fix: include missing predict_pipeline.py in repo with run_prediction_pipeline()

predict_pipeline.py

@@ -0,0 +1,55 @@
+import zipfile
+import tempfile
+from pathlib import Path
+import torch
+from transformers import DebertaV2Tokenizer, AutoModelForSequenceClassification
+
+from train_abuse_model import (
+    MODEL_DIR,
+    device,
+    load_saved_model_and_tokenizer,
+    map_to_3_classes,
+    convert_to_label_strings
+)
+
+def run_prediction_pipeline(desc_input, chat_zip):
+    try:
+        # Start with the base input
+        merged_input = desc_input.strip()
+
+        # If a chat zip was uploaded
+        if chat_zip:
+            with tempfile.TemporaryDirectory() as tmpdir:
+                with zipfile.ZipFile(chat_zip.name, 'r') as zip_ref:
+                    zip_ref.extractall(tmpdir)
+                chat_texts = []
+                for file in Path(tmpdir).glob("*.txt"):
+                    with open(file, encoding="utf-8", errors="ignore") as f:
+                        chat_texts.append(f.read())
+                full_chat = "\n".join(chat_texts)
+
+                # 🧠 MOCK summarization
+                summary = "[Mock summary of Hebrew WhatsApp chat...]"
+
+                # 🌍 MOCK translation
+                translated_summary = "[Translated summary in English]"
+
+                merged_input = f"{desc_input.strip()}\n\n[Summary]: {translated_summary}"
+
+        # Load classifier
+        tokenizer, model = load_saved_model_and_tokenizer()
+        inputs = tokenizer(merged_input, truncation=True, padding=True, max_length=512, return_tensors="pt").to(device)
+
+        with torch.no_grad():
+            outputs = model(**inputs).logits
+            probs = torch.sigmoid(outputs).cpu().numpy()
+
+        # Static threshold values (or load from config later)
+        best_low, best_high = 0.35, 0.65
+        pred_soft = map_to_3_classes(probs, best_low, best_high)
+        pred_str = convert_to_label_strings(pred_soft)
+
+        return merged_input, ", ".join(pred_str)
+
+    except Exception as e:
+        return f"❌ Prediction failed: {e}", ""