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llm/explaination.py

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+import torch
+
+def generate_explanation(transaction, tokenizer, model, device=torch.device("cpu")):
+    if tokenizer is None or model is None:
+        return "Explanation service unavailable"
+
+    try:
+        text = (
+            f"Amount: ${transaction['amount']:.2f}, "
+            f"Credit Limit: ${transaction['credit_limit']:.2f}, "
+            f"Ratio: {transaction['amount_ratio']:.2f}, "
+            f"Chip Usage: {transaction.get('use_chip', 'N/A')}"
+        )
+        inputs = tokenizer([text], return_tensors="pt", truncation=True, padding=True).to(device)
+        with torch.no_grad():
+            outputs = model(**inputs)
+            probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
+        return f"Risk assessment: {probs[0][1]*100:.1f}% suspicious activity likelihood"
+    except Exception as e:
+        return f"Error generating explanation: {e}"

llm/financial_chat.py

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+from .groq_client import get_groq_client
+
+def financial_chat(user_input, groq_client=None):
+    if groq_client is None:
+        try:
+            groq_client = get_groq_client()
+        except Exception as e:
+            return f"Error initializing chat service: {e}"
+
+    try:
+        response = groq_client.chat.completions.create(
+            messages=[
+                {
+                    "role": "system",
+                    "content": (
+                        "You are FinanceWise AI, a strict financial advisor. Only respond to finance-related questions. "
+                        "If the user asks about anything unrelated to finance (e.g. languages, science, entertainment), reply: "
+                        "'Sorry, I can only help with finance-related questions.'"
+                    ),
+                },
+                {"role": "user", "content": user_input}
+            ],
+            model="llama-3.3-70b-versatile",
+            temperature=0.3,
+            top_p=0.7
+        )
+        return response.choices[0].message.content
+    except Exception as e:
+        return f"Error during chat: {e}"

llm/groq_client.py

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+import os
+from groq import Groq
+
+def get_groq_client():
+    api_key = os.environ.get("GROQ_API_KEY")
+    if not api_key:
+        raise ValueError("GROQ_API_KEY not set in environment variables.")
+    return Groq(api_key=api_key)

llm/model_loader.py

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+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+def load_llm_components(device=torch.device("cpu")):
+    try:
+        tokenizer = AutoTokenizer.from_pretrained("ProsusAI/finbert")
+        model = AutoModelForSequenceClassification.from_pretrained("ProsusAI/finbert")
+        print("LLM components loaded successfully!")
+    except Exception as e:
+        print(f"Failed to load LLM components: {e}")
+        tokenizer, model = None, None
+    return tokenizer, model