Update model.py

model.py

@@ -1,37 +1,27 @@
-import torch
-from transformers import BertTokenizer, BertModel, T5ForConditionalGeneration
-from transformers import AutoModelForSequenceClassification
-from utilities import preprocess_features, generate_recommendation
-
-MODEL_NAME = "sathkrutha/deal-score-model"  # Use your actual repo ID
-SUMMARIZER_PATH = "t5-small"  # Can use distilgpt2 or other summarizers
+import random
+from utilities import prepare_text_input, generate_recommendation
 
+# Mock objects (None used in mock version)
 def load_model():
-    model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
-    tokenizer = BertTokenizer.from_pretrained(MODEL_PATH)
-    summarizer = T5ForConditionalGeneration.from_pretrained(SUMMARIZER_PATH)
-    return model, tokenizer, summarizer
+    return None, None, None
 
-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()
+def predict(data, model, tokenizer, summarizer):
+    text_input = prepare_text_input(data)
 
+    # Simulated scoring logic
+    score = random.randint(45, 95)
+    confidence = round(random.uniform(0.6, 0.95), 2)
     risk = (
         "Low" if score > 75 else
-        "Medium" if score > 50 else
+        "Medium" if score >= 55 else
         "High"
     )
 
-    recommendation = generate_recommendation(summarizer, input_data)
+    recommendation = generate_recommendation(data)
 
     return {
         "score": score,
-        "confidence": round(confidence, 2),
+        "confidence": confidence,
         "risk": risk,
         "recommendation": recommendation
     }