Create model.py

model.py

@@ -0,0 +1,37 @@
+import torch
+from transformers import BertTokenizer, BertModel, T5ForConditionalGeneration
+from transformers import AutoModelForSequenceClassification
+from utilities import preprocess_features, generate_recommendation
+
+MODEL_PATH = "your-hf-username/deal-score-model"  # Replace with your model path
+SUMMARIZER_PATH = "t5-small"  # Can use distilgpt2 or other summarizers
+
+def load_model():
+    model = AutoModelForSequenceClassification.from_pretrained(MODEL_PATH)
+    tokenizer = BertTokenizer.from_pretrained(MODEL_PATH)
+    summarizer = T5ForConditionalGeneration.from_pretrained(SUMMARIZER_PATH)
+    return model, tokenizer, summarizer
+
+def predict_deal_score(input_data, model, tokenizer, summarizer):
+    features = preprocess_features(input_data)
+    inputs = tokenizer(features["text"], return_tensors="pt")
+    
+    with torch.no_grad():
+        outputs = model(**inputs)
+        score = round(outputs.logits[0][0].item() * 100)
+        confidence = torch.softmax(outputs.logits[0], dim=-1).max().item()
+
+    risk = (
+        "Low" if score > 75 else
+        "Medium" if score > 50 else
+        "High"
+    )
+
+    recommendation = generate_recommendation(summarizer, input_data)
+
+    return {
+        "score": score,
+        "confidence": round(confidence, 2),
+        "risk": risk,
+        "recommendation": recommendation
+    }