Update app.py

app.py

@@ -1,97 +1,42 @@
-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.Blocks()
+demo = gr.Interface()
 
 with 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.")
+    
+    # Row 1: Speech Recognition and Text Summarization
     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)  
+            b1 = gr.Button("Recognize Speech")
+            text = gr.Textbox(value="")  # Textbox for speech-to-text output
+            
         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)
+            b2 = gr.Button("Summarize Text")
+            stext = gr.Textbox()  # Textbox for summarized text output
     
-demo.launch()
+    # Row 2: Financial Tone Analysis
+    with gr.Row():
+        b3 = gr.Button("Classify Financial Tone")
+        label = gr.Label()  # Label for sentiment analysis output
+        
+    # Row 3: Financial Tone and Forward Looking Statement Analysis
+    with gr.Row():
+        b5 = gr.Button("Financial Tone and Forward Looking Statement Analysis")
+        fin_spans = gr.HighlightedText()  # HighlightedText for financial sentiment spans
+        fls_spans = gr.HighlightedText()  # HighlightedText for forward looking statement spans
+        
+    # Row 4: Identify Companies & Locations
+    with gr.Row():
+        b4 = gr.Button("Identify Companies & Locations")
+        replaced_spans = gr.HighlightedText()  # HighlightedText for named entity recognition spans
+
+# Define the click handlers for the buttons
+b1.click(speech_to_text, inputs=audio_file, outputs=text)
+b2.click(summarize_text, inputs=text, outputs=stext)
+b3.click(text_to_sentiment, inputs=stext, outputs=label)
+b5.click(fin_ext, inputs=text, outputs=fin_spans)
+b5.click(fls, inputs=text, outputs=fls_spans)
+b4.click(fin_ner, inputs=text, outputs=replaced_spans)
+
+demo.launch()