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| import gradio as gr | |
| from transformers import AutoTokenizer, AutoModelWithLMHead | |
| import gc | |
| import os | |
| import pandas as pd | |
| HF_TOKEN = os.environ.get("HF_TOKEN") | |
| DATASET_NAME = "emotion_detection" | |
| DATASET_REPO_URL = f"https://huggingface.co/datasets/pragnakalp/{DATASET_NAME}" | |
| DATA_FILENAME = "emotion_detection_logs.csv" | |
| DATA_FILE = os.path.join("emotion_detection_logs", DATA_FILENAME) | |
| DATASET_REPO_ID = "pragnakalp/emotion_detection" | |
| print("is none?", HF_TOKEN is None) | |
| sentences_value = """Raj loves Simran.\nLast year I lost my Dog.\nI bought a new phone!\nShe is scared of cockroaches.\nWow! I was not expecting that.\nShe got mad at him.""" | |
| cwd = os.getcwd() | |
| model_path = os.path.join(cwd) | |
| tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-emotion") | |
| model_base = AutoModelWithLMHead.from_pretrained(model_path) | |
| def get_emotion(text): | |
| # input_ids = tokenizer.encode(text + '</s>', return_tensors='pt') | |
| input_ids = tokenizer.encode(text, return_tensors='pt') | |
| output = model_base.generate(input_ids=input_ids, | |
| max_length=2) | |
| dec = [tokenizer.decode(ids) for ids in output] | |
| label = dec[0] | |
| gc.collect() | |
| return label | |
| def generate_emotion(article): | |
| sen_list = article | |
| sen_list = sen_list.split('\n') | |
| sen_list_temp = sen_list[0:] | |
| print(sen_list_temp) | |
| results_dict = [] | |
| results = [] | |
| for sen in sen_list_temp: | |
| if(sen.strip()): | |
| cur_result = get_emotion(sen) | |
| results.append(cur_result) | |
| results_dict.append( | |
| { | |
| 'sentence': sen, | |
| 'emotion': cur_result | |
| } | |
| ) | |
| # result = { | |
| # 'result': results_dict, | |
| # } | |
| result = {'Input':sen_list_temp, 'Detected Emotion':results} | |
| gc.collect() | |
| add_csv = [results_dict] | |
| with open(DATA_FILE, "a") as f: | |
| writer = csv.writer(f) | |
| # write the data | |
| writer.writerow(add_csv) | |
| commit_url = repo.push_to_hub() | |
| print("commit data :",commit_url) | |
| return pd.DataFrame(result) | |
| """ | |
| Save generated details | |
| """ | |
| # def save_data_and_sendmail(article,generated_questions,num_que,result): | |
| # try: | |
| # hostname = {} | |
| # hostname = get_device_ip_address() | |
| # url = 'https://pragnakalpdev35.pythonanywhere.com/HF_space_que_gen' | |
| # # url = 'http://pragnakalpdev33.pythonanywhere.com/HF_space_question_generator' | |
| # myobj = {'article': article,'total_que': num_que,'gen_que':result,'ip_addr':hostname.get("ip_addr",""),'host':hostname.get("host","")} | |
| # x = requests.post(url, json = myobj) | |
| # add_csv = [article, generated_questions, num_que] | |
| # with open(DATA_FILE, "a") as f: | |
| # writer = csv.writer(f) | |
| # # write the data | |
| # writer.writerow(add_csv) | |
| # commit_url = repo.push_to_hub() | |
| # print("commit data :",commit_url) | |
| # # except Exception as e: | |
| # # return "Error while storing data -->" + e | |
| # # try: | |
| # # with open(DATA_FILE, "r") as file: | |
| # # data = json.load(file) | |
| # # data.append(entry) | |
| # # with open(DATA_FILE, "w") as file: | |
| # # json.dump(data, file) | |
| # # commit_url = repo.push_to_hub() | |
| # except Exception as e: | |
| # return "Error while sending mail" + e | |
| # return "Successfully save data" | |
| inputs = gr.Textbox(value=sentences_value,lines=10, label="Sentences",elem_id="inp_div") | |
| outputs = [gr.Dataframe(row_count = (2, "dynamic"), col_count=(2, "fixed"), label="Here is the Result", headers=["Input","Detected Emotion"])] | |
| demo = gr.Interface( | |
| generate_emotion, | |
| inputs, | |
| outputs, | |
| title="Emotion Detection", | |
| description="Feel free to give your feedback", | |
| css=".gradio-container {background-color: lightgray} #inp_div {background-color: #FB3D5;}" | |
| ) | |
| demo.launch() |