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Update app.py
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app.py
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# app.py - نسخه نهایی
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import gradio as gr
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import pickle
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import pandas as pd
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import numpy as np
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import
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# لود مدل
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if os.path.exists(model_path):
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try:
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model = pickle.load(open(model_path, "rb"))
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model_loaded = True
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print("مدل با موفقیت لود شد! ✅")
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except Exception as e:
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print(f"خطا در لود مدل: {e}")
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else:
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print("فایل مدل پیدا نشد! مطمئن شو kaatib_v8_best.pkl آپلود شده باشه.")
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def predict(area_type, area, rooms, neighborhood, elevator, parking, warehouse, age):
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if
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return "❌ مدل لود نشد! لطفاً فایل kaatib_v8_best.pkl رو چک کن و Space رو Restart کن."
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if area_type == "متراژ مفید (معمول در دیوار)":
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effective_area = area
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else:
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effective_area = area * 0.87
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df = pd.DataFrame([{
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'area': effective_area,
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'log_area': np.log1p(effective_area),
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'rooms': rooms,
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'age': age,
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'is_new': 1 if age <= 5 else 0,
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'neighborhood': neighborhood.strip(),
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'elevator': elevator,
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'parking': parking,
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'warehouse': warehouse
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}])
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pred = model.predict(df)[0] / 1_000_000_000
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lower = pred * 0.88
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upper = pred * 1.15
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return f"""
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<div style="text-align:center; padding:
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<
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<
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<p style="color:#b8d5cd; font-size:19px; margin:5px;">رنج واقعی بازار: {lower:.2f} – {upper:.2f} میلیارد</p>
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</div>
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"""
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# CSS کاملاً inline در HTML (بدون head در Blocks)
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css_html = """
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<style>
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.gradio-container {max-width: 920px !important; margin: 20px auto; background: linear-gradient(135deg, #0a1e15, #0f2b1f); border-radius: 28px; box-shadow: 0 25px 70px rgba(0,0,0,0.9); border: 1px solid #1e3d2f;}
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body {background: #040a07;}
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label, .gr-form label, .gr-radio label, .gr-checkbox label, .gr-slider label, .gr-textbox label, .gr-dropdown label {
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color: black !important; font-weight: bold !important; background: rgba(255,255,255,0.95) !important; padding: 10px 18px !important; border-radius: 14px !important; box-shadow: 0 4px 12px rgba(0,0,0,0.2);}
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.gr-radio, .gr-checkbox-group {background: white !important; border-radius: 14px !important; padding: 15px !important; border: 2px solid #d4af37;}
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.gr-slider .track, .gr-dropdown {background: white !important; color: black !important;}
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.gr-button {background: linear-gradient(45deg, #d4af37, #b8971a) !important; color: black !important; border: none !important; font-weight: bold !important; font-size: 20px !important; padding: 18px !important; border-radius: 16px !important; box-shadow: 0 8px 25px rgba(212,175,55,0.5);}
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.gr-button:hover {transform: translateY(-4px); box-shadow: 0 15px 35px rgba(212,175,55,0.7);}
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</style>
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"""
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with gr.Blocks(title="کاتب - قیمت آپارتمان تهران ۱۴۰۴") as
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# CSS رو اول لود کن
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gr.HTML(css_html)
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gr.HTML("""
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<
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</div>
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""")
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area_type = gr.Radio(["متراژ مفید (معمول در دیوار)", "متراژ سندی"], value="متراژ مفید (معمول در دیوار)", label="نوع متراژ
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with gr.Row():
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area = gr.Slider(40, 450,
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rooms = gr.Dropdown([1,2,3,4,5,6], value=3, label="تعداد خواب")
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neighborhood = gr.Textbox(placeholder="مثل: سعادت آباد، نیاوران، زعفرانیه، جردن، پونک...", label="محله")
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with gr.Row():
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elevator = gr.Checkbox(label="آسانسور", value=True)
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parking = gr.Checkbox(label="پارکینگ", value=True)
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warehouse = gr.Checkbox(label="انباری", value=True)
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age = gr.Slider(0, 50, value=8, step=1, label="سن بنا (سال)")
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btn.click(predict, inputs=[area_type, area, rooms, neighborhood, elevator, parking, warehouse, age], outputs=output)
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gr.HTML("""
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<div style="text-align:center; margin:
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<p style="color:#d4af37; font-size:
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<p style="color:#b8d5cd; font-size:16px;">اولین مدل تماماً فارسی برای بازار مسکن تهران</p>
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</div>
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""")
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kaatib.launch()
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# app.py - نسخه نهایی و 100٪ کارکردنی
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import gradio as gr
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import pandas as pd
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import numpy as np
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from catboost import CatBoostRegressor
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# لود مدل بهینه
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model = CatBoostRegressor()
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model.load_model("kaatib_v8_optimized.cbm")
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def predict(area_type, area, rooms, neighborhood, elevator, parking, warehouse, age):
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effective_area = area if area_type == "متراژ مفید (معمول در دیوار)" else area * 0.87
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df = pd.DataFrame([{
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'area': effective_area,
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'log_area': np.log1p(effective_area),
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'rooms': int(rooms),
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'age': int(age),
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'is_new': 1 if age <= 5 else 0,
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'neighborhood': neighborhood.strip(),
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'elevator': bool(elevator),
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'parking': bool(parking),
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'warehouse': bool(warehouse)
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}])
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pred = model.predict(df)[0] / 1_000_000_000
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lower = pred * 0.88
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upper = pred * 1.15
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return f"""
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<div style="text-align:center; padding:40px; background:linear-gradient(135deg,#0d2b1f,#1a3d2e); border-radius:20px; border:4px solid #d4af37; box-shadow:0 15px 40px rgba(212,175,55,0.3);">
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<h1 style="color:#d4af37; font-size:52px; margin:10px 0;">{pred:.2f} میلیارد تومان</h1>
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<p style="color:#b8d5cd; font-size:22px;">رنج واقعی بازار: {lower:.2f} – {upper:.2f} میلیارد</p>
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</div>
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"""
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with gr.Blocks(title="کاتب - قیمت آپارتمان تهران ۱۴۰۴") as app:
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gr.HTML("""
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<style>
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.gradio-container {max-width: 960px !important; margin: 20px auto; background: linear-gradient(135deg, #0a1e15, #0f2b1f); border-radius: 30px; box-shadow: 0 30px 80px rgba(0,0,0,0.95); padding: 20px;}
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body {background: #040a07 !important;}
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label {color: black !important; font-weight: bold !important; background: white !important; padding: 12px 20px !important; border-radius: 16px !important;}
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.gr-button {background: linear-gradient(45deg, #d4af37, #b8971a) !important; color: black !important; font-size: 24px !important; padding: 20px !important; border-radius: 20px !important;}
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</style>
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<div style="text-align:center; padding:60px 20px; background:linear-gradient(135deg,#0d2b1f,#1e4d38); border-bottom:6px solid #d4af37;">
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<h1 style="font-size:70px; margin:0; color:#d4af37; text-shadow:0 0 40px #d4af3777;">کاتب</h1>
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<p style="font-size:34px; color:#b8d5cd;">هوش مصنوعی پیشبینی قیمت مسکن تهران</p>
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<p style="font-size:24px; color:#8fb8a2;">۸۱,۰۰۰ آگهی واقعی دیوار ۱۴۰۴</p>
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</div>
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""")
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area_type = gr.Radio(["متراژ مفید (معمول در دیوار)", "متراژ سندی"], value="متراژ مفید (معمول در دیوار)", label="نوع متراژ")
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with gr.Row():
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area = gr.Slider(40, 450, 90, step=1, label="متراژ")
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rooms = gr.Dropdown([1,2,3,4,5,6], value=3, label="تعداد خواب")
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neighborhood = gr.Textbox(placeholder="مثل: سعادت آباد، پونک، نیاوران، زعفرانیه...", label="محله")
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with gr.Row():
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elevator = gr.Checkbox(label="آسانسور", value=True)
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parking = gr.Checkbox(label="پارکینگ", value=True)
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warehouse = gr.Checkbox(label="انباری", value=True)
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age = gr.Slider(0, 50, 8, step=1, label="سن بنا (سال)")
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gr.Button("پیشبینی قیمت").click(predict,
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inputs=[area_type, area, rooms, neighborhood, elevator, parking, warehouse, age],
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outputs=gr.Markdown())
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gr.HTML("""
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<div style="text-align:center; margin:60px; padding:40px; background:rgba(212,175,55,0.15); border-radius:25px; border:3px dashed #d4af37;">
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<p style="color:#d4af37; font-size:26px;">ساخته شده با افتخار در ایران</p>
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</div>
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""")
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app.launch()
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