app.py

import gradio as gr
import os
from deep_translator import GoogleTranslator
from huggingface_hub import InferenceClient

# دریافت توکن از محیط (در Hugging Face Secrets تنظیم شود)
HF_TOKEN = os.environ.get("HUGGINGFACE_API_TOKEN")
if not HF_TOKEN:
    raise RuntimeError("Missing HUGGINGFACE_API_TOKEN secret")

# ساخت کلاینت جدید
hf_client = InferenceClient(token=HF_TOKEN)

def generate_topics(field, major, keywords, audience, level):
    if not all([field.strip(), major.strip(), keywords.strip(), audience.strip()]):
        return "<div style='color: red;'>❌ لطفاً همه فیلدها را پر کنید.</div>"

    base_prompt = (
        f"Suggest 3 academic thesis topics based on the following:\n"
        f"Field: {field}\n"
        f"Specialization: {major}\n"
        f"Keywords: {keywords}\n"
        f"Target Audience: {audience}\n"
        f"Level: {level}\n"
    )
    extra = (
        "Since this is a doctoral-level project, focus on proposing theoretical frameworks, "
        "advanced modeling approaches, and in-depth methodological contributions."
        if level == "دکتری"
        else
        "Focus on practical and applied thesis topics suitable for a master's level student."
    )
    prompt = base_prompt + extra

    try:
        response = hf_client.chat.completions.create(
            model="deepseek-ai/DeepSeek-Prover-V2-671B",
            messages=[
                {"role": "system", "content": "You are an academic advisor assistant."},
                {"role": "user", "content": prompt}
            ],
            temperature=0.7,
            max_tokens=512
        )
        english_output = response.choices[0].message.content.strip()
        try:
            translated = GoogleTranslator(source='en', target='fa').translate(english_output)
        except:
            translated = english_output
        items = [f"<li>{line}</li>" for line in translated.split("\n") if line.strip()]
        translated_html = "<ol>" + "".join(items) + "</ol>"

        return (
            "<div>"
            f"{translated_html}"
            "<br><br>📢 برای مشاوره و راهنمایی تخصصی با گروه مشاوره کاسپین تماس بگیرید:<br>"
            "<strong>021-88252497</strong>"
            "</div>"
        )
    except Exception as e:
        return f"<div style='color: red;'>❌ خطا در تماس با مدل DeepSeek: {e}</div>"

# CSS برای زیبایی خروجی
css = """
#output_box {
    min-height: 350px !important;
    max-height: 600px !important;
    overflow-y: auto !important;
    background-color: #1e1e1e !important;
    color: white !important;
    padding: 20px;
    border: 2px solid #ccc;
    font-family: 'Tahoma', sans-serif;
    font-size: 16px;
    text-align: right;
    direction: rtl;
    line-height: 1.8;
}
"""

with gr.Blocks(css=css, title="🎓 پیشنهادگر موضوع پایان‌نامه کاسپین") as app:
    # لوگو بالای صفحه
    gr.HTML(
        "<div style='text-align:center;margin-bottom:10px;'>"
        "<img src='/file=logo.png' width='200'/>"
        "</div>"
    )
    with gr.Row():
        with gr.Column():
            field    = gr.Textbox(label="رشته", placeholder="مثال: کامپیوتر")
            major    = gr.Textbox(label="گرایش", placeholder="مثال: هوش مصنوعی")
            keywords = gr.Textbox(label="کلیدواژه‌ها", placeholder="مثال: یادگیری عمیق، بینایی ماشین")
            audience = gr.Textbox(label="جامعه هدف", placeholder="مثال: دانشجویان دکتری")
            level    = gr.Dropdown(["کارشناسی ارشد","دکتری"], label="مقطع")
            submit   = gr.Button("🎯 پیشنهاد موضوع")
        with gr.Column():
            output   = gr.HTML(elem_id="output_box")

    submit.click(
        fn=generate_topics,
        inputs=[field, major, keywords, audience, level],
        outputs=output
    )

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