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README.md

@@ -1,7 +1,7 @@
 ---
-title: Materials
-emoji: 🏃
-colorFrom: green
+title: Material Heat Analysis
+emoji: 🌡️
+colorFrom: blue
 colorTo: red
 sdk: gradio
 sdk_version: 5.44.1
@@ -9,4 +9,4 @@ app_file: app.py
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+یک اپلیکیشن برای تحلیل مصالح ساختمانی از تصویر و محاسبه ΔT سطح.

app.py

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+
+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()

requirements.txt

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+gradio==5.44.1
+transformers==4.40.0
+torch>=2.0.0
+pillow>=10.0.0
+numpy>=1.25.0
+matplotlib>=3.8.0