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app.py
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# app.py
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import os
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import pandas as pd
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import gradio as gr
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import comtradeapicall
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from huggingface_hub import InferenceClient
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from deep_translator import GoogleTranslator
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import spaces # برای مدیریت GPU کرایهای
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# --- بارگذاری HS DATA از CSV گیتهاب ---
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HS_CSV_URL = (
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"https://raw.githubusercontent.com/"
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"datasets/harmonized-system/master/data/harmonized-system.csv"
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)
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hs_df = pd.read_csv(HS_CSV_URL, dtype=str)
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def get_product_name(hs_code: str) -> str:
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code4 = str(hs_code).zfill(4)
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row = hs_df[hs_df["hscode"] == code4]
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return row.iloc[0]["description"] if not row.empty else "–"
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# --- تابع دریافت واردات و پردازش ستونها ---
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def get_importers(hs_code: str, year: str, month: str):
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product_name = get_product_name(hs_code)
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period = f"{year}{int(month):02d}"
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df = comtradeapicall.previewFinalData(
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typeCode='C', freqCode='M', clCode='HS', period=period,
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reporterCode=None, cmdCode=hs_code, flowCode='M',
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partnerCode=None, partner2Code=None,
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customsCode=None, motCode=None,
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maxRecords=500, includeDesc=True
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)
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if df is None or df.empty:
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return product_name, pd.DataFrame()
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# شناسایی ستونهای مورد نیاز
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code_col = next((c for c in df.columns if 'code' in c.lower()), None)
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title_col = next((c for c in df.columns if 'title' in c.lower()), None)
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value_col = next((c for c in df.columns if 'value' in c.lower()), None)
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if not (code_col and title_col and value_col):
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return product_name, df
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df_sorted = df.sort_values(value_col, ascending=False).head(10)
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out = df_sorted[[code_col, title_col, value_col]]
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out.columns = ['کد کشور', 'نام کشور', 'ارزش CIF']
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return product_name, out
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# --- تابع تولید مشاوره تخصصی با GPU کرایهای ---
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hf_token = os.getenv("HF_API_TOKEN")
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client = InferenceClient(token=hf_token)
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translator = GoogleTranslator(source='en', target='fa')
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@spaces.GPU
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def provide_advice(table_data: pd.DataFrame, hs_code: str, year: str, month: str):
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if table_data is None or table_data.empty:
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return "ابتدا نمایش دادههای واردات را انجام دهید."
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df_limited = table_data.head(10)
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table_str = df_limited.to_string(index=False)
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period = f"{year}/{int(month):02d}"
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prompt = (
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f"The following table shows the top {len(df_limited)} countries by CIF value importing HS code {hs_code} during {period}:\n"
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f"{table_str}\n\n"
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"Please provide a detailed and comprehensive analysis of market trends, risks, "
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"and opportunities for a new exporter entering this market."
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)
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try:
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outputs = client.text_generation(
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prompt=prompt,
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model="mistralai/Mixtral-8x7B-Instruct-v0.1",
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max_new_tokens=1024
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)
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return translator.translate(outputs)
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except Exception as e:
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return f"خطا در تولید مشاوره: {e}"
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# --- رابط کاربری Gradio با تنظیمات UI ---
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with gr.Blocks(css="""
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/* رنگ پسزمینه سفید و متن سیاه */
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body, .gradio-container { background-color: white !important; color: black !important; }
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/* پنهان کردن فوتر و لینکهای Gradio */
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footer, .gradio-info { display: none !important; }
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""") as demo:
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# عنوان سفارشی راستچین
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gr.Markdown(
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"<div dir='rtl' style='text-align: right; font-family: IRANSans;'>"
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"<h2>هوش مصنوعی مشاوره صادراتی با HS Code محصول – ساخته شده توسط Diginoron</h2>"
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"</div>"
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)
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with gr.Row():
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inp_hs = gr.Textbox(label="کد HS", placeholder="مثلاً 1006")
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inp_year = gr.Textbox(label="سال", placeholder="مثلاً 2023")
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inp_month = gr.Textbox(label="ماه", placeholder="مثلاً 1 تا 12")
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btn_show = gr.Button("نمایش دادههای واردات")
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out_name = gr.Markdown(label="<div dir='rtl' style='text-align: right; font-family: IRANSans;'>**نام محصول**</div>")
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out_table = gr.Dataframe(
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headers=['کد کشور', 'نام کشور', 'ارزش CIF'],
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datatype=["number", "text", "number"],
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interactive=True
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)
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btn_show.click(
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fn=get_importers,
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inputs=[inp_hs, inp_year, inp_month],
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outputs=[out_name, out_table]
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)
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btn_advice = gr.Button("ارائه مشاوره تخصصی")
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out_advice = gr.Textbox(label="<div dir='rtl' style='text-align: right; font-family: IRANSans;'>مشاوره تخصصی</div>", lines=8)
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btn_advice.click(
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fn=provide_advice,
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inputs=[out_table, inp_hs, inp_year, inp_month],
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outputs=out_advice
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)
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if __name__ == "__main__":
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demo.launch(
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share=True,
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show_api=False
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)
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