Update financial_analyst.py

financial_analyst.py

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+import os
+from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification, AutoModelForTokenClassification
+import gradio as gr
+import spacy
+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) 
+
+def create_financial_tab():
+    with gr.Row():
+        with gr.Column():
+            audio_file = gr.inputs.Audio(source="microphone", 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)