Delete analysis.py

analysis.py

@@ -1,32 +0,0 @@
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
-import os
-
-HF_TOKEN = os.getenv("HF_TOKEN")
-MODEL_NAME = "TheBloke/Mistral-7B-Instruct-v0.1-GGUF"  # Use a smaller model
-
-# Load tokenizer
-from transformers import AutoTokenizer
-
-# Load tokenizer from the official Mistral model
-tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct")
-
-# Load model (use torch.float16 if on GPU, otherwise use torch.float32 for CPU)
-model = AutoModelForCausalLM.from_pretrained(
-    MODEL_NAME,
-    torch_dtype=torch.float32,  # Change to float16 if running on GPU
-    device_map="auto",  # Uses CPU if no GPU is available
-    token=HF_TOKEN
-)
-# ✅ Create LLM Pipeline
-llm_pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer, device_map="auto")
-
-def analyze_spending_pattern(df):
-    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):
-    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