Update analysis.py

analysis.py

@@ -1,39 +1,34 @@
-from transformers import pipeline
+from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
 from huggingface_hub import login
-import pandas as pd
+import os
+import torch
 
-# 🔹 OPTIONAL: Authenticate if using a gated/private model
-# login(token="your_huggingface_token")  # Uncomment and replace if needed
+# ✅ Get API token from environment variable
+HF_TOKEN = os.getenv("HF_TOKEN")
 
-# ✅ Use a Free Model (Mistral-7B-v0.1 OR Gemma-2B)
-MODEL_NAME = "tiiuae/falcon-7b-instruct"  # Open-source alternative  # Publicly available
-# MODEL_NAME = "google/gemma-2b"  # Alternative (smaller but open-access)
+# ✅ Authenticate with Hugging Face (without exposing the token in code)
+login(HF_TOKEN)
 
-# Load the text generation model
-llm_pipeline = pipeline("text-generation", model=MODEL_NAME, device_map="auto")
+# ✅ Load Model Efficiently
+MODEL_NAME = "mistralai/Mistral-7B-Instruct"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, token=HF_TOKEN)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_NAME,
+    token=HF_TOKEN,
+    device_map="auto",
+    torch_dtype=torch.float16,  
+    load_in_8bit=True  
+)
+
+# ✅ Create LLM Pipeline
+llm_pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer, device_map="auto")
 
 def analyze_spending_pattern(df):
-    """
-    Analyze the user's spending behavior.
-    """
-    prompt = f"""
-    Here is the user's spending data:
-    {df.to_string(index=False)}
-    
-    Identify spending trends, categorize expenses, and highlight areas for cost-saving.
-    """
-    response = llm_pipeline(prompt, max_length=200, do_sample=True)
-    return response[0]['generated_text']
+    prompt = "Analyze the following UPI transactions:\n" + df.to_string()
+    response = llm_pipeline(prompt, max_length=200)[0]["generated_text"]
+    return response
 
 def get_financial_advice(df):
-    """
-    Provide personalized financial recommendations.
-    """
-    prompt = f"""
-    Given the following transaction history:
-    {df.to_string(index=False)}
-    
-    Provide personalized recommendations to reduce expenses and improve financial health.
-    """
-    response = llm_pipeline(prompt, max_length=200, do_sample=True)
-    return response[0]['generated_text']
+    prompt = "Provide financial advice based on these UPI transactions:\n" + df.to_string()
+    response = llm_pipeline(prompt, max_length=200)[0]["generated_text"]
+    return response