Update modules/financial_analyst.py

modules/financial_analyst.py

@@ -1,105 +1,105 @@
-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')
+# 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 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
+# 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")
+# 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
+# ##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
+# ##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 
+# ##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    
+# ##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)
+# ##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) 
+# ##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.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)
+#             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()
+# if __name__ == "__main__":
+#     demo.launch()