import os
import gradio as gr
import pandas as pd
import comtradeapicall
import spaces
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
from deep_translator import GoogleTranslator
import torch

# کلید COMTRADE
subscription_key = os.getenv("COMTRADE_API_KEY", "")
# توکن Hugging Face
hf_token = os.getenv("HF_API_TOKEN")

# تنظیم کوانت‌سازی
quantization_config = BitsAndBytesConfig(load_in_4bit=True)

# بارگذاری توکنایزر و مدل
tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b", token=hf_token)
model = AutoModelForCausalLM.from_pretrained(
    "google/gemma-7b",
    token=hf_token,
    quantization_config=quantization_config,
    device_map="auto",
    torch_dtype=torch.float16
)

# تابع دریافت اطلاعات واردکنندگان
def get_importers(hs_code: str, year: str, month: str):
    period = f"{year}{int(month):02d}"
    df = comtradeapicall.previewFinalData(
        typeCode='C', freqCode='M', clCode='HS', period=period,
        reporterCode=None, cmdCode=hs_code, flowCode='M',
        partnerCode=None, partner2Code=None,
        customsCode=None, motCode=None,
        maxRecords=500, includeDesc=True
    )
    if df is None or df.empty:
        return pd.DataFrame(columns=["کد کشور", "نام کشور", "ارزش CIF"])
    df = df[df['cifvalue'] > 0]
    result = (
        df.groupby(["reporterCode", "reporterDesc"], as_index=False)
          .agg({"cifvalue": "sum"})
          .sort_values("cifvalue", ascending=False)
    )
    result.columns = ["کد کشور", "نام کشور", "ارزش CIF"]
    return result

# تابع ارائه مشاوره با استفاده از GPU
@spaces.GPU(duration=120)
def provide_advice(table_data: pd.DataFrame, hs_code: str, year: str, month: str):
    if table_data is None or table_data.empty:
        return "ابتدا باید اطلاعات واردات را نمایش دهید."
    
    table_str = table_data.to_string(index=False)
    period = f"{year}/{int(month):02d}"
    prompt = (
        f"The following table shows countries that imported a product with HS code {hs_code} during the period {period}:\n"
        f"{table_str}\n\n"
        f"Please provide a detailed and comprehensive analysis in two paragraphs. The first paragraph should discuss market opportunities, potential demand, and specific cultural or economic factors influencing the demand for this product in these countries. The second paragraph should offer actionable strategic recommendations for exporters, including detailed trade strategies, risk management techniques, and steps to establish local partnerships."
    )
    print("پرامپت ساخته‌شده:")
    print(prompt)

    try:
        input_ids = tokenizer(prompt, return_tensors="pt").to("cuda")
        outputs = model.generate(
            **input_ids,
            max_new_tokens=1024,
            do_sample=True,
            temperature=0.7,
            top_p=0.9
        )
        generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
        print("خروجی مدل دریافت شد (به انگلیسی):")
        print(generated_text)

        translated_outputs = translator.translate(generated_text)
        print("خروجی ترجمه‌شده به فارسی:")
        print(translated_outputs)
        return translated_outputs
    except Exception as e:
        error_msg = f"خطا در تولید مشاوره: {str(e)}"
        print(error_msg)
        return error_msg

# تنظیمات رابط Gradio
current_year = pd.Timestamp.now().year
years = [str(y) for y in range(2000, current_year+1)]
months = [str(m) for m in range(1, 13)]

with gr.Blocks() as demo:
    gr.Markdown("##تولید شده توسط DIGINORON نمایش کشورهایی که یک کالا را وارد کرده‌اند")
    with gr.Row():
        inp_hs = gr.Textbox(label="HS Code")
        inp_year = gr.Dropdown(choices=years, label="سال", value=str(current_year))
        inp_month = gr.Dropdown(choices=months, label="ماه", value=str(pd.Timestamp.now().month))
    btn_show = gr.Button("نمایش اطلاعات")
    out_table = gr.Dataframe(
        headers=["کد کشور", "نام کشور", "ارزش CIF"],
        datatype=["number", "text", "number"],
        interactive=True,
    )
    btn_show.click(get_importers, [inp_hs, inp_year, inp_month], out_table)

    btn_advice = gr.Button("ارائه مشاوره تخصصی")
    out_advice = gr.Textbox(label="مشاوره تخصصی", lines=6)

    btn_advice.click(
        provide_advice,
        inputs=[out_table, inp_hs, inp_year, inp_month],
        outputs=out_advice
    )

if __name__ == "__main__":
    demo.launch()