app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
# import spaces # Removed as @spaces.GPU is not used with async
from duckduckgo_search import DDGS
import time
import torch
from datetime import datetime
import os
import subprocess
import numpy as np
from typing import List, Dict, Tuple, Any, Optional, Union
from functools import lru_cache
import asyncio
import threading
from concurrent.futures import ThreadPoolExecutor
import warnings
import traceback # For detailed error logging
import re # For text cleaning
import shutil # For checking sudo
import html # For escaping HTML

# --- Configuration ---
MODEL_NAME = "deepseek-ai/DeepSeek-R1-Distill-Llama-8B"
MAX_SEARCH_RESULTS = 5
TTS_SAMPLE_RATE = 24000
MAX_TTS_CHARS = 1000 # Max characters for a single TTS chunk
MAX_NEW_TOKENS = 300
TEMPERATURE = 0.7
TOP_P = 0.95
KOKORO_PATH = 'Kokoro-82M' # Path to TTS model directory

# --- Initialization ---
# Use a ThreadPoolExecutor for blocking I/O or CPU-bound tasks
executor = ThreadPoolExecutor(max_workers=os.cpu_count() or 4) # Use available cores

# Suppress specific warnings
warnings.filterwarnings("ignore", category=UserWarning, message="TypedStorage is deprecated")
warnings.filterwarnings("ignore", message="Backend 'inductor' is not available.")

# --- LLM Initialization ---
llm_model: Optional[AutoModelForCausalLM] = None
llm_tokenizer: Optional[AutoTokenizer] = None
llm_device = "cpu" # Default device

try:
    print("Initializing LLM...")
    llm_tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
    llm_tokenizer.pad_token = llm_tokenizer.eos_token

    if torch.cuda.is_available():
        llm_device = "cuda"
        torch_dtype = torch.float16
        device_map = "auto" # Let accelerate handle distribution
        print(f"CUDA detected. Loading model with device_map='{device_map}', dtype={torch_dtype}")
    else:
        llm_device = "cpu"
        torch_dtype = torch.float32 # float32 for CPU
        device_map = {"": "cpu"}
        print(f"CUDA not found. Loading model on CPU with dtype={torch_dtype}")

    llm_model = AutoModelForCausalLM.from_pretrained(
        MODEL_NAME,
        device_map=device_map,
        low_cpu_mem_usage=True,
        torch_dtype=torch_dtype,
        # attn_implementation="flash_attention_2" # Optional: Uncomment if flash-attn is installed and compatible GPU
    )
    print(f"LLM loaded successfully. Device map: {llm_model.hf_device_map if hasattr(llm_model, 'hf_device_map') else 'N/A'}")
    llm_model.eval() # Set to evaluation mode

except Exception as e:
    print(f"FATAL: Error initializing LLM model: {str(e)}")
    print(traceback.format_exc())
    # Depending on environment, you might exit or just disable LLM features
    llm_model = None
    llm_tokenizer = None
    print("LLM features will be unavailable.")


# --- TTS Initialization ---
VOICE_CHOICES = {
    '🇺🇸 Female (Default)': 'af',
    '🇺🇸 Bella': 'af_bella',
    '🇺🇸 Sarah': 'af_sarah',
    '🇺🇸 Nicole': 'af_nicole'
}
TTS_ENABLED = False
tts_model: Optional[Any] = None # Define type more specifically if Kokoro provides it
voicepacks: Dict[str, Any] = {}  # Cache voice packs
tts_device = "cpu" # Default device for TTS model

# Use a lock for thread-safe access during initialization if needed, though Thread ensures sequential execution
# tts_init_lock = threading.Lock()

def _run_subprocess(cmd: List[str], check: bool = True, cwd: Optional[str] = None) -> subprocess.CompletedProcess:
    """Helper to run subprocess and capture output."""
    print(f"Running command: {' '.join(cmd)}")
    try:
        result = subprocess.run(cmd, check=check, capture_output=True, text=True, cwd=cwd)
        if result.stdout: print(f"Stdout: {result.stdout.strip()}")
        if result.stderr: print(f"Stderr: {result.stderr.strip()}")
        return result
    except FileNotFoundError:
        print(f"Error: Command not found - {cmd[0]}")
        raise
    except subprocess.CalledProcessError as e:
        print(f"Error running command: {' '.join(e.cmd)}")
        if e.stdout: print(f"Stdout: {e.stdout.strip()}")
        if e.stderr: print(f"Stderr: {e.stderr.strip()}")
        raise

def setup_tts_task():
    """Initializes Kokoro TTS model and dependencies."""
    global TTS_ENABLED, tts_model, voicepacks, tts_device
    print("[TTS Setup] Starting background initialization...")

    # Determine TTS device
    tts_device = "cuda" if torch.cuda.is_available() else "cpu"
    print(f"[TTS Setup] Target device: {tts_device}")

    can_sudo = shutil.which('sudo') is not None
    apt_cmd_prefix = ['sudo'] if can_sudo else []

    try:
        # 1. Clone Kokoro Repo if needed
        if not os.path.exists(KOKORO_PATH):
            print(f"[TTS Setup] Cloning repository to {KOKORO_PATH}...")
            try:
                _run_subprocess(['git', 'lfs', 'install', '--system', '--skip-repo'])
            except Exception as lfs_err:
                print(f"[TTS Setup] Warning: git lfs install command failed: {lfs_err}. Continuing clone...")
            _run_subprocess(['git', 'clone', 'https://huggingface.co/hexgrad/Kokoro-82M', KOKORO_PATH])
            try:
                 print("[TTS Setup] Running git lfs pull...")
                 _run_subprocess(['git', 'lfs', 'pull'], cwd=KOKORO_PATH)
            except Exception as lfs_pull_err:
                 print(f"[TTS Setup] Warning: git lfs pull failed: {lfs_pull_err}")
        else:
            print(f"[TTS Setup] Directory {KOKORO_PATH} already exists.")

        # 2. Install espeak dependency
        print("[TTS Setup] Checking/Installing espeak...")
        try:
            _run_subprocess(apt_cmd_prefix + ['apt-get', 'update', '-qq'])
            _run_subprocess(apt_cmd_prefix + ['apt-get', 'install', '-y', '-qq', 'espeak-ng'])
            print("[TTS Setup] espeak-ng installed or already present.")
        except Exception:
            print("[TTS Setup] espeak-ng failed, trying espeak...")
            try:
                _run_subprocess(apt_cmd_prefix + ['apt-get', 'install', '-y', '-qq', 'espeak'])
                print("[TTS Setup] espeak installed or already present.")
            except Exception as espeak_err:
                print(f"[TTS Setup] ERROR: Failed to install both espeak-ng and espeak: {espeak_err}. TTS disabled.")
                return # Critical dependency missing

        # 3. Load Kokoro Model and Voices
        if os.path.exists(KOKORO_PATH):
            sys_path_updated = False
            if KOKORO_PATH not in sys.path:
                sys.path.append(KOKORO_PATH)
                sys_path_updated = True
            try:
                from models import build_model
                from kokoro import generate as generate_tts_internal

                globals()['build_model'] = build_model # Make available globally
                globals()['generate_tts_internal'] = generate_tts_internal

                model_file = os.path.join(KOKORO_PATH, 'kokoro-v0_19.pth')
                if not os.path.exists(model_file):
                    print(f"[TTS Setup] ERROR: Model file {model_file} not found. TTS disabled.")
                    return

                print(f"[TTS Setup] Loading TTS model from {model_file} onto {tts_device}...")
                tts_model = build_model(model_file, tts_device)
                tts_model.eval() # Set to eval mode
                print("[TTS Setup] TTS model loaded.")

                # Load voices
                loaded_voices = 0
                for voice_name, voice_id in VOICE_CHOICES.items():
                    voice_file_path = os.path.join(KOKORO_PATH, 'voices', f'{voice_id}.pt')
                    if os.path.exists(voice_file_path):
                        try:
                            print(f"[TTS Setup] Loading voice: {voice_id} ({voice_name})")
                            # map_location ensures it loads to the correct device
                            voicepacks[voice_id] = torch.load(voice_file_path, map_location=tts_device)
                            loaded_voices += 1
                        except Exception as e:
                            print(f"[TTS Setup] Warning: Failed to load voice {voice_id}: {str(e)}")
                    else:
                        print(f"[TTS Setup] Info: Voice file {voice_file_path} not found, skipping.")

                if loaded_voices == 0:
                    print("[TTS Setup] ERROR: No voicepacks could be loaded. TTS disabled.")
                    tts_model = None # Unload model if no voices
                    return

                TTS_ENABLED = True
                print(f"[TTS Setup] Initialization successful. {loaded_voices} voices loaded. TTS Enabled: {TTS_ENABLED}")

            except ImportError as ie:
                print(f"[TTS Setup] ERROR: Failed to import Kokoro modules: {ie}. Check clone and path. TTS disabled.")
            except Exception as load_err:
                print(f"[TTS Setup] ERROR: Failed loading TTS model/voices: {load_err}. TTS disabled.")
                print(traceback.format_exc())
            finally:
                 # Clean up sys.path if modified
                 if sys_path_updated and KOKORO_PATH in sys.path:
                     sys.path.remove(KOKORO_PATH)
        else:
            print(f"[TTS Setup] ERROR: {KOKORO_PATH} directory not found. TTS disabled.")

    except Exception as e:
        print(f"[TTS Setup] ERROR: Unexpected error during setup: {str(e)}")
        print(traceback.format_exc())
        # Ensure TTS is marked as disabled
        TTS_ENABLED = False
        tts_model = None
        voicepacks.clear()

# Start TTS setup in a background thread
print("Starting TTS setup thread...")
tts_setup_thread = threading.Thread(target=setup_tts_task, daemon=True)
tts_setup_thread.start()


# --- Core Functions ---

@lru_cache(maxsize=128)
def get_web_results_sync(query: str, max_results: int = MAX_SEARCH_RESULTS) -> List[Dict[str, Any]]:
    """Synchronous web search function with caching."""
    print(f"[Web Search] Searching (sync): '{query}' (max_results={max_results})")
    try:
        with DDGS() as ddgs:
            results = list(ddgs.text(query, max_results=max_results, safesearch='moderate', timelimit='y'))
            print(f"[Web Search] Found {len(results)} results.")
            formatted = [{
                "id": i + 1,
                "title": res.get("title", "No Title"),
                "snippet": res.get("body", "No Snippet"),
                "url": res.get("href", "#"),
            } for i, res in enumerate(results)]
            return formatted
    except Exception as e:
        print(f"[Web Search] Error: {e}")
        print(traceback.format_exc())
        return []

def format_llm_prompt(query: str, context: List[Dict[str, Any]]) -> str:
    """Formats the prompt for the LLM, including context and instructions."""
    current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
    context_str = "\n\n".join(
        [f"[{res['id']}] {res['title']}\n{res['snippet']}" for res in context]
    ) if context else "No relevant web context found."

    return f"""You are a helpful AI assistant. Answer the user's query based *only* on the provided web search context.
Instructions:
- Synthesize information from the context to answer concisely.
- Cite sources using bracket notation like [1], [2], etc., corresponding to the context IDs.
- If the context is insufficient, state that clearly. Do not add external information.
- Use markdown for formatting.

Current Time: {current_time}

Web Context:
---
{context_str}
---

User Query: {query}

Answer:"""

def format_sources_html(web_results: List[Dict[str, Any]]) -> str:
    """Formats search results into HTML for display."""
    if not web_results:
        return "<div class='no-sources'>No sources found for this query.</div>"
    items_html = ""
    for res in web_results:
        title_safe = html.escape(res.get("title", "Source"))
        snippet_safe = html.escape(res.get("snippet", "")[:150] + ("..." if len(res.get("snippet", "")) > 150 else ""))
        url = res.get("url", "#")
        items_html += f"""
        <div class='source-item'>
            <div class='source-number'>[{res['id']}]</div>
            <div class='source-content'>
                <a href="{url}" target="_blank" class='source-title' title="{url}">{title_safe}</a>
                <div class='source-snippet'>{snippet_safe}</div>
            </div>
        </div>
        """
    return f"<div class='sources-container'>{items_html}</div>"

async def generate_llm_answer(prompt: str) -> str:
    """Generates answer using the loaded LLM (Async Wrapper)."""
    if not llm_model or not llm_tokenizer:
        return "Error: LLM model is not available."

    print(f"[LLM Generate] Requesting generation (prompt length {len(prompt)})...")
    start_time = time.time()
    try:
        inputs = llm_tokenizer(
            prompt,
            return_tensors="pt",
            padding=True,
            truncation=True,
            max_length=1024, # Consider model's actual max length
            return_attention_mask=True
        ).to(llm_model.device) # Ensure inputs are on the same device as model parts

        with torch.inference_mode(), torch.cuda.amp.autocast(enabled=(llm_model.dtype == torch.float16)):
            # Run blocking model.generate in the executor thread pool
            outputs = await asyncio.get_event_loop().run_in_executor(
                executor,
                llm_model.generate,
                inputs.input_ids,
                attention_mask=inputs.attention_mask,
                max_new_tokens=MAX_NEW_TOKENS,
                temperature=TEMPERATURE,
                top_p=TOP_P,
                pad_token_id=llm_tokenizer.eos_token_id,
                eos_token_id=llm_tokenizer.eos_token_id,
                do_sample=True,
                num_return_sequences=1
            )

        # Decode only newly generated tokens relative to input
        output_ids = outputs[0][inputs.input_ids.shape[1]:]
        answer_part = llm_tokenizer.decode(output_ids, skip_special_tokens=True).strip()

        # Handle potential empty generation
        if not answer_part:
             # Sometimes the split method above is needed if the model includes the prompt
             full_output = llm_tokenizer.decode(outputs[0], skip_special_tokens=True)
             answer_marker = "Answer:"
             marker_index = full_output.rfind(answer_marker)
             if marker_index != -1:
                 answer_part = full_output[marker_index + len(answer_marker):].strip()
             else:
                 answer_part = "*Model generated an empty response.*" # Fallback message

        end_time = time.time()
        print(f"[LLM Generate] Generation complete in {end_time - start_time:.2f}s. Length: {len(answer_part)}")
        return answer_part

    except Exception as e:
        print(f"[LLM Generate] Error: {e}")
        print(traceback.format_exc())
        return f"Error during answer generation: {str(e)}"

async def generate_tts_speech(text: str, voice_id: str = 'af') -> Optional[Tuple[int, np.ndarray]]:
    """Generates speech using the loaded TTS model (Async Wrapper)."""
    if not TTS_ENABLED or not tts_model or 'generate_tts_internal' not in globals():
        print("[TTS Generate] Skipping: TTS not ready.")
        return None
    if not text or not text.strip():
        print("[TTS Generate] Skipping: Empty text.")
        return None

    print(f"[TTS Generate] Requesting speech (length {len(text)}, voice '{voice_id}')...")
    start_time = time.time()

    try:
        # Verify voicepack availability
        actual_voice_id = voice_id
        if voice_id not in voicepacks:
            print(f"[TTS Generate] Warning: Voice '{voice_id}' not loaded. Trying default 'af'.")
            actual_voice_id = 'af'
            if 'af' not in voicepacks:
                print("[TTS Generate] Error: Default voice 'af' also not available.")
                return None

        # Clean text for TTS
        clean_text = re.sub(r'\[\d+\](\[\d+\])*', '', text) # Remove citations like [1], [2][3]
        clean_text = re.sub(r'[\*\#\`]', '', clean_text)      # Remove markdown symbols
        clean_text = ' '.join(clean_text.split())             # Normalize whitespace

        if not clean_text: return None # Skip if empty after cleaning

        # Truncate if necessary
        if len(clean_text) > MAX_TTS_CHARS:
            print(f"[TTS Generate] Truncating text from {len(clean_text)} to {MAX_TTS_CHARS} chars.")
            clean_text = clean_text[:MAX_TTS_CHARS]
            last_punct = max(clean_text.rfind(p) for p in '.?! ')
            if last_punct != -1: clean_text = clean_text[:last_punct+1]
            clean_text += "..."

        print(f"[TTS Generate] Generating audio for: '{clean_text[:100]}...'")
        gen_func = globals()['generate_tts_internal']
        voice_pack_data = voicepacks[actual_voice_id]

        # Run blocking TTS generation in the executor thread pool
        # Assuming 'afr' is the correct language code for Kokoro's default voices
        audio_data, _ = await asyncio.get_event_loop().run_in_executor(
            executor,
            gen_func,
            tts_model,      # The loaded model object
            clean_text,     # The cleaned text string
            voice_pack_data,# The loaded voice pack tensor/dict
            'afr'           # Language code (verify this is correct)
        )

        if isinstance(audio_data, torch.Tensor):
            audio_np = audio_data.detach().cpu().numpy()
        elif isinstance(audio_data, np.ndarray):
            audio_np = audio_data
        else:
            print("[TTS Generate] Warning: Unexpected audio data type.")
            return None

        # Ensure audio is 1D float32
        audio_np = audio_np.flatten().astype(np.float32)

        end_time = time.time()
        print(f"[TTS Generate] Audio generated in {end_time - start_time:.2f}s. Shape: {audio_np.shape}")
        return (TTS_SAMPLE_RATE, audio_np)

    except Exception as e:
        print(f"[TTS Generate] Error: {str(e)}")
        print(traceback.format_exc())
        return None

def get_voice_id_from_display(voice_display_name: str) -> str:
    """Maps the user-friendly voice name to the internal voice ID."""
    return VOICE_CHOICES.get(voice_display_name, 'af') # Default to 'af'


# --- Gradio Interaction Logic ---

# Define type for chat history using the 'messages' format
ChatHistoryType = List[Dict[str, str]]

async def handle_interaction(
    query: str,
    history: ChatHistoryType,
    selected_voice_display_name: str
):
    """Main async generator function to handle user queries and update Gradio UI."""
    print(f"\n--- Handling Query ---")
    print(f"Query: '{query}', Voice: '{selected_voice_display_name}'")

    if not query or not query.strip():
        print("Empty query received.")
        # Need to yield the current state for all outputs
        yield history, "*Please enter a query.*", "<div class='no-sources'>Enter a query to search.</div>", None, gr.Button(value="Search", interactive=True)
        return

    # Append user message to history
    current_history = history + [{"role": "user", "content": query}]
    # Add placeholder for assistant response
    current_history.append({"role": "assistant", "content": "*Searching...*"})

    # 1. Initial State: Searching
    yield (
        current_history,
        "*Searching the web...*", # Update answer area
        "<div class='searching'><span>Searching the web...</span></div>", # Update sources area
        None, # No audio yet
        gr.Button(value="Searching...", interactive=False) # Update button state
    )

    # 2. Perform Web Search (in executor)
    web_results = await asyncio.get_event_loop().run_in_executor(
        executor, get_web_results_sync, query
    )
    sources_html = format_sources_html(web_results)

    # Update state: Generating Answer
    current_history[-1]["content"] = "*Generating answer...*" # Update assistant placeholder
    yield (
        current_history,
        "*Generating answer...*", # Update answer area
        sources_html,             # Show sources
        None,
        gr.Button(value="Generating...", interactive=False)
    )

    # 3. Generate LLM Answer (async)
    llm_prompt = format_llm_prompt(query, web_results)
    final_answer = await generate_llm_answer(llm_prompt)

    # Update assistant message in history with the final answer
    current_history[-1]["content"] = final_answer

    # Update state: Generating Audio (if applicable)
    yield (
        current_history,
        final_answer, # Show final answer
        sources_html,
        None,
        gr.Button(value="Audio...", interactive=False) if TTS_ENABLED else gr.Button(value="Search", interactive=True) # Enable search if TTS disabled
    )

    # 4. Generate TTS Speech (async)
    audio_output_data = None
    tts_status_message = ""
    if not TTS_ENABLED:
        if tts_setup_thread.is_alive():
             tts_status_message = "\n\n*(TTS initializing...)*"
        else:
             tts_status_message = "\n\n*(TTS disabled or failed)*"
    elif final_answer and not final_answer.startswith("Error"):
        voice_id = get_voice_id_from_display(selected_voice_display_name)
        audio_output_data = await generate_tts_speech(final_answer, voice_id)
        if audio_output_data is None:
            tts_status_message = "\n\n*(Audio generation failed)*"

    # 5. Final State: Show all results
    final_answer_with_status = final_answer + tts_status_message
    current_history[-1]["content"] = final_answer_with_status # Update history with status msg too

    print("--- Query Handling Complete ---")
    yield (
        current_history,
        final_answer_with_status, # Show answer + TTS status
        sources_html,
        audio_output_data,        # Output audio data (or None)
        gr.Button(value="Search", interactive=True) # Re-enable button
    )


# --- Gradio UI Definition ---
# (CSS remains largely the same - ensure it targets default Gradio classes if elem_classes was removed)
css = """
/* ... [Your existing refined CSS, but remove selectors using .gradio-examples if you were using it] ... */
/* Example: Style examples container via its parent or default class if needed */
/* .examples-container .gradio-examples { ... } */ /* This might still work depending on structure */
.gradio-container { max-width: 1200px !important; background-color: #f7f7f8 !important; }
#header { text-align: center; margin-bottom: 2rem; padding: 2rem 0; background: linear-gradient(135deg, #1a1b1e, #2d2e32); border-radius: 12px; color: white; box-shadow: 0 8px 32px rgba(0,0,0,0.2); }
#header h1 { color: white; font-size: 2.5rem; margin-bottom: 0.5rem; text-shadow: 0 2px 4px rgba(0,0,0,0.3); }
#header h3 { color: #a8a9ab; }
.search-container { background: #ffffff; border: 1px solid #e0e0e0; border-radius: 12px; box-shadow: 0 4px 16px rgba(0,0,0,0.05); padding: 1.5rem; margin-bottom: 1.5rem; }
.search-box { padding: 0; margin-bottom: 1rem; display: flex; align-items: center; }
.search-box .gradio-textbox { border-radius: 8px 0 0 8px !important; height: 44px !important; flex-grow: 1; }
.search-box .gradio-dropdown { border-radius: 0 !important; margin-left: -1px; margin-right: -1px; height: 44px !important; width: 180px; flex-shrink: 0; }
.search-box .gradio-button { border-radius: 0 8px 8px 0 !important; height: 44px !important; flex-shrink: 0; }
.search-box input[type="text"] { background: #f7f7f8 !important; border: 1px solid #d1d5db !important; color: #1f2937 !important; transition: all 0.3s ease; height: 100% !important; padding: 0 12px !important;}
.search-box input[type="text"]:focus { border-color: #2563eb !important; box-shadow: 0 0 0 2px rgba(37, 99, 235, 0.2) !important; background: white !important; z-index: 1; }
.search-box input[type="text"]::placeholder { color: #9ca3af !important; }
.search-box button { background: #2563eb !important; border: none !important; color: white !important; box-shadow: 0 1px 2px rgba(0,0,0,0.05) !important; transition: all 0.3s ease !important; height: 100% !important; }
.search-box button:hover { background: #1d4ed8 !important; }
.search-box button:disabled { background: #9ca3af !important; cursor: not-allowed; }
.results-container { background: transparent; padding: 0; margin-top: 1.5rem; }
.answer-box { background: white; border: 1px solid #e0e0e0; border-radius: 10px; padding: 1.5rem; color: #1f2937; margin-bottom: 1.5rem; box-shadow: 0 2px 8px rgba(0,0,0,0.05); }
.answer-box p { color: #374151; line-height: 1.7; }
.answer-box code { background: #f3f4f6; border-radius: 4px; padding: 2px 4px; color: #4b5563; font-size: 0.9em; }
.sources-box { background: white; border: 1px solid #e0e0e0; border-radius: 10px; padding: 1.5rem; }
.sources-box h3 { margin-top: 0; margin-bottom: 1rem; color: #111827; font-size: 1.2rem; }
.sources-container { margin-top: 0; }
.source-item { display: flex; padding: 10px 0; margin: 0; border-bottom: 1px solid #f3f4f6; transition: background-color 0.2s; }
.source-item:last-child { border-bottom: none; }
.source-number { font-weight: bold; margin-right: 12px; color: #6b7280; width: 20px; text-align: right; flex-shrink: 0;}
.source-content { flex: 1; min-width: 0;} /* Allow content to shrink */
.source-title { color: #2563eb; font-weight: 500; text-decoration: none; display: block; margin-bottom: 4px; transition: all 0.2s; font-size: 0.95em; white-space: nowrap; overflow: hidden; text-overflow: ellipsis;}
.source-title:hover { color: #1d4ed8; text-decoration: underline; }
.source-snippet { color: #4b5563; font-size: 0.9em; line-height: 1.5; }
.chat-history { /* Style the chatbot container */ max-height: 400px; overflow-y: auto; background: #f9fafb; border: 1px solid #e5e7eb; border-radius: 8px; margin-top: 1rem; scrollbar-width: thin; scrollbar-color: #d1d5db #f9fafb; }
.chat-history > div { padding: 1rem; } /* Add padding inside the chatbot display area */
.chat-history::-webkit-scrollbar { width: 6px; }
.chat-history::-webkit-scrollbar-track { background: #f9fafb; }
.chat-history::-webkit-scrollbar-thumb { background-color: #d1d5db; border-radius: 20px; }
.examples-container { background: #f9fafb; border-radius: 8px; padding: 1rem; margin-top: 1rem; border: 1px solid #e5e7eb; }
/* Default styling for example buttons (since elem_classes might not work) */
.examples-container button { background: white !important; border: 1px solid #d1d5db !important; color: #374151 !important; transition: all 0.2s; margin: 4px !important; font-size: 0.9em !important; padding: 6px 12px !important; border-radius: 4px !important; }
.examples-container button:hover { background: #f3f4f6 !important; border-color: #adb5bd !important; }
.markdown-content { color: #374151 !important; font-size: 1rem; line-height: 1.7; }
.markdown-content h1, .markdown-content h2, .markdown-content h3 { color: #111827 !important; margin-top: 1.2em !important; margin-bottom: 0.6em !important; font-weight: 600; }
.markdown-content h1 { font-size: 1.6em !important; border-bottom: 1px solid #e5e7eb; padding-bottom: 0.3em; }
.markdown-content h2 { font-size: 1.4em !important; border-bottom: 1px solid #e5e7eb; padding-bottom: 0.3em;}
.markdown-content h3 { font-size: 1.2em !important; }
.markdown-content a { color: #2563eb !important; text-decoration: none !important; transition: all 0.2s; }
.markdown-content a:hover { color: #1d4ed8 !important; text-decoration: underline !important; }
.markdown-content code { background: #f3f4f6 !important; padding: 2px 6px !important; border-radius: 4px !important; font-family: monospace !important; color: #4b5563; font-size: 0.9em; }
.markdown-content pre { background: #f3f4f6 !important; padding: 12px !important; border-radius: 8px !important; overflow-x: auto !important; border: 1px solid #e5e7eb;}
.markdown-content pre code { background: transparent !important; padding: 0 !important; border: none !important; font-size: 0.9em;}
.markdown-content blockquote { border-left: 4px solid #d1d5db !important; padding-left: 1em !important; margin-left: 0 !important; color: #6b7280 !important; }
.markdown-content table { border-collapse: collapse !important; width: 100% !important; margin: 1em 0; }
.markdown-content th, .markdown-content td { padding: 8px 12px !important; border: 1px solid #d1d5db !important; text-align: left;}
.markdown-content th { background: #f9fafb !important; font-weight: 600; }
.accordion { background: #f9fafb !important; border: 1px solid #e5e7eb !important; border-radius: 8px !important; margin-top: 1rem !important; box-shadow: none !important; }
.accordion > .label-wrap { padding: 10px 15px !important; }
.voice-selector { margin: 0; padding: 0; height: 100%; }
.voice-selector div[data-testid="dropdown"] { height: 100% !important; border-radius: 0 !important;}
.voice-selector select { background: white !important; color: #374151 !important; border: 1px solid #d1d5db !important; border-left: none !important; border-right: none !important; border-radius: 0 !important; height: 100% !important; padding: 0 10px !important; transition: all 0.2s; appearance: none !important; -webkit-appearance: none !important; background-image: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' fill='none' viewBox='0 0 20 20'%3e%3cpath stroke='%236b7280' stroke-linecap='round' stroke-linejoin='round' stroke-width='1.5' d='M6 8l4 4 4-4'/%3e%3c/svg%3e") !important; background-position: right 0.5rem center !important; background-repeat: no-repeat !important; background-size: 1.5em 1.5em !important; padding-right: 2.5rem !important; }
.voice-selector select:focus { border-color: #2563eb !important; box-shadow: none !important; z-index: 1; position: relative;}
.audio-player { margin-top: 1rem; background: #f9fafb !important; border-radius: 8px !important; padding: 0.5rem !important; border: 1px solid #e5e7eb;}
.audio-player audio { width: 100% !important; }
.searching, .error { padding: 1rem; border-radius: 8px; text-align: center; margin: 1rem 0; border: 1px dashed; }
.searching { background: #eff6ff; color: #3b82f6; border-color: #bfdbfe; }
.error { background: #fef2f2; color: #ef4444; border-color: #fecaca; }
.no-sources { padding: 1rem; text-align: center; color: #6b7280; background: #f9fafb; border-radius: 8px; border: 1px solid #e5e7eb;}
@keyframes pulse { 0% { opacity: 0.7; } 50% { opacity: 1; } 100% { opacity: 0.7; } }
.searching span { animation: pulse 1.5s infinite ease-in-out; display: inline-block; }
/* Dark Mode Styles (Optional - keep if needed) */
.dark .gradio-container { background-color: #111827 !important; }
.dark #header { background: linear-gradient(135deg, #1f2937, #374151); }
.dark #header h3 { color: #9ca3af; }
.dark .search-container { background: #1f2937; border-color: #374151; }
.dark .search-box input[type="text"] { background: #374151 !important; border-color: #4b5563 !important; color: #e5e7eb !important; }
.dark .search-box input[type="text"]:focus { border-color: #3b82f6 !important; background: #4b5563 !important; box-shadow: 0 0 0 2px rgba(59, 130, 246, 0.3) !important; }
.dark .search-box input[type="text"]::placeholder { color: #9ca3af !important; }
.dark .search-box button { background: #3b82f6 !important; }
.dark .search-box button:hover { background: #2563eb !important; }
.dark .search-box button:disabled { background: #4b5563 !important; }
.dark .answer-box { background: #1f2937; border-color: #374151; color: #e5e7eb; }
.dark .answer-box p { color: #d1d5db; }
.dark .answer-box code { background: #374151; color: #9ca3af; }
.dark .sources-box { background: #1f2937; border-color: #374151; }
.dark .sources-box h3 { color: #f9fafb; }
.dark .source-item { border-bottom-color: #374151; }
.dark .source-item:hover { background-color: #374151; }
.dark .source-number { color: #9ca3af; }
.dark .source-title { color: #60a5fa; }
.dark .source-title:hover { color: #93c5fd; }
.dark .source-snippet { color: #d1d5db; }
.dark .chat-history { background: #374151; border-color: #4b5563; scrollbar-color: #4b5563 #374151; color: #d1d5db;}
.dark .chat-history::-webkit-scrollbar-track { background: #374151; }
.dark .chat-history::-webkit-scrollbar-thumb { background-color: #4b5563; }
.dark .examples-container { background: #374151; border-color: #4b5563; }
.dark .examples-container button { background: #1f2937 !important; border-color: #4b5563 !important; color: #d1d5db !important; }
.dark .examples-container button:hover { background: #4b5563 !important; border-color: #6b7280 !important; }
.dark .markdown-content { color: #d1d5db !important; }
.dark .markdown-content h1, .dark .markdown-content h2, .dark .markdown-content h3 { color: #f9fafb !important; border-bottom-color: #4b5563; }
.dark .markdown-content a { color: #60a5fa !important; }
.dark .markdown-content a:hover { color: #93c5fd !important; }
.dark .markdown-content code { background: #374151 !important; color: #9ca3af; }
.dark .markdown-content pre { background: #374151 !important; border-color: #4b5563;}
.dark .markdown-content pre code { background: transparent !important; }
.dark .markdown-content blockquote { border-left-color: #4b5563 !important; color: #9ca3af !important; }
.dark .markdown-content th, .dark .markdown-content td { border-color: #4b5563 !important; }
.dark .markdown-content th { background: #374151 !important; }
.dark .accordion { background: #374151 !important; border-color: #4b5563 !important; }
.dark .accordion > .label-wrap { color: #d1d5db !important; }
.dark .voice-selector select { background: #1f2937 !important; color: #d1d5db !important; border-color: #4b5563 !important; background-image: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' fill='none' viewBox='0 0 20 20'%3e%3cpath stroke='%239ca3af' stroke-linecap='round' stroke-linejoin='round' stroke-width='1.5' d='M6 8l4 4 4-4'/%3e%3c/svg%3e") !important;}
.dark .voice-selector select:focus { border-color: #3b82f6 !important; }
.dark .audio-player { background: #374151 !important; border-color: #4b5563;}
.dark .audio-player audio::-webkit-media-controls-panel { background-color: #374151; }
.dark .audio-player audio::-webkit-media-controls-play-button { color: #d1d5db; }
.dark .audio-player audio::-webkit-media-controls-current-time-display { color: #9ca3af; }
.dark .audio-player audio::-webkit-media-controls-time-remaining-display { color: #9ca3af; }
.dark .searching { background: #1e3a8a; color: #93c5fd; border-color: #3b82f6; }
.dark .error { background: #7f1d1d; color: #fca5a5; border-color: #ef4444; }
.dark .no-sources { background: #374151; color: #9ca3af; border-color: #4b5563;}
"""

import sys # Needed for sys.path manipulation in TTS setup

with gr.Blocks(title="AI Search Assistant", css=css, theme=gr.themes.Default(primary_hue="blue")) as demo:
    # Use gr.State to store the chat history in the 'messages' format
    chat_history_state = gr.State([])

    with gr.Column(): # Main container
        # Header
        with gr.Column(elem_id="header"):
            gr.Markdown("# 🔍 AI Search Assistant")
            gr.Markdown("### Powered by DeepSeek & Real-time Web Results with Voice")

        # Search Area
        with gr.Column(elem_classes="search-container"):
            with gr.Row(elem_classes="search-box", equal_height=False):
                search_input = gr.Textbox(label="", placeholder="Ask anything...", scale=5, container=False)
                voice_select = gr.Dropdown(choices=list(VOICE_CHOICES.keys()), value=list(VOICE_CHOICES.keys())[0], label="", scale=1, min_width=180, container=False, elem_classes="voice-selector")
                search_btn = gr.Button("Search", variant="primary", scale=0, min_width=100)

            # Results Area
            with gr.Row(elem_classes="results-container", equal_height=False):
                # Left Column: Answer & History
                with gr.Column(scale=3):
                    # Chatbot display (uses 'messages' format now)
                    chatbot_display = gr.Chatbot(
                        label="Conversation",
                        bubble_full_width=True,
                        height=500,
                        elem_classes="chat-history",
                        type="messages", # Use the recommended type
                        avatar_images=(None, os.path.join(KOKORO_PATH, "icon.png") if os.path.exists(os.path.join(KOKORO_PATH, "icon.png")) else None) # Optional: Add avatar for assistant
                    )
                    # Separate Markdown for status/intermediate answer
                    answer_status_output = gr.Markdown(value="*Enter a query to start.*", elem_classes="answer-box markdown-content")
                    # Audio Output
                    audio_player = gr.Audio(label="Voice Response", type="numpy", autoplay=False, show_label=False, elem_classes="audio-player")

                # Right Column: Sources
                with gr.Column(scale=2):
                    with gr.Column(elem_classes="sources-box"):
                        gr.Markdown("### Sources")
                        sources_output_html = gr.HTML(value="<div class='no-sources'>Sources will appear here.</div>")

            # Examples Area
            with gr.Row(elem_classes="examples-container"):
                 # REMOVED elem_classes from gr.Examples
                 gr.Examples(
                    examples=[
                        "Latest news about renewable energy",
                        "Explain Large Language Models (LLMs)",
                        "Symptoms and prevention tips for the flu",
                        "Compare Python and JavaScript for web development",
                        "Summarize the main points of the Paris Agreement",
                    ],
                    inputs=search_input,
                    label="Try these examples:",
                )

    # --- Event Handling Setup ---
    # Define the inputs and outputs for the Gradio event triggers
    event_inputs = [search_input, chat_history_state, voice_select]
    event_outputs = [
        chatbot_display,        # Updated chat history
        answer_status_output,   # Status or final answer text
        sources_output_html,    # Formatted sources
        audio_player,           # Audio data
        search_btn              # Button state (enabled/disabled)
    ]

    # Create a wrapper to adapt the async generator for Gradio's streaming updates
    async def stream_interaction_updates(query, history, voice_display_name):
         try:
             # Iterate through the states yielded by the handler
             async for state_update in handle_interaction(query, history, voice_display_name):
                 yield state_update # Yield the tuple of output values
         except Exception as e:
            print(f"[Gradio Stream] Error during interaction: {e}")
            print(traceback.format_exc())
            # Yield a final error state to the UI
            error_history = history + [{"role":"user", "content":query}, {"role":"assistant", "content":f"*Error: {e}*"}]
            yield (
                error_history,
                f"An error occurred: {e}",
                "<div class='error'>Request failed.</div>",
                None,
                gr.Button(value="Search", interactive=True)
            )
         finally:
            # Clear the text input after processing is complete (or errored out)
            # We need to yield the final state *plus* the cleared input
            # This requires adding search_input to the outputs list for the event triggers
            # For now, let's not clear it automatically to avoid complexity.
            # yield (*final_state_tuple, gr.Textbox(value="")) # Example if clearing input
             print("[Gradio Stream] Interaction stream finished.")


    # Connect the streaming function to the button click and input submit events
    search_btn.click(
        fn=stream_interaction_updates,
        inputs=event_inputs,
        outputs=event_outputs
    )
    search_input.submit(
        fn=stream_interaction_updates,
        inputs=event_inputs,
        outputs=event_outputs
    )

if __name__ == "__main__":
    print("Starting Gradio application...")
    demo.queue(max_size=20).launch(
        debug=True,
        share=True,
        # server_name="0.0.0.0" # Optional: Bind to all interfaces
    )