Delete FinGPT_TaskII_Submission/scripts/inference_with_tools.py

FinGPT_TaskII_Submission/scripts/inference_with_tools.py

@@ -1,108 +0,0 @@
-# ================================================================
-# File: inference_with_tools.py
-# Author: fengxb30
-# Description: Inference script for FinGPT Task II (Compliance Agents)
-# ================================================================
-
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import requests
-import yfinance as yf
-from datetime import datetime
-import json
-import os
-
-# -----------------------------
-# 1️⃣  Load the model
-# -----------------------------
-def load_model(model_name_or_path="fengxb30/FinGPT_TaskII_Compliance"):
-    print(f"🔹 Loading model from {model_name_or_path} ...")
-    tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
-    model = AutoModelForCausalLM.from_pretrained(
-        model_name_or_path,
-        torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
-        device_map="auto"
-    )
-    model.eval()
-    return tokenizer, model
-
-# -----------------------------
-# 2️⃣  Financial Data API Helper
-# -----------------------------
-def fetch_financial_data(ticker="AAPL"):
-    """Fetch real-time market data from Yahoo Finance"""
-    stock = yf.Ticker(ticker)
-    info = stock.info
-    return {
-        "symbol": ticker,
-        "price": info.get("currentPrice", None),
-        "marketCap": info.get("marketCap", None),
-        "fiftyTwoWeekHigh": info.get("fiftyTwoWeekHigh", None),
-        "fiftyTwoWeekLow": info.get("fiftyTwoWeekLow", None),
-    }
-
-# -----------------------------
-# 3️⃣  External Tool: RAG-style retrieval
-# -----------------------------
-def retrieve_context_from_web(query):
-    """Use simple web API (DuckDuckGo Instant Answer) to retrieve context"""
-    url = f"https://api.duckduckgo.com/?q={query}&format=json"
-    try:
-        res = requests.get(url, timeout=10)
-        data = res.json()
-        return data.get("AbstractText") or data.get("Heading") or "No context found."
-    except Exception as e:
-        return f"Retrieval failed: {e}"
-
-# -----------------------------
-# 4️⃣  Model inference with context
-# -----------------------------
-def generate_response(model, tokenizer, prompt, context=None, temperature=0.2, max_new_tokens=512):
-    if context:
-        full_input = f"Context: {context}\n\nQuestion: {prompt}\nAnswer:"
-    else:
-        full_input = f"Question: {prompt}\nAnswer:"
-    inputs = tokenizer(full_input, return_tensors="pt").to(model.device)
-    outputs = model.generate(
-        **inputs,
-        max_new_tokens=max_new_tokens,
-        temperature=temperature,
-        top_p=0.9,
-        do_sample=True
-    )
-    return tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-# -----------------------------
-# 5️⃣  Pipeline function
-# -----------------------------
-def compliance_agent_pipeline(prompt, ticker=None, use_web=True):
-    tokenizer, model = load_model()
-    context = ""
-
-    # Optional: integrate external tools
-    if ticker:
-        fin_data = fetch_financial_data(ticker)
-        context += f"Market data for {ticker}: {json.dumps(fin_data, indent=2)}\n\n"
-
-    if use_web:
-        web_context = retrieve_context_from_web(prompt)
-        context += f"Retrieved context: {web_context}\n\n"
-
-    # Generate model output
-    response = generate_response(model, tokenizer, prompt, context)
-    return response
-
-
-# -----------------------------
-# 6️⃣  Example run
-# -----------------------------
-if __name__ == "__main__":
-    os.environ["TOKENIZERS_PARALLELISM"] = "false"
-    print("🧠 FinGPT Task II - Compliance Agent Inference")
-    query = input("Enter your compliance query: ")
-
-    # Example: "Summarize the compliance risk of Meta in 2025"
-    result = compliance_agent_pipeline(prompt=query, ticker="META", use_web=True)
-
-    print("\n=== AI Compliance Agent Response ===\n")
-    print(result)