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# -*- coding: utf-8 -*-
"""OpenAI Whisper from Hugging Face Transformers with Microsoft PHI 3 Integration"""
import gradio as gr
from transformers import pipeline
import torch
from huggingface_hub import InferenceClient
import os
import librosa
# Fetch the token from Hugging Face Secrets
HF_API_TOKEN = os.getenv("HF_API_TOKEN", "")
client = InferenceClient(
"microsoft/phi-4",
token=HF_API_TOKEN
)
# Check if a GPU is available and use it if possible
device = 'cuda' if torch.cuda.is_available() else 'cpu'
# Initialize the Whisper pipeline
whisper = pipeline('automatic-speech-recognition', model='openai/whisper-tiny', device=device)
# Instructions (can be set through Hugging Face Secrets or hardcoded)
instructions = os.getenv("INST", "Your default instructions here.")
def query_phi(prompt):
print("Sending request to PHI 3 API...")
response = ""
try:
for message in client.chat_completion(
messages=[{"role": "user", "content": f"{instructions}\n{prompt}"}],
max_tokens=500,
stream=True,
):
response += message.choices[0].delta.content
except Exception as e:
print("Error in PHI 3 API:", e)
return "PHI 3 API Error: " + str(e)
return response
def transcribe_and_query(audio):
try:
# Load the audio file as waveform
audio_data, sr = librosa.load(audio, sr=16000)
# Transcribe using Whisper
transcription = whisper(audio_data)["text"]
transcription = "Prompt : " + transcription
# Query Microsoft PHI 3 with the transcribed text
phi_response = query_phi(transcription)
return transcription, phi_response
except Exception as e:
return f"Error processing audio: {str(e)}", "No response from PHI 3"
# Create Gradio interface
iface = gr.Interface(
fn=transcribe_and_query,
inputs=gr.Audio(type="filepath"),
outputs=["text", "text"],
title="Scam Call Detector with BEEP",
description="Upload your recorded call to see if it is a scam or not.\n Stay Safe, Stay Secure."
)
# Launch the interface
iface.launch(share=True) |