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import os
import streamlit as st
import torch
import tempfile
from groq import Groq
from diffusers import AutoPipelineForText2Image
from io import BytesIO
# Load API keys
GROQ_API_KEY = os.getenv("GROQ_API_KEY")
HF_API_KEY = os.getenv("HF_API_KEY")
# Initialize Groq client
client = Groq(api_key=GROQ_API_KEY)
# Load image generation model
device = "cuda" if torch.cuda.is_available() else "cpu"
image_gen = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo", use_auth_token=HF_API_KEY).to(device)
# Function to transcribe Tamil audio using Groq's Whisper
def transcribe(audio_bytes):
if not audio_bytes:
return "No audio provided."
# Save the audio file temporarily
with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_audio:
temp_audio.write(audio_bytes)
temp_audio_path = temp_audio.name
# Call Whisper API
with open(temp_audio_path, "rb") as file:
transcription = client.audio.transcriptions.create(
file=file,
model="whisper-large-v3",
language="ta",
response_format="verbose_json"
)
# Cleanup temp file
os.remove(temp_audio_path)
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")
# Upload audio file
audio_file = st.file_uploader("Upload a Tamil audio file", type=["wav", "mp3"])
if st.button("Generate"):
if audio_file is not None:
# Read audio bytes
audio_bytes = audio_file.read()
# Process Steps
tamil_text = transcribe(audio_bytes)
english_text = translate_text(tamil_text)
story = generate_text(english_text)
image = generate_image(english_text)
# Display Outputs
st.subheader("📝 Transcribed Tamil Text")
st.write(tamil_text)
st.subheader("🔠 Translated English Text")
st.write(english_text)
st.subheader("📖 Generated Story")
st.write(story)
st.subheader("🖼️ Generated Image")
st.image(image, caption="Generated Image from Story")
else:
st.warning("⚠️ Please upload an audio file before generating.")
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