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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
# Initialize the InferenceClient for PHI 3
client = InferenceClient(
"microsoft/Phi-3.5-mini-instruct", # Update this to the correct model name for PHI 3
token=os.getenv("HF_API_TOKEN", "") # You can configure this API token through the Hugging Face Secrets
)
# 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=0 if device == 'cuda' else -1)
# Instructions (can be set through Hugging Face Secrets or hardcoded)
instructions = os.getenv("INST", "Your default instructions here.")
def query_phi(prompt):
response = "" # Initialize an empty string to store the response
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 # Append each message to the response
return response # Return the accumulated response after the loop
def transcribe_and_query(audio):
# Transcribe the audio file
transcription = whisper(audio)["text"]
transcription = "Prompt : " + transcription
# Query Microsoft PHI 3 with the transcribed text
phi_response = query_phi(transcription)
return transcription, phi_response
# 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) # share=True is optional, it provides a public link |