app.py

import gradio as gr
import uuid
import subprocess
import time
import asyncio
import re

USER_ID = str(uuid.uuid4())

# Iniciar o servidor MCP em background
# subprocess.Popen(["python", "mcp_players_table_sql.py"])  
# subprocess.Popen(["python", "mcp_team_table_sql.py"])     
# subprocess.Popen(["python", "mcp_one_player_supabase.py"])     
# time.sleep(3)
# subprocess.Popen(["python", "mcp_graph_server.py"])   
# time.sleep(3)

import pandas as pd

# Caminho do CSV já presente no repositório do Space (coloque seu arquivo em /, ou numa pasta).
CSV_PATH = "barca.csv"  # ajuste para o seu caminho real

# Carregamento único no startup
try:
    _df_full = pd.read_csv(CSV_PATH)
    _df_preview = _df_full.head(100)
    _df_meta = f"Source: Author • Rows: {len(_df_full)} • Columns: {len(_df_full.columns)}"
except Exception as e:
    _df_full = None
    _df_preview = None
    _df_meta = f"Erro ao carregar CSV em startup: {e}"

#from place_holder_image import PLACEHOLDER_IMAGE, get_current_chart
from main_agent import stream_agent_response_safe, agent_conventional_response
from utils import CUSTOM_CSS

async def simulate_streaming_adaptive(full_text: str):
    """Streaming adaptativo com delays diferentes para pontuação"""
    words = re.findall(r'\S+\s*', full_text)
    current_text = ""
    
    for word in words:
        current_text += word
        
        if word.strip().endswith(('.', '!', '?')):
            delay = 0.15
        elif word.strip().endswith((',', ';', ':')):
            delay = 0.08
        else:
            delay = 0.04
        
        await asyncio.sleep(delay)
        yield current_text

async def respond(message, history):
    """Função de resposta com streaming + atualização de gráfico"""
    message = str(message)
    print(f"Message received: {message}")
    
    try:
        print("Obtendo resposta completa do agente...")
        full_response = await stream_agent_response_safe(message)
        print(f"Resposta obtida: {len(full_response)} caracteres")
        
        # Simular streaming da resposta
        async for text_chunk in simulate_streaming_adaptive(full_response):
            yield text_chunk
            
    except Exception as e:
        print(f"Erro durante o processamento: {e}")
        import traceback
        traceback.print_exc()
        yield f"❌ Erro: {str(e)}", get_current_chart()

# def refresh_chart():
#     """Atualiza a visualização do gráfico"""
#     return get_current_chart()

if __name__ == "__main__":
    with gr.Blocks(
        title="Barcelona Analytics Platform",
        theme=gr.themes.Soft(
            primary_hue="blue",
            secondary_hue="slate",
            neutral_hue="slate",
            font=[gr.themes.GoogleFont("Inter"), "sans-serif"],
            font_mono=[gr.themes.GoogleFont("JetBrains Mono"), "monospace"]
        ),
        css=CUSTOM_CSS
    ) as demo:
        
        gr.Markdown(
            """
            ## Data Analyst Agent with FC Barcelona Statistics (2020/2021)
            """
        )
        
        with gr.Tabs() as tabs:
            # ABA 1: ANALYSIS & CHAT
            with gr.Tab("Chat", id=0, scale=0):
                #gr.Markdown("### Intelligent Assistant")
                
                chatbot = gr.Chatbot(
                    type="messages",
                    label="",
                    height=400,
                    show_copy_button=True,
                    scale=0
                )
                
                with gr.Row():
                    msg = gr.Textbox(
                        placeholder="Ask about players, matches, or request visualizations...",
                        label="Your Query",
                        lines=2,
                        scale=4,
                        show_label=False
                    )
                    submit_btn = gr.Button("Send", variant="primary", scale=1, size="lg")
                
                with gr.Accordion("📋 Query Examples", open=False):
                    gr.Examples(
                        examples=[
                            "Return the top 10 players by total xG and assists combined",
                            "Create a bar chart showing the top 5 players with most passes",
                            "Find players with at least 900 minutes who rank in the top 5% for progressive passes and top 10% for xG assisted in 2020/2021, returning player, minutes, prog_passes, xA, and z-scores"
                        ],
                        inputs=msg,
                        label=""
                    )
            
            # ABA 2: DATA VISUALIZATION
            # with gr.Tab("Generated Chart", id=1):
            #     gr.Markdown(
            #         """
            #         ### Interactive Visualizations
                    
            #         Charts and graphs generated by the AI assistant will appear here. 
            #         Request visualizations in the Analysis tab to see them rendered.
            #         """
            #     )
                
            #     chart_display = gr.Image(
            #         value=PLACEHOLDER_IMAGE,
            #         label="",
            #         type="pil",
            #         height=360,
            #         show_label=False,
            #         show_download_button=True,
            #         show_share_button=False
            #     )
                
            #     with gr.Row():
            #         refresh_btn = gr.Button("🔄 Refresh Visualization", variant="secondary", size="lg")
            #         gr.Markdown("_Last updated: Live_", elem_classes="status-badge")
                    
            with gr.Tab("CSV Source Data", id=2):
                gr.Markdown("### Data consulted by the Agent\nStatic view (100 lines) from preloaded dataset.", elem_classes=["section-title"])
                meta = gr.Markdown(value=_df_meta)
                df_view = gr.Dataframe(
                    value=_df_preview,
                    label="Preview - 100 lines",
                    wrap=False,
                    interactive=False
                )
        
        # Footer informativo
        gr.Markdown(
            """
            ---
            **Data Source:** SQL Database • **AI Model:** Groq-powered Analysis • **Coverage:** 1 Seasons (2020-2021)
            """
        )
        
        async def respond_and_update(message, history):
            """Responde e retorna tanto o chat quanto o gráfico atualizado"""
            if not message.strip():
                yield history
                return
            
            # Adicionar mensagem do usuário
            history.append(gr.ChatMessage(role="user", content=message))
            
            try:
                # Obter resposta
                # full_response = await asyncio.wait_for(
                #     stream_agent_response_safe(message),
                #     timeout=120.0
                # )
                
                full_response = await agent_conventional_response(message)
                
                # Adicionar mensagem inicial do assistente
                history.append(gr.ChatMessage(role="assistant", content=""))
                
                # Streaming adaptativo
                async for text_chunk in simulate_streaming_adaptive(full_response):
                    history[-1] = gr.ChatMessage(role="assistant", content=text_chunk)
                    yield history
            
            except asyncio.TimeoutError:
                history.append([message, "⏱️ Timeout: consulta demorou muito"])
                yield history, get_current_chart()
                
            except Exception as e:
                print(f"Erro: {e}")
                import traceback
                traceback.print_exc()
                history.append(gr.ChatMessage(role="assistant", content=f"⚠️ Error: {str(e)}"))
                yield history
        
        # Eventos
        submit_btn.click(
            fn=respond_and_update,
            inputs=[msg, chatbot],
            outputs=[chatbot]
        ).then(
            lambda: "",
            None,
            [msg]
        )
        
        msg.submit(
            fn=respond_and_update,
            inputs=[msg, chatbot],
            outputs=[chatbot]
        ).then(
            lambda: "",
            None,
            [msg]
        )
        
        # refresh_btn.click(
        #     fn=refresh_chart,
        #     inputs=[],
        #     outputs=[chart_display]
        # )
    
    demo.launch(ssr_mode=False, share=False)