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import gradio as gr
import whisper
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import uuid
import csv
import os
# Load models
whisper_model = whisper.load_model("base")
tokenizer = AutoTokenizer.from_pretrained("yiyanghkust/finbert-tone")
model = AutoModelForSequenceClassification.from_pretrained("yiyanghkust/finbert-tone")
labels = ["Positive", "Negative", "Neutral"]
# Analysis Function
def analyze_sentiment_auto(audio_path, client_type):
transcription_result = whisper_model.transcribe(audio_path, task="translate")
transcript = transcription_result["text"].strip()
inputs = tokenizer(transcript, return_tensors="pt", truncation=True)
with torch.no_grad():
logits = model(**inputs).logits
prediction = torch.argmax(logits, dim=1).item()
sentiment = labels[prediction]
client_id = str(uuid.uuid4())[:8]
result = {
"client_id": client_id,
"client_type": client_type,
"sentiment": sentiment,
"transcript": transcript
}
# Save to CSV
file_exists = os.path.isfile("sentiment_results.csv")
with open("sentiment_results.csv", mode='a', newline='', encoding='utf-8') as csv_file:
fieldnames = ["client_id", "client_type", "sentiment", "transcript"]
writer = csv.DictWriter(csv_file, fieldnames=fieldnames)
if not file_exists:
writer.writeheader()
writer.writerow(result)
return (
transcript,
f"🆔 {client_id}",
f"📋 {client_type}",
f"{sentiment}"
)
# Color map for sentiment
def sentiment_color(sentiment):
return {
"Positive": "#22c55e", # Green
"Negative": "#ef4444", # Red
"Neutral": "#facc15" # Yellow
}.get(sentiment, "#e5e7eb")
# Gradio UI
with gr.Blocks(theme=gr.themes.Soft()) as demo:
gr.Markdown("""
<h1 style='text-align: center; color: #1d4ed8;'>🎙️ Multilingual Voice Sentiment Analyzer</h1>
<p style='text-align: center; font-size: 16px;'>
Upload a voice note in any language. It will be auto-translated to English and analyzed using FinBERT.
</p>
""")
with gr.Row():
audio_input = gr.Audio(sources=["upload"], type="filepath", label="🔊 Upload Voice File")
client_type = gr.Dropdown(choices=["New", "Renewal", "Support"], value="New", label="👤 Client Type")
submit_btn = gr.Button("🚀 Analyze Sentiment", variant="primary")
with gr.Row():
transcript_output = gr.Textbox(label="📝 English Transcript", lines=4, interactive=False)
client_id_output = gr.Textbox(label="🆔 Client ID", interactive=False)
client_type_output = gr.Textbox(label="👤 Client Type", interactive=False)
sentiment_output = gr.Textbox(label="📊 Sentiment", interactive=False)
def process(audio, client_type):
transcript, client_id, ctype, sentiment = analyze_sentiment_auto(audio, client_type)
return (
gr.update(value=transcript),
gr.update(value=client_id),
gr.update(value=ctype),
gr.update(value=sentiment, label=f"📊 Sentiment: {sentiment}", elem_id="sentiment-output")
)
submit_btn.click(
fn=process,
inputs=[audio_input, client_type],
outputs=[transcript_output, client_id_output, client_type_output, sentiment_output]
)
gr.Markdown("""
<style>
#sentiment-output textarea {
font-weight: bold;
text-align: center;
font-size: 1.2em;
color: white;
background-color: #1f2937;
}
</style>
""")
demo.launch()
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