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import gradio as gr
import openai
from langdetect import detect
from transformers import pipeline
from keybert import KeyBERT
import os
# --- SETUP ---
openai.api_key = os.getenv("OPENAI_API_KEY") # Set in HF Space Secrets
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
kw_model = KeyBERT()
# Key Indian brokerages, investment apps, and fintech brands
BRANDS = [
"Zerodha", "Upstox", "Groww", "Angel One", "Motilal Oswal", "Sharekhan", "5paisa", "ICICI Direct",
"HDFC Securities", "Kotak Securities", "Axis Direct", "IIFL", "Paytm Money", "Edelweiss", "Geojit",
"Fyers", "Alice Blue", "mStock", "Stockal", "Kuvera", "Smallcase", "Jupiter", "Fi", "INDmoney",
"PhonePe", "Paytm", "Google Pay", "BHIM", "MobiKwik", "Cred", "Niyo", "Razorpay", "ETMoney",
"Bajaj Finserv", "SBI Securities", "YES Securities", "IDFC FIRST", "CAMS", "Karvy", "LIC", "ICICI Prudential"
]
def extract_brands(text):
found = [brand for brand in BRANDS if brand.lower() in text.lower()]
return found if found else ["None detected"]
def extract_topics(text, top_n=5):
keywords = kw_model.extract_keywords(text, top_n=top_n, stop_words='english')
topics = [kw for kw, score in keywords]
return topics if topics else ["None extracted"]
def make_bullets(summary):
sentences = summary.replace("\n", " ").split('. ')
bullets = [f"- {s.strip()}" for s in sentences if s.strip()]
return "\n".join(bullets)
def make_str(val):
try:
if val is None:
return ""
if isinstance(val, (bool, int, float)):
return str(val)
if isinstance(val, list):
return "\n".join([make_str(v) for v in val])
if isinstance(val, dict):
return str(val)
return str(val)
except Exception:
return ""
def process_audio(audio_path):
if not audio_path or not isinstance(audio_path, str):
return ("No audio file provided.", "", "", "", "", "")
try:
with open(audio_path, "rb") as audio_file:
transcript = openai.audio.transcriptions.create(
model="whisper-1",
file=audio_file,
response_format="text"
)
transcript = make_str(transcript).strip()
except Exception as e:
return (f"Error in transcription: {e}", "", "", "", "", "")
try:
detected_lang = detect(transcript)
lang_text = {'en': 'English', 'hi': 'Hindi', 'ta': 'Tamil'}.get(detected_lang, detected_lang)
except Exception:
lang_text = "unknown"
transcript_en = transcript
if detected_lang != "en":
try:
with open(audio_path, "rb") as audio_file:
transcript_en = openai.audio.translations.create(
model="whisper-1",
file=audio_file,
response_format="text"
)
transcript_en = make_str(transcript_en).strip()
except Exception as e:
transcript_en = f"Error translating: {e}"
try:
summary_obj = summarizer(transcript_en, max_length=100, min_length=30, do_sample=False)
summary = summary_obj[0]["summary_text"] if isinstance(summary_obj, list) and "summary_text" in summary_obj[0] else make_str(summary_obj)
except Exception as e:
summary = f"Error summarizing: {e}"
brands = extract_brands(transcript_en)
topics = extract_topics(transcript_en)
key_takeaways = make_bullets(summary)
return (
lang_text,
transcript,
transcript_en,
", ".join(brands),
", ".join(topics),
key_takeaways
)
iface = gr.Interface(
fn=process_audio,
inputs=gr.Audio(type="filepath", label="Upload MP3/WAV Audio"),
outputs=[
gr.Textbox(label="Detected Language"),
gr.Textbox(label="Original Transcript"),
gr.Textbox(label="English Transcript (if translated)"),
gr.Textbox(label="Indian Brokerages & Fintech Brands Detected"),
gr.Textbox(label="Key Topics"),
gr.Textbox(label="Bulleted Key Takeaways")
],
title="Audio to Text & Insights Generation",
description="Upload your audio file (MP3/WAV). Get key insights!"
)
iface.launch()
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