app.py

import os
import streamlit as st
import requests
import json
from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
from io import BytesIO

# Load tokens from environment variables
HUGGINGFACE_TOKEN = os.getenv("HUGGINGFACE_TOKEN")
GROQ_TOKEN = os.getenv("GROQ_TOKEN")

# Hugging Face API URLs
TRANSLATE_API_URL = "https://api-inference.huggingface.co/models/facebook/mbart-large-50-many-to-one-mmt"
IMAGE_API_URL = "https://api-inference.huggingface.co/models/CompVis/stable-diffusion-v1-4"
headers = {"Authorization": f"Bearer {HUGGINGFACE_TOKEN}"}

# Function to translate Tamil to English
def translate_tamil_to_english(text):
    payload = {"inputs": text}
    response = requests.post(TRANSLATE_API_URL, headers=headers, json=payload)
    if response.status_code == 200:
        result = response.json()
        return result[0]['generated_text']
    else:
        return f"Error {response.status_code}: {response.text}"

# Function to generate an image from a prompt
def generate_image(prompt):
    data = {"inputs": prompt}
    response = requests.post(IMAGE_API_URL, headers=headers, json=data)
    if response.status_code == 200:
        image = Image.open(BytesIO(response.content))
        return image
    else:
        return f"Error {response.status_code}: {response.text}"

# Function to generate text using Groq
def generate_text(prompt, max_tokens, temperature):
    messages = [{"role": "user", "content": prompt}]
    payload = {
        "model": "mixtral-8x7b-32768",
        "messages": messages,
        "max_tokens": max_tokens,
        "temperature": temperature,
    }
    response = requests.post("https://api.groq.com/openai/v1/chat/completions", 
                              headers={"Authorization": f"Bearer {GROQ_TOKEN}"}, 
                              json=payload)
    if response.status_code == 200:
        result = response.json()
        return result["choices"][0]["message"]["content"]
    else:
        return f"Error {response.status_code}: {response.text}"

# Process input data
def process_input(tamil_text, max_tokens, temperature):
    english_text = translate_tamil_to_english(tamil_text)
    image = generate_image(english_text)
    generated_story = generate_text(english_text, max_tokens, temperature)
    return english_text, image, generated_story

# Streamlit UI
st.markdown("<h1 style='text-align: center;'>Tamil to English Translation and Story Generation App</h1>", unsafe_allow_html=True)

st.sidebar.header("Settings")
tamil_text_input = st.sidebar.text_area("Enter Tamil Text")
max_tokens_input = st.sidebar.slider("Max Tokens", min_value=50, max_value=200, value=100, step=10)
temperature_input = st.sidebar.slider("Temperature", min_value=0.0, max_value=1.0, value=0.7, step=0.1)

if st.sidebar.button("Submit"):
    english_text, image, generated_story = process_input(tamil_text_input, max_tokens_input, temperature_input)

    st.subheader("Translated English Text")
    st.write(english_text)

    st.subheader("Generated Image")
    if isinstance(image, Image.Image):
        plt.imshow(np.array(image))
        plt.axis('off')
        st.pyplot(plt)
    else:
        st.error(image)

    st.subheader("Generated Story")
    st.write(generated_story)

# Guide section
if st.sidebar.button("How to Use"):
    st.sidebar.write(""" 
    ### How to Use This App 
    1. **Enter Tamil Text**: Type or paste the Tamil text you want to translate. 
    2. **Adjust Settings**: Use the sliders to set the maximum tokens for text generation and the temperature for creativity. 
    3. **Submit**: Click the Submit button to generate translations, images, and stories. 
    4. **View Results**: Check the translated text, generated image, and story in the main area. 
    """)