app.py

import os
import requests
import torch
import scipy.io.wavfile as wav
import streamlit as st
from io import BytesIO
from transformers import (
    AutoTokenizer, 
    AutoModelForCausalLM, 
    pipeline,
    AutoProcessor, 
    MusicgenForConditionalGeneration
)
from streamlit_lottie import st_lottie

# ---------------------------------------------------------------------
# 1) PAGE CONFIGURATION
# ---------------------------------------------------------------------
st.set_page_config(
    page_title="AI Radio Imaging with Llama 3",
    page_icon="🎧",
    layout="wide"
)

# ---------------------------------------------------------------------
# 2) CUSTOM CSS / UI DESIGN
# ---------------------------------------------------------------------
CUSTOM_CSS = """
<style>
body {
    background-color: #121212;
    color: #FFFFFF;
    font-family: "Helvetica Neue", sans-serif;
}
.block-container {
    max-width: 1100px;
    padding: 1rem 1.5rem;
}
h1, h2, h3 {
    color: #1DB954;
}
.stButton>button {
    background-color: #1DB954 !important;
    color: #FFFFFF !important;
    border-radius: 24px;
    padding: 0.6rem 1.2rem;
}
.stButton>button:hover {
    background-color: #1ed760 !important;
}
textarea, input, select {
    border-radius: 8px !important;
    background-color: #282828 !important;
    color: #FFFFFF !important;
}
audio {
    width: 100%;
    margin-top: 1rem;
}
.footer-note {
    text-align: center; 
    font-size: 14px; 
    opacity: 0.7;
    margin-top: 2rem;
}
#MainMenu, footer {visibility: hidden;}
</style>
"""
st.markdown(CUSTOM_CSS, unsafe_allow_html=True)

# ---------------------------------------------------------------------
# 3) LOAD LOTTIE ANIMATION
# ---------------------------------------------------------------------
@st.cache_data
def load_lottie_url(url: str):
    r = requests.get(url)
    if r.status_code != 200:
        return None
    return r.json()

LOTTIE_URL = "https://assets3.lottiefiles.com/temp/lf20_Q6h5zV.json"
lottie_animation = load_lottie_url(LOTTIE_URL)

# ---------------------------------------------------------------------
# 4) LOAD LLAMA 3 (GATED MODEL)
# ---------------------------------------------------------------------
@st.cache_resource
def load_llama_pipeline(model_id: str, device: str, token: str):
    try:
        tokenizer = AutoTokenizer.from_pretrained(model_id, use_auth_token=token)
        model = AutoModelForCausalLM.from_pretrained(
            model_id,
            use_auth_token=token,
            torch_dtype=torch.float16 if device == "auto" else torch.float32,
            device_map=device,
            low_cpu_mem_usage=True
        )
        text_gen_pipeline = pipeline(
            "text-generation",
            model=model,
            tokenizer=tokenizer,
            device_map=device
        )
        return text_gen_pipeline
    except Exception as e:
        st.error(f"Error loading Llama model: {e}")
        raise

# ---------------------------------------------------------------------
# 5) GENERATE RADIO SCRIPT
# ---------------------------------------------------------------------
def generate_radio_script(user_input: str, pipeline_llama) -> str:
    system_prompt = (
        "You are a top-tier radio imaging producer using Llama 3. "
        "Take the user's concept and craft a short, creative promo script."
    )
    combined_prompt = f"{system_prompt}\nUser concept: {user_input}\nRefined script:"

    result = pipeline_llama(
        combined_prompt,
        max_new_tokens=200,
        do_sample=True,
        temperature=0.9
    )
    output_text = result[0]["generated_text"]
    if "Refined script:" in output_text:
        output_text = output_text.split("Refined script:", 1)[-1].strip()
    output_text += "\n\n(Generated by Llama 3 - Radio Imaging)"
    return output_text

# ---------------------------------------------------------------------
# 6) LOAD MUSICGEN
# ---------------------------------------------------------------------
@st.cache_resource
def load_musicgen_model():
    mg_model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-small")
    mg_processor = AutoProcessor.from_pretrained("facebook/musicgen-small")
    return mg_model, mg_processor

# ---------------------------------------------------------------------
# 7) HEADER
# ---------------------------------------------------------------------
st.title("🎧 AI Radio Imaging with Llama 3")
st.subheader("Create engaging radio promos with Llama 3 + MusicGen")
st.markdown("""Create **radio imaging promos** and **jingles** easily. Ensure you have access to 
**meta-llama/Meta-Llama-3-70B** on Hugging Face and provide your token below.""")

if lottie_animation:
    st_lottie(lottie_animation, height=180, loop=True, key="radio_lottie")

st.markdown("---")

# ---------------------------------------------------------------------
# 8) USER INPUT
# ---------------------------------------------------------------------
st.subheader("🎤 Step 1: Describe Your Promo Idea")
prompt = st.text_area(
    "Example: 'A 15-second hype jingle for a morning talk show, fun and energetic.'",
    height=120
)

col_model, col_device = st.columns(2)
with col_model:
    llama_model_id = st.text_input(
        "Llama 3 Model ID",
        value="meta-llama/Meta-Llama-3-70B",
        help="Enter the exact model ID from Hugging Face."
    )
with col_device:
    device_option = st.selectbox(
        "Device",
        ["auto", "cpu"],
        help="Choose GPU (auto) or CPU."
    )

hf_token = os.getenv("HF_TOKEN")
if not hf_token:
    st.error("No HF_TOKEN found. Please set it in your environment.")
    st.stop()

if st.button("✍ Generate Promo Script"):
    if not prompt.strip():
        st.error("Please provide a concept first.")
    else:
        with st.spinner("Generating script..."):
            try:
                llama_pipeline = load_llama_pipeline(llama_model_id, device_option, hf_token)
                final_script = generate_radio_script(prompt, llama_pipeline)
                st.success("Promo script generated!")
                st.text_area("Generated Script", value=final_script, height=200)
            except Exception as e:
                st.error(f"Llama generation error: {e}")

st.markdown("---")

# ---------------------------------------------------------------------
# 9) GENERATE AUDIO WITH MUSICGEN
# ---------------------------------------------------------------------
st.subheader("🎵 Step 2: Generate Audio")
audio_length = st.slider("Track Length (tokens)", 128, 1024, 512, 64)

if st.button("🎧 Create Audio"):
    if "final_script" not in st.session_state:
        st.error("Please generate a script first.")
    else:
        with st.spinner("Generating audio..."):
            try:
                mg_model, mg_processor = load_musicgen_model()
                inputs = mg_processor(
                    text=[st.session_state["final_script"]],
                    padding=True,
                    return_tensors="pt"
                )
                audio_values = mg_model.generate(**inputs, max_new_tokens=audio_length)
                sr = mg_model.config.audio_encoder.sampling_rate
                output_file = "radio_jingle.wav"

                audio_data = audio_values[0, 0].cpu().numpy()
                normalized_audio = (audio_data / max(abs(audio_data)) * 32767).astype("int16")
                wav.write(output_file, rate=sr, data=normalized_audio)

                st.success("Audio generated! Play it below:")
                st.audio(output_file)
            except Exception as e:
                st.error(f"MusicGen error: {e}")

# ---------------------------------------------------------------------
# 10) FOOTER
# ---------------------------------------------------------------------
st.markdown("---")
st.markdown(
    """
    <div class="footer-note">
    © 2025 AI Radio Imaging – Built with Hugging Face & Streamlit
    </div>
    """,
    unsafe_allow_html=True
)