06T03,D13B10GT07T02, D12B190GT21T01ST02", label = "topics"), |
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gr.Textbox(value="10", label = "Count"), |
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gr.Textbox(value="8", label = "Difficulty")], outputs=["text","dataframe"]) |
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demo.launch() |
