Update app.py

app.py

@@ -1,3 +1,65 @@
+import os
+os.system("pip install gradio==3.0.18")
+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) 
+
 demo = gr.Interface()
 
 with demo: