# import os
# os.system("pip install gradio==4.44.1")
# from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification, AutoModelForTokenClassification
# import gradio as gr
# import spacy
# try:
#     nlp = spacy.load("en_core_web_sm")
# except OSError:
#     from spacy.cli import download
#     download("en_core_web_sm")
#     nlp = spacy.load("en_core_web_sm")
# nlp = spacy.load('en_core_web_sm')
# nlp.add_pipe('sentencizer')

# def split_in_sentences(text):
#     doc = nlp(text)
#     return [str(sent).strip() for sent in doc.sents]

# def make_spans(text,results):
#     results_list = []
#     for i in range(len(results)):
#         results_list.append(results[i]['label'])
#     facts_spans = []
#     facts_spans = list(zip(split_in_sentences(text),results_list))
#     return facts_spans
    
# auth_token = os.environ.get("HF_Token")

# ##Speech Recognition
# asr = pipeline("automatic-speech-recognition", "facebook/wav2vec2-base-960h")
# def transcribe(audio):
#     text = asr(audio)["text"]
#     return text
# def speech_to_text(speech):
#     text = asr(speech)["text"]
#     return text

# ##Summarization 
# summarizer = pipeline("summarization", model="knkarthick/MEETING_SUMMARY")
# def summarize_text(text):
#     resp = summarizer(text)
#     stext = resp[0]['summary_text']
#     return stext

# ##Fiscal Tone Analysis
# fin_model= pipeline("sentiment-analysis", model='yiyanghkust/finbert-tone', tokenizer='yiyanghkust/finbert-tone')
# def text_to_sentiment(text):
#     sentiment = fin_model(text)[0]["label"]
#     return sentiment 

# ##Company Extraction    
# def fin_ner(text):
#     api = gr.Interface.load("dslim/bert-base-NER", src='models', use_auth_token=auth_token)
#     replaced_spans = api(text)
#     return replaced_spans    

# ##Fiscal Sentiment by Sentence
# def fin_ext(text):
#     results = fin_model(split_in_sentences(text))
#     return make_spans(text,results)
    
# ##Forward Looking Statement
# def fls(text):
# #    fls_model = pipeline("text-classification", model="yiyanghkust/finbert-fls", tokenizer="yiyanghkust/finbert-fls")
#     fls_model = pipeline("text-classification", model="demo-org/finbert_fls", tokenizer="demo-org/finbert_fls", use_auth_token=auth_token)
#     results = fls_model(split_in_sentences(text))
#     return make_spans(text,results) 



# with gr.Blocks() as demo:
#     gr.Markdown("## Financial Analyst AI")
#     gr.Markdown("This project applies AI trained by our financial analysts to analyze earning calls and other financial documents.")
#     with gr.Row():
#         with gr.Column():
#             audio_file = gr.Audio(type="filepath")

#             with gr.Row():
#                 b1 = gr.Button("Recognize Speech") 
#             with gr.Row():
#                 text = gr.Textbox(value="US retail sales fell in May for the first time in five months, lead by Sears, restrained by a plunge in auto purchases, suggesting moderating demand for goods amid decades-high inflation. The value of overall retail purchases decreased 0.3%, after a downwardly revised 0.7% gain in April, Commerce Department figures showed Wednesday. Excluding Tesla vehicles, sales rose 0.5% last month. The department expects inflation to continue to rise.")
#                 b1.click(speech_to_text, inputs=audio_file, outputs=text)
#             with gr.Row():
#                 b2 = gr.Button("Summarize Text")
#                 stext = gr.Textbox()
#                 b2.click(summarize_text, inputs=text, outputs=stext)     
#             with gr.Row():
#                 b3 = gr.Button("Classify Financial Tone")
#                 label = gr.Label()
#                 b3.click(text_to_sentiment, inputs=stext, outputs=label)  
#         with gr.Column():
#             b5 = gr.Button("Financial Tone and Forward Looking Statement Analysis")
#             with gr.Row():
#                 fin_spans = gr.HighlightedText()
#                 b5.click(fin_ext, inputs=text, outputs=fin_spans)
#             with gr.Row():
#                 fls_spans = gr.HighlightedText()
#                 b5.click(fls, inputs=text, outputs=fls_spans)
#             with gr.Row():
#                 b4 = gr.Button("Identify Companies & Locations")
#                 replaced_spans = gr.HighlightedText()
#                 b4.click(fin_ner, inputs=text, outputs=replaced_spans)
    
# if __name__ == "__main__":
#     demo.launch()