witty explainabilty added

src/llm.py

@@ -0,0 +1,78 @@
+import os
+import time
+from typing import Dict, Optional
+from groq import Groq
+from huggingface_hub import InferenceClient
+from dotenv import load_dotenv
+load_dotenv()
+
+class PersonaExplainer:
+    def __init__(self):
+        self.groq_api_key = os.getenv("GROQ_API_KEY")
+        self.hf_api_token = os.getenv("HF_API_TOKEN")
+        
+        self.groq_client = Groq(api_key=self.groq_api_key) if self.groq_api_key else None
+        self.hf_client = InferenceClient(token=self.hf_api_token) if self.hf_api_token else None
+
+    def generate_explanation(self, persona: str, stats: Dict[str, float]) -> str:
+        """
+        Generates a humorous/insightful explanation of the wallet's persona.
+        """
+        prompt = self._construct_prompt(persona, stats)
+        
+        # Try Groq First (Fastest)
+        if self.groq_client:
+            try:
+                return self._call_groq(prompt)
+            except Exception as e:
+                print(f"Groq API failed: {e}. Falling back...")
+        
+        # Fallback to Hugging Face
+        if self.hf_client:
+            try:
+                return self._call_hf(prompt)
+            except Exception as e:
+                print(f"HF API failed: {e}.")
+        
+        return "Analysis unavailable (AI models busy)."
+
+    def _construct_prompt(self, persona: str, stats: Dict) -> str:
+        # Simplify stats for the LLM to avoid token bloat
+        key_stats = {
+            "Transactions": int(stats.get('tx_count', 0)),
+            "NFT Volume (USD)": f"${stats.get('total_nft_volume_usd', 0):,.2f}",
+            "Gas Spent (ETH)": f"{stats.get('total_gas_spent', 0):.4f}",
+            "Active Days": int(stats.get('active_days', 0)),
+            "DEX Trades": int(stats.get('dex_trades', 0))
+        }
+        
+        return (
+            f"You are a crypto analytics bot with a witty, slightly roasting personality. "
+            f"Analyze this wallet:\n"
+            f"Persona: {persona}\n"
+            f"Stats: {key_stats}\n\n"
+            f"Task: Write a 2-3 sentence 'Roast' or 'Insight' about this user. "
+            f"Explain WHY they fit this persona based on the stats. "
+            f"Be specific but concise."
+        )
+
+    def _call_groq(self, prompt: str) -> str:
+        chat_completion = self.groq_client.chat.completions.create(
+            messages=[
+                {"role": "system", "content": "You are a witty crypto analyst."},
+                {"role": "user", "content": prompt}
+            ],
+            model="llama-3.1-8b-instant",
+            temperature=0.7,
+            max_tokens=200,
+        )
+        return chat_completion.choices[0].message.content
+
+    def _call_hf(self, prompt: str) -> str:
+        # Using Mistral-7B-Instruct via HF Inference API
+        return self.hf_client.text_generation(
+            prompt, 
+            model="mistralai/Mistral-7B-Instruct-v0.2", 
+            max_new_tokens=200,
+            temperature=0.7
+        )