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import gradio as gr
import whisper
from transformers import pipeline
model = whisper.load_model("base")
sentiment_analysis = pipeline("sentiment-analysis", framework="pt", model="SamLowe/roberta-base-go_emotions")
def analyze_sentiment(text):
results = sentiment_analysis(text)
sentiment_results = {result['label']: result['score'] for result in results}
return sentiment_results
def get_sentiment_emoji(sentiment):
# Define the emojis corresponding to each sentiment
emoji_mapping = {
"disappointment": "😞",
"sadness": "😢",
"annoyance": "😠",
"neutral": "😐",
"disapproval": "👎",
"realization": "😮",
"nervousness": "😬",
"approval": "👍",
"joy": "😄",
"anger": "😡",
"embarrassment": "😳",
"caring": "🤗",
"remorse": "😔",
"disgust": "🤢",
"grief": "😥",
"confusion": "😕",
"relief": "😌",
"desire": "😍",
"admiration": "😌",
"optimism": "😊",
"fear": "😨",
"love": "❤️",
"excitement": "🎉",
"curiosity": "🤔",
"amusement": "😄",
"surprise": "😲",
"gratitude": "🙏",
"pride": "🦁"
}
return emoji_mapping.get(sentiment, "")
def display_sentiment_results(sentiment_results, option):
sentiment_text = ""
for sentiment, score in sentiment_results.items():
emoji = get_sentiment_emoji(sentiment)
if option == "Sentiment Only":
sentiment_text += f"{sentiment} {emoji}\n"
elif option == "Sentiment + Score":
sentiment_text += f"{sentiment} {emoji}: {score}\n"
return sentiment_text
def inference(audio, sentiment_option):
audio = whisper.load_audio(audio)
audio = whisper.pad_or_trim(audio)
mel = whisper.log_mel_spectrogram(audio).to(model.device)
_, probs = model.detect_language(mel)
lang = max(probs, key=probs.get)
options = whisper.DecodingOptions(fp16=False)
result = whisper.decode(model, mel, options)
sentiment_results = analyze_sentiment(result.text)
sentiment_output = display_sentiment_results(sentiment_results, sentiment_option)
return lang.upper(), result.text, sentiment_output
title = """<h1 align="center">🎤 Sentiment analysis for Voice Calls 💬</h1>"""
image_path = "genai.png"
description = """ This POC was developed for AI FINTECH HACKATHON @ BHARATPE"""
custom_css = """
#banner-image {
display: block;
margin-left: auto;
margin-right: auto;
}
#chat-message {
font-size: 14px;
min-height: 300px;
}
"""
block = gr.Blocks(css=custom_css)
with block:
gr.HTML(title)
with gr.Row():
with gr.Column():
gr.Image(image_path, elem_id="banner-image", show_label=False)
with gr.Column():
gr.HTML(description)
with gr.Group():
with gr.Group():
audio = gr.Audio(
label="Input Audio",
show_label=False,
type="filepath"
)
sentiment_option = gr.Radio(
choices=["Sentiment Only", "Sentiment + Score"],
label="Select an option",
# default="Sentiment Only"
)
btn = gr.Button("Transcribe")
lang_str = gr.Textbox(label="Language")
text = gr.Textbox(label="Transcription")
sentiment_output = gr.Textbox(label="Sentiment Analysis Results",
# output=True
)
btn.click(inference, inputs=[audio, sentiment_option], outputs=[lang_str, text, sentiment_output])
block.launch()
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