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import streamlit as st
import requests
import os
# Fetch Hugging Face and Groq API keys from secrets
HUGGINGFACE_TOKEN = os.getenv('HUGGINGFACE_TOKEN')
GROQ_API_KEY = os.getenv('GROQ_API_KEY')
# API Headers
headers_hf = {"Authorization": f"Bearer {HUGGINGFACE_TOKEN}"}
headers_groq = {
"Authorization": f"Bearer {GROQ_API_KEY}",
"Content-Type": "application/json"
}
# Translation Model API URL (Tamil to English)
translation_url = "https://api-inference.huggingface.co/models/facebook/mbart-large-50-many-to-one-mmt"
# Text-to-Image Model API URL
image_generation_url = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-schnell"
# Function to query Hugging Face translation model
def translate_text(text):
payload = {"inputs": text}
response = requests.post(translation_url, headers=headers_hf, json=payload)
if response.status_code == 200:
result = response.json()
translated_text = result[0]['generated_text']
return translated_text
else:
st.error(f"Translation Error {response.status_code}: {response.text}")
return None
# Function to query Groq content generation model
def generate_content(english_text, max_tokens, temperature):
url = "https://api.groq.com/openai/v1/chat/completions"
payload = {
"model": "llama-3.1-70b-versatile",
"messages": [
{"role": "system", "content": "You are a creative and insightful writer."},
{"role": "user", "content": f"Write educational content about {english_text} within {max_tokens} tokens."}
],
"max_tokens": max_tokens,
"temperature": temperature
}
response = requests.post(url, json=payload, headers=headers_groq)
if response.status_code == 200:
result = response.json()
return result['choices'][0]['message']['content']
else:
st.error(f"Content Generation Error: {response.status_code}")
return None
# Function to generate image prompt
def generate_image_prompt(english_text):
payload = {
"model": "mixtral-8x7b-32768",
"messages": [
{"role": "system", "content": "You are a professional Text to image prompt generator."},
{"role": "user", "content": f"Create a text to image generation prompt about {english_text} within 30 tokens."}
],
"max_tokens": 30
}
response = requests.post("https://api.groq.com/openai/v1/chat/completions", json=payload, headers=headers_groq)
if response.status_code == 200:
result = response.json()
return result['choices'][0]['message']['content']
else:
st.error(f"Prompt Generation Error: {response.status_code}")
return None
# Function to generate an image from the prompt
def generate_image(image_prompt):
data = {"inputs": image_prompt}
response = requests.post(image_generation_url, headers=headers_hf, json=data)
if response.status_code == 200:
return response.content
else:
st.error(f"Image Generation Error {response.status_code}: {response.text}")
return None
# Main Streamlit app
def main():
st.title("Multimodal Generator")
# Sidebar for temperature and token adjustment
st.sidebar.header("Settings")
temperature = st.sidebar.slider("Select Temperature", 0.1, 1.0, 0.7)
max_tokens = st.sidebar.slider("Max Tokens for Content Generation", 100, 300, 200)
# Suggested inputs
st.write("## Suggested Inputs")
suggestions = ["தரவு அறிவியல்", "புதிய திறன்களைக் கற்றுக்கொள்வது எப்படி", "ராக்கெட் எப்படி வேலை செய்கிறது"]
selected_suggestion = st.selectbox("Select a suggestion or enter your own:", [""] + suggestions)
# Input box for user
tamil_input = st.text_input("Enter Tamil text (or select a suggestion):", selected_suggestion)
if st.button("Generate"):
# Step 1: Translation (Tamil to English)
if tamil_input:
st.write("### Translated English Text:")
english_text = translate_text(tamil_input)
if english_text:
st.success(english_text)
# Step 2: Generate Educational Content
st.write("### Generated Educational Content:")
with st.spinner('Generating content...'):
content_output = generate_content(english_text, max_tokens, temperature)
if content_output:
st.success(content_output)
# Step 3: Generate Image from the prompt
st.write("### Generated Image:")
with st.spinner('Generating image...'):
image_prompt = generate_image_prompt(english_text)
image_data = generate_image(image_prompt)
if image_data:
st.image(image_data, caption="Generated Image")
if __name__ == "__main__":
main()
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