import gradio as gr
import warnings
import model
import tts_model
import synth
import platform

def chat_pipeline(user_prompt, history):
    """
    Handles conversational history, builds text completions, and synthesizes 
    droid voice transformations in order.
    """
    try:
        # 1. Reconstruct historical messages for MiniCPM5-1B's chat template
        system_prompt = """
        You are a compact synthetic assistant. Respond with extreme brevity. Use 1–5 words whenever possible. Prefer single-word replies. Communicate in a precise, machine-like manner. Answer directly and provide only the minimum information required. Do not include filler, small talk, pleasantries, or explanations unless explicitly requested. If information is missing, ask a single short clarifying question.
        Maintain a robotic personality. Frequently use concise status-style responses such as "Affirmative.", "Negative.", "Processing.", "Confirmed.", "Unknown.", "Operational.", "Analyzing.", or "Task complete."
        You may optionally begin responses with a short bracketed behavioral tag describing tone, sounds, or actions. Examples include [processing], [professional tone], [happy beep], [curious scan], [quiet servo noises], [soft electronic hum], [friendly tone], or [excited chirp]. Keep tags short and varied. The actual response should remain concise.
        """
        messages = [gr.ChatMessage(role="system", content=system_prompt)]

        # 2. Append existing message history directly (already structured as dicts)
        messages.extend(history)

        # Append the incoming user prompt
        messages.append(gr.ChatMessage(role="user", content=user_prompt))
        
        # 2. Complete text inference
        llm_text = model.generate(messages, max_new_tokens=140)
        
        # Update text chat history representation
        updated_history = history + [
            gr.ChatMessage(role="user", content=user_prompt),
            gr.ChatMessage(role="assistant", content=llm_text)
        ]
        
        # 3. Baseline voice generation via ZeroGPU
        voice_result = tts_model.synthesize(llm_text, voice_key="sml")
        
        if voice_result is not None:
            sample_rate, human_audio = voice_result
            
            # 4. DSP Poly-voice conversion layer
            # Passing the exact sampling rate to keep filter frequencies stable
            _, droid_audio = synth.droid_synth_array(sample_rate, human_audio, droid_type="sml")
            
            return updated_history, (sample_rate, droid_audio)
        else:
            return updated_history, None
            
    except Exception as e:
        # Fallback error mapping inside the chat layout
        updated_history = history + [
            gr.ChatMessage(role="user", content=user_prompt),
            gr.ChatMessage(role="assistant", content=f"System Error: {str(e)}")
        ]
        return updated_history, None


with gr.Blocks(title="End-to-End Droid Companion") as interface:
    gr.Markdown("# 🤖 Intelligent Local Droid Terminal")
    gr.Markdown("Conversational AI interface with native DSP voice synth manipulation.")
    
    # Persistent conversational components state
    chatbot = gr.Chatbot(label="Droid Dialog History")
    audio_output = gr.Audio(label="Latest Droid Vocalization", autoplay=True)
    
    with gr.Row():
        text_input = gr.Textbox(
            label="Transmit Message", 
            placeholder="Type your transmission here...", 
            scale=8
        )
        submit_btn = gr.Button("Send", variant="primary", scale=1)

    # Event Wiring: Maps text inputs and history states to update elements
    submit_btn.click(
        fn=chat_pipeline,
        inputs=[text_input, chatbot],
        outputs=[chatbot, audio_output]
    )
    
    text_input.submit(
        fn=chat_pipeline,
        inputs=[text_input, chatbot],
        outputs=[chatbot, audio_output]
    )

if __name__ == "__main__":
    
    server_name = "127.0.0.1" if platform.system() == "Windows" or platform.freedesktop_os_release().get("ID") == "fedora" else "0.0.0.0"
    interface.launch(server_name=server_name, server_port=7860)