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app.py

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+import gradio as gr
+import torch
+import collections
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+HF_USERNAME = "mahmoudmohammad"
+CONFIDENCE_THRESHOLD = 0.70
+
+print("Booting Global Taxonomy Engine...")
+# --- 1. Permanently Load L1 Model ---
+l1_repo = f"{HF_USERNAME}/SANAD-L1-Root-Classifier"
+l1_tokenizer = AutoTokenizer.from_pretrained(l1_repo)
+l1_model = AutoModelForSequenceClassification.from_pretrained(l1_repo)
+l1_model.eval()
+
+# Dynamically extract which L2 classes exist directly from the L1 id mappings
+# Matches format deployed to HF Hub
+available_branches = [label for label in l1_model.config.id2label.values()]
+
+# --- 2. Smart Memory Manager (LRU Cache) ---
+# Limits how many L2 models sit in RAM at once to avoid Out-Of-Memory errors
+class L2ModelCache:
+    def __init__(self, max_models=3):
+        self.max_models = max_models
+        self.cache = collections.OrderedDict()
+
+    def get_model(self, l1_label):
+        if l1_label in self.cache:
+            self.cache.move_to_end(l1_label)
+            return self.cache[l1_label]
+            
+        print(f"Loading {l1_label} L2 model into RAM...")
+        repo_id = f"{HF_USERNAME}/SANAD-L2-{l1_label}-Classifier"
+        
+        try:
+            tok = AutoTokenizer.from_pretrained(repo_id)
+            mod = AutoModelForSequenceClassification.from_pretrained(repo_id)
+            mod.eval()
+            self.cache[l1_label] = (tok, mod)
+            
+            if len(self.cache) > self.max_models:
+                evicted = self.cache.popitem(last=False)
+                print(f"Unloaded {evicted[0]} L2 model from RAM to free space.")
+                
+            return self.cache[l1_label]
+        except Exception:
+            return None, None # Branch model doesn't exist on hub (Flattened L1)
+
+l2_manager = L2ModelCache(max_models=3)
+
+# --- 3. The 2-Stage Routing Logic ---
+def classify_news(text):
+    if not text.strip():
+        return "Empty text", "N/A"
+
+    # Stage 1: L1 Routing
+    inputs = l1_tokenizer(text, return_tensors="pt", truncation=True, max_length=256)
+    with torch.no_grad():
+        out1 = l1_model(**inputs)
+        
+    probs1 = torch.softmax(out1.logits, dim=-1).squeeze()
+    conf1 = probs1.max().item()
+    pred1 = l1_model.config.id2label[probs1.argmax().item()]
+    
+    # Cascade: If root is unsure, drop instantly
+    if conf1 < CONFIDENCE_THRESHOLD:
+        return "Uncertain", f"L1 Drop: {pred1} (Conf: {conf1:.2f})"
+        
+    # Attempt Stage 2 (Drilldown)
+    l2_tok, l2_mod = l2_manager.get_model(pred1)
+    
+    # Branch doesn't exist? (Phase 1D Flattening executed correctly)
+    if not l2_mod:
+        return pred1, f"Status: L1 Flat Structure Approved (Conf: {conf1:.2f})"
+        
+    # Route through existing L2 
+    l2_in = l2_tok(text, return_tensors="pt", truncation=True, max_length=256)
+    with torch.no_grad():
+        out2 = l2_mod(**l2_in)
+        
+    probs2 = torch.softmax(out2.logits, dim=-1).squeeze()
+    conf2 = probs2.max().item()
+    pred2 = l2_mod.config.id2label[probs2.argmax().item()]
+    
+    # Confidence test Stage 2 - Drop safely to L1 generalization if fail
+    if conf2 < CONFIDENCE_THRESHOLD:
+         return pred1, f"Status: Sub-Tag Rejected. Dropped to Base Root (L2 Conf: {conf2:.2f})"
+         
+    # Pure hierarchy completion
+    return f"{pred1} / {pred2}", f"Success: L1({conf1:.2f}) -> L2({conf2:.2f})"
+
+# --- 4. The Front-End UI ---
+iface = gr.Interface(
+    fn=classify_news,
+    inputs=gr.Textbox(lines=7, label="Arabic News Text", placeholder="Paste article here..."),
+    outputs=[
+        gr.Textbox(label="Final Category Assignment"), 
+        gr.Textbox(label="Confidence Diagnostics Routing Debugger")
+    ],
+    title="Arabic News Hierarchical Categorizer (L1 + L2 Pipeline)",
+    description="This gateway automates intelligent semantic tracking against 8 Deep Learning architecture branches globally.",
+    examples=["سجل فريق ريال مدريد فوزاً كاسحاً في دوري أبطال أوروبا"]
+)
+
+iface.launch()

requirements.txt

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+transformers
+torch
+scikit-learn
+pandas