adding sentence level changes

app.py

@@ -1,35 +1,54 @@
 from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
 import gradio as gr
 import os
+import spacy
+nlp = spacy.load('en_core_web_sm')
+
 auth_token = os.environ.get("HF_Token")
 
+##Speech Recognition
 asr = pipeline("automatic-speech-recognition", "facebook/wav2vec2-base-960h")
-summarizer = pipeline("summarization", model="knkarthick/MEETING_SUMMARY")
-tokenizer = AutoTokenizer.from_pretrained("demo-org/auditor_review_model",use_auth_token=auth_token)
-audit_model = AutoModelForSequenceClassification.from_pretrained("demo-org/auditor_review_model",use_auth_token=auth_token)
-nlp = pipeline("text-classification", model=audit_model, tokenizer=tokenizer)
-
 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):
     stext = summarizer(text)
     return stext
 
+##Fiscal Sentiment
+tokenizer = AutoTokenizer.from_pretrained("demo-org/auditor_review_model",use_auth_token=auth_token)
+audit_model = AutoModelForSequenceClassification.from_pretrained("demo-org/auditor_review_model",use_auth_token=auth_token)
+nlp = pipeline("text-classification", model=audit_model, tokenizer=tokenizer)
 def text_to_sentiment(text):
     sentiment = nlp(text)[0]["label"]
     return sentiment 
-    
+
+##Company Extraction    
 def ner(text):
     api = gr.Interface.load("dslim/bert-base-NER", src='models')
     spans = api(text)
     #replaced_spans = [(key, None) if value=='No Disease' else (key, value) for (key, value) in spans]
     return spans    
+
+##Fiscal Sentiment by Sentence
+def fin_ext(text):
+    doc = nlp(text)
+    doc_sents = [sent for sent in doc.sents]
+    sents_list = []
+    for sent in doc.sents:
+        sents_list.append(sent.text)
+    results_list=[]
+    for i in range(len(results)):
+        results_list.append(results[i]['label'])
+    fin_spans = list(zip(sents_list,results_list))
+    return fin_spans    
+
     
 demo = gr.Blocks()
 
@@ -44,7 +63,7 @@ with demo:
     stext = gr.Textbox()
     b2.click(summarize_text, inputs=text, outputs=stext)
     
-    b3 = gr.Button("Classify Sentiment")
+    b3 = gr.Button("Classify Overall Financial Sentiment")
     label = gr.Label()
     b3.click(text_to_sentiment, inputs=stext, outputs=label)
     
@@ -52,4 +71,9 @@ with demo:
     replaced_spans = gr.HighlightedText()
     b4.click(ner, inputs=text, outputs=replaced_spans)
     
+    b5 = gr.Button("Extract Financial Sentiment")
+    replaced_spans = gr.HighlightedText()
+    b5.click(fin_ext, inputs=text, outputs=fin_spans)
+    
+    
 demo.launch(share=True)