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import os
import torch
import streamlit as st
from groq import Groq
from diffusers import AutoPipelineForText2Image
# Load API keys
GROQ_API_KEY = os.getenv("GROQ_API_KEY")
HF_API_KEY = os.getenv("HF_API_KEY")
# Initialize Groq client with API key
client = Groq(api_key=GROQ_API_KEY)
# Select device (GPU if available, else CPU)
device = "cuda" if torch.cuda.is_available() else "cpu"
st.write(f"Using device: {device}") # Display device info
# Load lightweight Hugging Face image generation model
image_gen = AutoPipelineForText2Image.from_pretrained(
"stabilityai/sdxl-turbo", use_auth_token=HF_API_KEY
)
image_gen.to(device)
# Function to transcribe Tamil audio using Groq's Whisper
def transcribe(audio_file):
with open(audio_file, "rb") as file:
transcription = client.audio.transcriptions.create(
file=(audio_file, file.read()),
model="whisper-large-v3",
language="ta", # Tamil
response_format="verbose_json"
)
return transcription["text"]
# Function to translate Tamil to English using Groq's Gemma
def translate_text(tamil_text):
response = client.chat.completions.create(
model="gemma-7b-it",
messages=[{"role": "user", "content": f"Translate this Tamil text to English: {tamil_text}"}]
)
return response.choices[0].message.content
# Function to generate text using Groq's DeepSeek R1
def generate_text(prompt):
response = client.chat.completions.create(
model="deepseek-coder-r1-7b",
messages=[{"role": "user", "content": f"Write a short story about: {prompt}"}]
)
return response.choices[0].message.content
# Function to generate an image
def generate_image(prompt):
img = image_gen(prompt=prompt).images[0]
return img
# Streamlit UI
st.title("Tamil Speech to Image & Story Generator")
# File uploader for audio
uploaded_audio = st.file_uploader("Upload your Tamil speech", type=["wav", "mp3", "m4a"])
if uploaded_audio is not None:
st.audio(uploaded_audio, format="audio/wav")
if st.button("Generate"):
with st.spinner("Transcribing..."):
tamil_text = transcribe(uploaded_audio)
st.success("Transcription complete!")
st.text_area("Tamil Text Output", tamil_text)
with st.spinner("Translating to English..."):
english_text = translate_text(tamil_text)
st.success("Translation complete!")
st.text_area("Translated English Text", english_text)
with st.spinner("Generating story..."):
story = generate_text(english_text)
st.success("Story generation complete!")
st.text_area("Generated Story", story)
with st.spinner("Generating image..."):
image = generate_image(english_text)
st.success("Image generation complete!")
st.image(image, caption="Generated Image")
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