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import os |
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import csv |
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import json |
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import requests |
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import re as r |
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import gradio as gr |
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import pandas as pd |
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from transformers import pipeline |
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from huggingface_hub import Repository |
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from urllib.request import urlopen |
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HF_TOKEN = os.environ.get("HF_TOKEN") |
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DATASET_NAME = "huggingface_sentiment_analysis_dataset" |
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DATASET_REPO_URL = f"https://huggingface.co/datasets/pragnakalp/huggingface_sentiment_analysis_dataset" |
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DATA_FILENAME = "hf_sentiment_logs.csv" |
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DATA_FILE = os.path.join("hf_sentiment_logs", DATA_FILENAME) |
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DATASET_REPO_ID = "pragnakalp/huggingface_sentiment_analysis_dataset" |
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print("is none?", HF_TOKEN is None) |
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input_para = "I am happy\nI am sad\nI am not feeling well\nHe is a very good person\nHe is bad person\nI love pineapple\nI hate mangoes" |
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try: |
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hf_hub_download( |
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repo_id=DATASET_REPO_ID, |
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filename=DATA_FILENAME, |
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cache_dir=DATA_DIRNAME, |
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force_filename=DATA_FILENAME |
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) |
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except: |
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print("file not found") |
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repo = Repository( |
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local_dir="hf_sentiment_logs", clone_from=DATASET_REPO_URL, use_auth_token=HF_TOKEN |
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) |
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def getIP(): |
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d = str(urlopen('http://checkip.dyndns.com/') |
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.read()) |
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return r.compile(r'Address: (\d+\.\d+\.\d+\.\d+)').search(d).group(1) |
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def get_location(ip_addr): |
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ip=ip_addr |
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req_data={ |
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"ip":ip, |
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"token":"pkml123" |
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} |
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url = "https://demos.pragnakalp.com/get-ip-location" |
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headers = {'Content-Type': 'application/json'} |
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response = requests.request("POST", url, headers=headers, data=json.dumps(req_data)) |
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response = response.json() |
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print("response======>>",response) |
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return response |
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def huggingface_result_page(paragraph): |
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if paragraph.strip(): |
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model_base = pipeline('sentiment-analysis') |
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sen_list = paragraph |
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sen_list = sen_list.split('\n') |
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sen_list_temp = sen_list[0:] |
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results = [] |
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temp_result_dict = [] |
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for sen in sen_list_temp: |
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sen = sen.strip() |
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if sen: |
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cur_result = model_base(sen)[0] |
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temp_result_dict.append(sen) |
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results.append(cur_result['label']) |
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result = { |
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'Input': sen_list, 'Sentiment': results |
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} |
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print("LENGTH of results ====> ",str(len(results))) |
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print("LENGTH of sen_list ====> ",str(len(temp_result_dict))) |
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return pd.DataFrame(result) |
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else: |
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raise gr.Error("Please enter text in inputbox!!!!") |
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def save_data_and_sendmail(sen_list,results,result,paragraph): |
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try: |
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print("welcome") |
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ip_address = '' |
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ip_address= getIP() |
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print(ip_address) |
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location = get_location(ip_address) |
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print(location) |
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add_csv = [paragraph,result,ip_address,location] |
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with open(DATA_FILE, "a") as f: |
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writer = csv.writer(f) |
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writer.writerow(add_csv) |
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commit_url = repo.push_to_hub() |
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print("commit data :",commit_url) |
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url = 'https://pragnakalpdev33.pythonanywhere.com/HF_space_sentiment' |
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myobj = {'para': sen_list,'result':results,'ip_addr':ip_address,"location":location} |
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x = requests.post(url, json = myobj) |
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return "Successfully save data" |
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except Exception as e: |
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print("error") |
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return "Error while sending mail" + str(e) |
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inputs = gr.Textbox(lines=3, label="Paragraph",value=input_para) |
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outputs = gr.Dataframe(row_count = (3, "dynamic"), col_count=(2, "fixed"), label="Here is the Result", headers=["Input","Sentiment"],wrap=True) |
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demo = gr.Interface( |
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huggingface_result_page, |
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inputs, |
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outputs, |
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title="Huggingface Sentiment Analysis", |
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css=".gradio-container {background-color: lightgray}", |
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article = """<p style='text-align: center;'>Feel free to give us your <a href="https://www.pragnakalp.com/contact/" target="_blank">feedback</a> on this Question Generation using T5 demo.</p> |
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<p style='text-align: center;'>Developed by: <a href="https://www.pragnakalp.com" target="_blank">Pragnakalp Techlabs</a></p>""" |
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) |
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demo.launch() |
