app.py

import gradio as gr
from collections import Counter
from transformers import AutoImageProcessor, AutoModelForImageClassification
from PIL import Image
import torch
import math

# ============================== (همان پارامترها و توابع قبلی)
material_params = {
    "brick": {"alpha": 0.3, "eps": 0.9, "I": 1600},
    "stone": {"alpha": 0.25, "eps": 0.92, "I": 2000},
    "polishedstone": {"alpha": 0.2, "eps": 0.9, "I": 2100},
    "concrete": {"alpha": 0.35, "eps": 0.9, "I": 1800},
    "metal": {"alpha": 0.5, "eps": 0.2, "I": 4000},
    "glass": {"alpha": 0.1, "eps": 0.85, "I": 1500},
    "wood": {"alpha": 0.35, "eps": 0.9, "I": 800},
    "tile": {"alpha": 0.4, "eps": 0.9, "I": 1200},
    "ceramic": {"alpha": 0.45, "eps": 0.92, "I": 1300},
    "painted": {"alpha": 0.3, "eps": 0.9, "I": 1000},
    "plastic": {"alpha": 0.1, "eps": 0.95, "I": 800},
    "paper": {"alpha": 0.6, "eps": 0.95, "I": 500},
    "mirror": {"alpha": 0.7, "eps": 0.1, "I": 2000},
    "foliage": {"alpha": 0.25, "eps": 0.98, "I": 900},
    "water": {"alpha": 0.06, "eps": 0.98, "I": 4200},
    
}

material_categories = {
    "facade": {"members": ["brick", "stone", "polishedstone", "concrete", "tile", "ceramic", "painted"], 
               "candidates": ["brick", "stone", "polishedstone", "concrete", "tile", "ceramic", "painted"]},
    "glazing": {"members": ["glass", "mirror"], "candidates": ["glass", "mirror"]},
    "metallic": {"members": ["metal"], "candidates": ["metal"]},
    "coverings": {"members": ["plastic", "paper", "fabric"], "candidates": ["plastic", "paper", "fabric"]},
    "wood_elements": {"members": ["wood"], "candidates": ["wood"]},
    "vegetation": {"members": ["foliage"], "candidates": ["foliage"]},
    "water_bodies": {"members": ["water"], "candidates": ["water"]},
  
}

replacement_text = {
    "facade": {"brick": "آجر روشن یا نمای سرامیکی/تایل روشن با پوشش بازتابی (cool coating)",
               "stone": "سنگ روشن یا سنگ با پوشش بازتابی",
               "polishedstone": "سنگ مات روشن یا سرامیک نما روشن",
               "concrete": "بتن روشن با پوشش بازتابی یا موزاییک نما روشن",
               "tile": "کاشی/سرامیک روشن یا متخلخل",
               "ceramic": "سرامیک روشن با نمای بازتابی",
               "painted": "رنگ بازتابی (cool paint) یا پوشش نانو بازتابی"},
    "glazing": {"glass": "شیشه دو جداره با پوشش Low-E یا شیشه بازتابی کنترل‌شده",
                "mirror": "شیشه مات یا شیشه Low-E با فریم عایق"},
    "metallic": {"metal": "آلومینیوم رنگ روشن یا پوشش پودری با بازتاب بالا"},
    "coverings": {"plastic": "سنگ سبک یا چوب روکش‌دار روشن (بسته به کاربرد)",
                  "paper": "در نما کاربرد معمول ندارد - بررسی بهینه‌سازی طراحی",
                  "fabric": "پارچه با روکش بازتابی یا سایه‌انداز طبیعی"},
    "wood_elements": {"wood": "چوب رنگ روشن یا چوب با روکش بازتابی/محافظ"},
    "vegetation": {"foliage": None},
    "water_bodies": {"water": None},

}

# ============================== (توابع کمکی)
def ET_proxy(T, RH):
    es = 0.6108 * math.exp((17.27 * T) / (T + 237.3))
    return es * (1 - RH / 100.0)

def calc_deltaT(material, T_air, RH=40, u=2, S=700):
    if material not in material_params: return 0.0
    alpha, eps, I = material_params[material]["alpha"], material_params[material]["eps"], material_params[material]["I"]
    A, B, C, D = 1.0, 0.4, 0.8, 0.015
    h_c = 5.8 + 4.1 * u
    if material == "foliage":
        C_m = A * (1 - alpha) - D * ET_proxy(T_air, RH)
    else:
        C_m = A * (1 - alpha) + B * (1 - eps) + (C / math.sqrt(max(I, 1)))
    gamma = S / max(h_c, 1e-6)
    return gamma * C_m / 1000.0

# ============================== (بارگذاری مدل)
model_id = "prithivMLmods/Minc-Materials-23"
processor = AutoImageProcessor.from_pretrained(model_id)
model = AutoModelForImageClassification.from_pretrained(model_id)

patch_size = 224
def get_patches(image, size=224, stride=100):
    patches = []
    w, h = image.size
    for scale in [1.0, 0.75, 0.5]:
        scaled_w, scaled_h = int(w * scale), int(h * scale)
        if min(scaled_w, scaled_h) < size: continue
        scaled_img = image.resize((scaled_w, scaled_h), Image.Resampling.LANCZOS)
        for i in range(0, scaled_w, stride):
            for j in range(0, scaled_h, stride):
                box = (i, j, min(i+size, scaled_w), min(j+size, scaled_h))
                patch = scaled_img.crop(box)
                if patch.size[0] >= size and patch.size[1] >= size:
                    patches.append(patch)
    return patches

# ============================== (تابع اصلی Gradio)
def analyze_image(image, T_air=32.0, RH=40, u=2.0, S=700):
    patches = get_patches(image, size=patch_size)
    all_predictions = []
    for patch in patches:
        inputs = processor(images=patch, return_tensors="pt")
        with torch.no_grad():
            outputs = model(**inputs)
            probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
        top1 = torch.argmax(probs[0]).item()
        label = model.config.id2label[top1]
        all_predictions.append(label)
    
    counter = Counter(all_predictions)
    total_patches = len(patches)
    MIN_COUNT = 3
    ignore_classes = ["food", "skin", "other", "wallpaper", "carpet","sky"]
    materials_found = {label for label, count in counter.items() if count >= MIN_COUNT and label not in ignore_classes}
    
    if len(materials_found) == 0:
        return "هیچ مصالح معتبرِ کافی در تصویر شناسایی نشد (حداقل تکرار MIN_COUNT رعایت نمی‌شود)."
    
    material_info = {}
    for label in sorted(materials_found):
        count = counter[label]
        share = count / total_patches
        dT = calc_deltaT(label, T_air, RH, u, S)
        material_info[label] = {"count": count, "share": share, "deltaT": dT}
    
    # مقایسه درون‌دسته‌ای و توصیه
    IMPROVEMENT_THRESHOLD = 0.02
    SHARE_IMPORTANCE_THRESHOLD = 0.03
    recommendations = []
    candidate_delta_cache = {}
    for cat, info in material_categories.items():
        for candidate in info["candidates"]:
            if candidate not in candidate_delta_cache:
                candidate_delta_cache[candidate] = calc_deltaT(candidate, T_air, RH, u, S)
    
    for label, info in material_info.items():
        found_category = None
        for cat, cinfo in material_categories.items():
            if label in cinfo["members"]:
                found_category = cat
                break
        if found_category is None:
            recommendations.append(f"{label}: در دسته‌های پیش‌تعریف قرار ندارد.")
            continue
        candidates = material_categories[found_category]["candidates"]
        cand_list = [(c, candidate_delta_cache.get(c, calc_deltaT(c, T_air, RH, u, S))) for c in candidates]
        cand_list.sort(key=lambda x: x[1])
        current_dT = info["deltaT"]
        best_candidate, best_dT = cand_list[0]
        improvement = current_dT - best_dT
        share_pct = info["share"] * 100
        if improvement >= IMPROVEMENT_THRESHOLD and best_candidate != label:
            importance = "High" if info["share"] >= SHARE_IMPORTANCE_THRESHOLD else "Optional"
            suggestion_text = replacement_text.get(found_category, {}).get(best_candidate, f"Consider replacing with {best_candidate}")
            recommendations.append(
                f"{label} ({found_category}): ΔT={current_dT:+.2f}°C → جایگزین: {best_candidate} (ΔT={best_dT:+.2f}°C) | بهبود: {improvement:+.2f}°C | اهمیت: {importance} | پیشنهاد: {suggestion_text}"
            )
        else:
            recommendations.append(f"{label}: ΔT={current_dT:+.2f}°C → نیازی به جایگزینی ندارد.")
    
    scene_deltaT = sum([info["share"] * info["deltaT"] for info in material_info.values()])
    recommendations.append(f"ΔT میانگین وزنی کل صحنه: {scene_deltaT:+.2f}°C")
    recommendations.append(f"دمای مؤثر سطح: {T_air + scene_deltaT:.2f}°C")
    
    return "\n".join(recommendations)

# ============================== (راه‌اندازی رابط Gradio)
iface = gr.Interface(
    fn=analyze_image,
    inputs=[
        gr.Image(type="pil", label="آپلود تصویر"),
        gr.Number(value=32.0, label="دمای هوا T_air (°C)"),
        gr.Number(value=40, label="رطوبت نسبی RH (%)"),
        gr.Number(value=2.0, label="سرعت باد u (m/s)"),
        gr.Number(value=700, label="تابش خورشیدی S (W/m²)")
    ],
    outputs=gr.Textbox(label="خروجی ΔT و توصیه‌ها"),
    title="تحلیل مصالح و ΔT سطحی",
    description="آپلود تصویر ساختمان/محیط → نمایش ΔT مصالح و توصیه جایگزینی منطقی."
)

iface.launch()