app.py

import os
import gradio as gr
import pandas as pd
import comtradeapicall
from huggingface_hub import InferenceClient
from deep_translator import GoogleTranslator

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

client = InferenceClient(token=hf_token)
translator = GoogleTranslator(source='en', target='fa')

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"]), "برنج"
    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"]
    
    product_name = df['cmdDesc'].iloc[0] if 'cmdDesc' in df.columns else "برنج"
    return result, product_name

def provide_advice(table_data: pd.DataFrame, hs_code: str, year: str, month: str, product_name: 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 the product '{product_name}' 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:
        print("در حال فراخوانی مدل meta-llama/Llama-2-7b-chat...")
        outputs = client.text_generation(
            prompt=prompt,
            model="meta-llama/Llama-2-7b-chat",  # مدل جدید
            max_new_tokens=1024
        )
        print("خروجی مدل دریافت شد (به انگلیسی):")
        print(outputs)

        # ترجمه خروجی به فارسی
        translated_outputs = translator.translate(outputs)
        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

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("## نمایش کشورهایی که یک کالا را وارد کرده‌اند")
    with gr.Row():
        inp_hs = gr.Textbox(label="HS Code", value="1006")
        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(
        fn=get_importers,
        inputs=[inp_hs, inp_year, inp_month],
        outputs=[out_table, gr.State()]
    )

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

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

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