app.py

import os
import tempfile
import asyncio
from pathlib import Path
import gradio as gr
from huggingface_hub import InferenceClient
import edge_tts
from pydub import AudioSegment
from langchain_community.document_loaders import PyPDFLoader, TextLoader, Docx2txtLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter

# =================================================================
# 1. UI STYLING & PREMIUM MOVING ANIMATIONS
# =================================================================
CUSTOM_CSS = """
.gradio-container {
    background: #ffffff;
    background-image: 
        radial-gradient(at 0% 0%, rgba(147, 51, 234, 0.15) 0px, transparent 50%),
        radial-gradient(at 100% 0%, rgba(249, 115, 22, 0.12) 0px, transparent 50%),
        radial-gradient(at 100% 100%, rgba(147, 51, 234, 0.15) 0px, transparent 50%),
        radial-gradient(at 0% 100%, rgba(249, 115, 22, 0.12) 0px, transparent 50%);
    background-attachment: fixed;
    animation: meshFlow 20s ease-in-out infinite alternate;
    min-height: 100vh;
    overflow-x: hidden;
}

@keyframes meshFlow {
    0% { background-size: 100% 100%; background-position: 0% 0%; }
    50% { background-size: 140% 140%; background-position: 50% 50%; }
    100% { background-size: 100% 100%; background-position: 100% 100%; }
}

.glass-panel {
    background: rgba(255, 255, 255, 0.5) !important;
    backdrop-filter: blur(25px) saturate(160%);
    -webkit-backdrop-filter: blur(25px) saturate(160%);
    border: 1px solid rgba(255, 255, 255, 0.4) !important;
    border-radius: 28px !important;
    padding: 30px !important;
    box-shadow: 0 20px 40px rgba(0, 0, 0, 0.03) !important;
    transition: all 0.4s cubic-bezier(0.4, 0, 0.2, 1);
}

.glass-panel:hover {
    transform: translateY(-8px);
    background: rgba(255, 255, 255, 0.65) !important;
    box-shadow: 0 35px 70px rgba(147, 51, 234, 0.12) !important;
}

.premium-btn {
    background: linear-gradient(135deg, #f97316 0%, #9333ea 50%, #f97316 100%) !important;
    background-size: 200% auto !important;
    border: none !important;
    color: white !important;
    font-weight: 800 !important;
    text-transform: uppercase;
    letter-spacing: 1px;
    border-radius: 15px !important;
    box-shadow: 0 10px 25px rgba(147, 51, 234, 0.35) !important;
    transition: 0.5s all !important;
}

.premium-btn:hover {
    background-position: right center !important;
    transform: scale(1.04);
    box-shadow: 0 15px 35px rgba(147, 51, 234, 0.5) !important;
}

.gradio-container > * {
    animation: fadeIn 1.2s ease-out;
}

@keyframes fadeIn {
    from { opacity: 0; transform: translateY(20px); }
    to { opacity: 1; transform: translateY(0); }
}
"""

SURAJIT_HF_TOKEN = os.getenv("CLONE_SURAJIT_TOKEN")
client = InferenceClient(token=SURAJIT_HF_TOKEN)
MODEL_ID = "HuggingFaceH4/zephyr-7b-beta"

# =================================================================
# 2. CORE LOGIC
# =================================================================

def process_multiple_documents(files) -> str:
    if not files: return ""
    combined_text = ""
    for file in files:
        ext = Path(file.name).suffix.lower()
        try:
            if ext == ".pdf": loader = PyPDFLoader(file.name)
            elif ext == ".docx": loader = Docx2txtLoader(file.name)
            else: loader = TextLoader(file.name)
            docs = loader.load()
            combined_text += " ".join([d.page_content for d in docs]) + "\n\n"
        except Exception as e:
            print(f"Error loading {file.name}: {e}")
    
    splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
    chunks = splitter.split_text(combined_text)
    return " ".join(chunks)[:10000]

def generate_timed_script(context: str, mode: str, duration: str):
    duration_map = {
        "1 Minute (Short)": "approx 150 words",
        "5 Minutes (Medium)": "approx 750 words",
        "10 Minutes (Detailed)": "approx 1500 words",
        "20 Minutes (Deep Dive)": "approx 3000 words"
    }
    target_len = duration_map.get(duration, "750 words")
    
    messages = [
        {"role": "system", "content": f"You are a master scriptwriter. Mode: {mode}. Length: {target_len}. Use 'Host:' and 'Expert:' for dialogue."},
        {"role": "user", "content": f"Analyze these documents and write the script:\n\n{context}"}
    ]
    response = client.chat_completion(model=MODEL_ID, messages=messages, max_tokens=2500)
    return response.choices[0].message.content

async def create_audio(script: str, mode: str, voice: str, speed: float):
    rate_val = int((speed - 1.0) * 100)
    rate_str = f"{rate_val:+d}%"
    
    if mode == "Podcast":
        combined = AudioSegment.empty()
        
        for line in script.split('\n'):
            line = line.strip()
            if not line: continue
            
            # Determine which voice to use
            if ":" in line:
                current_voice = voice if "Host" in line else "en-GB-SoniaNeural"
                text_to_speak = line.split(":", 1)[1].strip()
            else:
                # If no colon, Host reads the line instead of skipping it
                current_voice = voice
                text_to_speak = line
            
            if text_to_speak:
                communicate = edge_tts.Communicate(text_to_speak, current_voice, rate=rate_str)
                t_path = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3").name
                await communicate.save(t_path)
                combined += AudioSegment.from_mp3(t_path) + AudioSegment.silent(duration=600)
                os.remove(t_path)
        
        out = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3").name
        combined.export(out, format="mp3")
        return out
    else:
        communicate = edge_tts.Communicate(script, voice, rate=rate_str)
        tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3")
        await communicate.save(tmp.name)
        return tmp.name

# =================================================================
# 3. INTERFACE
# =================================================================
with gr.Blocks() as app:
    gr.HTML("<div style='text-align: center; padding: 20px;'><img src='https://cdn.pixabay.com/animation/2023/06/13/15/12/15-12-47-323_512.gif' style='width:50px;'><h1 style='color: #1f2937; font-weight: 900;'>AI Multi-Doc Studio</h1></div>")
    
    with gr.Row():
        with gr.Column(scale=1):
            with gr.Group(elem_classes="glass-panel"):
                gr.HTML("<h4>📚 Upload Documents</h4>")
                file_input = gr.File(label=None, file_count="multiple", file_types=[".pdf", ".docx", ".txt"])
                
                gr.HTML("<h4>⏱️ Duration & Style</h4>")
                duration_sel = gr.Dropdown(
                    ["1 Minute (Short)", "5 Minutes (Medium)", "10 Minutes (Detailed)", "20 Minutes (Deep Dive)"], 
                    value="5 Minutes (Medium)", label="Target Audio Length"
                )
                mode_sel = gr.Dropdown(["Podcast", "Storytelling", "Teaching", "Summary"], value="Podcast", label="Script Style")
                
                gr.HTML("<h4>🗣️ Voice Settings</h4>")
                voice_sel = gr.Dropdown([
                    ("Andrew (US - Male)", "en-US-AndrewNeural"),
                    ("Ava (US - Female)", "en-US-AvaNeural"),
                    ("Emma (UK - Female)", "en-GB-SoniaNeural"),
                    ("Aditi (IN - Female)", "en-IN-NeerjaNeural")
                ], value="en-US-AndrewNeural", label="Voice Selection")
                speed_sld = gr.Slider(0.5, 1.5, value=1.0, label="Pace")
                
                btn = gr.Button("🚀 GENERATE STUDIO AUDIO", elem_classes="premium-btn")
        
        with gr.Column(scale=1):
            with gr.Group(elem_classes="glass-panel"):
                gr.HTML("<h4>📝 Generated Script</h4>")
                out_txt = gr.Textbox(label=None, lines=15)
                gr.HTML("<h4>🔊 Audio Output</h4>")
                out_aud = gr.Audio(label=None)

    async def run_pipeline(files, dur, mode, voice, speed):
        if not files: return "Please upload at least one file.", None
        ctx = process_multiple_documents(files)
        sc = generate_timed_script(ctx, mode, dur)
        aud = await create_audio(sc, mode, voice, speed)
        return sc, aud

    btn.click(run_pipeline, inputs=[file_input, duration_sel, mode_sel, voice_sel, speed_sld], outputs=[out_txt, out_aud])

if __name__ == "__main__":
    app.launch(css=CUSTOM_CSS)