import gradio as gr
from faster_whisper import WhisperModel
from transformers import AutoTokenizer, AutoModelForCausalLM
from duckduckgo_search import DDGS
import time
import torch
import base64
import tempfile
import os
from datetime import datetime

# Initialize models
print("Loading Whisper model...")
whisper_model = WhisperModel("tiny", device="cpu", compute_type="int8")

print("Loading LLM...")
model_name = "Qwen/Qwen2.5-1.5B-Instruct"  # Upgraded to 1.5B for better quality
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float32,
    device_map="cpu",
    low_cpu_mem_usage=True
)

# Initialize DuckDuckGo Search
ddgs = DDGS(timeout=3)

def search_web(query, max_results=3):
    """Perform web search using DuckDuckGo"""
    try:
        results = ddgs.text(
            keywords=query,
            region='wt-wt',
            safesearch='moderate',
            timelimit='m',
            max_results=max_results
        )
        
        context = ""
        for i, result in enumerate(results[:max_results], 1):
            title = result.get('title', '')
            body = result.get('body', '')
            context += f"\n[Source {i}] {title}\n{body}\n"
        
        return context.strip() if context else "No search results found."
    
    except Exception as e:
        return f"Search failed: {str(e)}"

def transcribe_audio_base64(audio_base64):
    """Transcribe audio from base64 string (for Pluely STT endpoint)"""
    try:
        audio_bytes = base64.b64decode(audio_base64)
        
        with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_audio:
            temp_audio.write(audio_bytes)
            temp_path = temp_audio.name
        
        segments, _ = whisper_model.transcribe(temp_path, language="en", beam_size=1)
        transcription = " ".join([seg.text for seg in segments])
        
        os.unlink(temp_path)
        
        return {"text": transcription.strip()}
    
    except Exception as e:
        return {"error": f"Transcription failed: {str(e)}"}

def generate_answer(text_input):
    """Generate complete answer with context"""
    try:
        if not text_input or text_input.strip() == "":
            return "No input provided"
        
        # Get current date for context
        current_date = datetime.now().strftime("%B %d, %Y")
        
        # Web search for current information
        search_results = search_web(text_input, max_results=3)
        
        # Enhanced prompt for comprehensive responses
        messages = [
            {"role": "system", "content": f"""You are a knowledgeable assistant providing comprehensive, well-researched answers. Today's date is {current_date}.

When answering:
1. Provide the direct answer first
2. Add relevant context and background information
3. Include recent developments or current status when applicable
4. Be informative but concise (150-200 words)
5. Use the web search results to ensure accuracy and currency"""},
            {"role": "user", "content": f"""Based on these current web search results:

{search_results}

Question: {text_input}

Provide a comprehensive answer that includes:
- Direct answer to the question
- Relevant context and background
- Recent developments (as of {current_date})
- Key points the user should know

Answer:"""}
        ]
        
        text = tokenizer.apply_chat_template(
            messages,
            tokenize=False,
            add_generation_prompt=True
        )
        
        inputs = tokenizer([text], return_tensors="pt").to("cpu")
        
        with torch.no_grad():
            outputs = model.generate(
                **inputs,
                max_new_tokens=250,  # Increased from 80 to 250
                temperature=0.3,     # Slightly higher for more natural responses
                do_sample=True,
                top_p=0.9,
                repetition_penalty=1.1,
                pad_token_id=tokenizer.eos_token_id
            )
        
        response = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
        return response.strip()
        
    except Exception as e:
        return f"Error: {str(e)}"

def process_audio(audio_path, question_text):
    """Main pipeline - returns tuple (answer, time)"""
    start_time = time.time()
    
    # Transcribe if audio provided
    if audio_path:
        try:
            segments, _ = whisper_model.transcribe(audio_path, language="en", beam_size=1)
            question = " ".join([seg.text for seg in segments])
        except Exception as e:
            return f"❌ Transcription error: {str(e)}", 0.0
    else:
        question = question_text
    
    if not question or question.strip() == "":
        return "❌ No input provided", 0.0
    
    transcription_time = time.time() - start_time
    
    # Web search
    search_start = time.time()
    search_results = search_web(question, max_results=3)
    search_time = time.time() - search_start
    
    # Generate answer
    llm_start = time.time()
    answer = generate_answer(question)
    llm_time = time.time() - llm_start
    
    total_time = time.time() - start_time
    time_emoji = "🟢" if total_time < 5.0 else "🟡" if total_time < 7.0 else "🔴"
    
    timing_info = f"\n\n{time_emoji} **Performance:** Trans={transcription_time:.2f}s | Search={search_time:.2f}s | LLM={llm_time:.2f}s | **Total={total_time:.2f}s**"
    
    return answer + timing_info, total_time

# Wrapper functions
def audio_handler(audio_path):
    """Wrapper for audio input"""
    return process_audio(audio_path, None)

def text_handler(text_input):
    """Wrapper for text input"""
    return process_audio(None, text_input)

# Gradio interface
with gr.Blocks(title="Enhanced Political Q&A", theme=gr.themes.Soft()) as demo:
    gr.Markdown("""
    # 🎯 Enhanced Political Q&A System
    **Comprehensive answers with context** - Powered by Qwen2.5-1.5B
    
    **Features:** Whisper-tiny + Qwen2.5-1.5B + DuckDuckGo + Rich contextual responses
    """)
    
    with gr.Tab("🎙️ Audio Input"):
        with gr.Row():
            with gr.Column():
                audio_input = gr.Audio(
                    sources=["microphone", "upload"],
                    type="filepath",
                    label="Record or upload audio"
                )
                audio_submit = gr.Button("🚀 Submit Audio", variant="primary", size="lg")
            
            with gr.Column():
                audio_output = gr.Textbox(label="Comprehensive Answer", lines=12, show_copy_button=True)
                audio_time = gr.Number(label="Response Time (seconds)", precision=2)
        
        audio_submit.click(
            fn=audio_handler,
            inputs=[audio_input],
            outputs=[audio_output, audio_time],
            api_name="audio_query"
        )
    
    with gr.Tab("✍️ Text Input"):
        with gr.Row():
            with gr.Column():
                text_input = gr.Textbox(
                    label="Type your question",
                    placeholder="Who is the current US president?",
                    lines=3
                )
                text_submit = gr.Button("🚀 Submit Text", variant="primary", size="lg")
            
            with gr.Column():
                text_output = gr.Textbox(label="Comprehensive Answer", lines=12, show_copy_button=True)
                text_time = gr.Number(label="Response Time (seconds)", precision=2)
        
        text_submit.click(
            fn=text_handler,
            inputs=[text_input],
            outputs=[text_output, text_time],
            api_name="text_query"
        )
        
        gr.Examples(
            examples=[
                ["Who won the 2024 US presidential election?"],
                ["What is the current inflation rate in India?"],
                ["Who is the prime minister of UK and what are their key policies?"],
                ["Explain the latest developments in AI regulation"]
            ],
            inputs=text_input
        )
    
    # API endpoints for Pluely
    with gr.Tab("🔌 Pluely Integration"):
        gr.Markdown("""
        ## API Endpoints for Pluely
        
        ### STT Endpoint (Audio Transcription)
        ```
        curl -X POST https://archcoder-basic-app.hf.space/call/transcribe_stt \\
          -H "Content-Type: application/json" \\
          -d '{"data": ["BASE64_AUDIO_DATA"]}'
        ```
        **Response:** `{"data": [{"text": "transcribed text"}]}`
        
        ### AI Endpoint (Enhanced Responses)
        ```
        curl -X POST https://archcoder-basic-app.hf.space/call/answer_ai \\
          -H "Content-Type: application/json" \\
          -d '{"data": ["Your question here"]}'
        ```
        **Response:** `{"data": ["Comprehensive answer with context"]}`
        
        ## Pluely Configuration
        
        ### Custom STT Provider:
        ```
        curl https://archcoder-basic-app.hf.space/call/transcribe_stt -H "Content-Type: application/json" -d '{"data": ["{{AUDIO_BASE64}}"]}'
        ```
        **Response Path:** `data[0].text` | **Streaming:** OFF
        
        ### Custom AI Provider:
        ```
        curl https://archcoder-basic-app.hf.space/call/answer_ai -H "Content-Type: application/json" -d '{"data": ["{{TEXT}}"]}'
        ```
        **Response Path:** `data[0]` | **Streaming:** OFF
        """)
        
        # Hidden components for API endpoints
        with gr.Row(visible=False):
            stt_input = gr.Textbox()
            stt_output = gr.JSON()
            ai_input = gr.Textbox()
            ai_output = gr.Textbox()
        
        stt_btn = gr.Button("STT", visible=False)
        stt_btn.click(
            fn=transcribe_audio_base64,
            inputs=[stt_input],
            outputs=[stt_output],
            api_name="transcribe_stt"
        )
        
        ai_btn = gr.Button("AI", visible=False)
        ai_btn.click(
            fn=generate_answer,
            inputs=[ai_input],
            outputs=[ai_output],
            api_name="answer_ai"
        )
    
    gr.Markdown("""
    ---
    **Model:** Qwen2.5-1.5B-Instruct (3x larger for better answers)  
    **Output:** 150-200 words with context and background  
    **Date-aware:** Responses reference current date ({})  
    
    🟢 = Under 5s | 🟡 = 5-7s | 🔴 = Over 7s
    """.format(datetime.now().strftime("%B %d, %Y")))

if __name__ == "__main__":
    demo.queue(max_size=5)
    demo.launch()