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import streamlit as st
import torch
import requests
import os
from transformers import AutoTokenizer, AutoModelForCausalLM
from huggingface_hub import login, HfApi
# Optional: Login if you want access to gated/private models
HF_TOKEN = os.getenv("HF_TOKEN", None)
if HF_TOKEN:
login(HF_TOKEN)
# Define model map with access type
model_map = {
"FinGPT": {"id": "AI4Finance/FinGPT", "local": True},
"InvestLM": {"id": "mrm8488/investLM-7B", "local": False}, # example ID, update if needed
"FinLLaMA": {"id": "HuggingFaceH4/fin-llama", "local": False},
"FinanceConnect": {"id": "ceadar-ie/FinanceConnect-13B", "local": True},
"Sujet-Finance": {"id": "sujet-ai/Sujet-Finance-8B-v0.1", "local": True}
}
# Cache local models
@st.cache_resource
def load_local_model(model_id):
tokenizer = AutoTokenizer.from_pretrained(model_id, use_auth_token=HF_TOKEN)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
device_map="auto" if torch.cuda.is_available() else None,
use_auth_token=HF_TOKEN
)
return model, tokenizer
# Local model querying
def query_local_model(model_id, prompt):
model, tokenizer = load_local_model(model_id)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=150)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
# Remote model querying (via Inference API)
def query_remote_model(model_id, prompt):
headers = {"Authorization": f"Bearer {HF_TOKEN}"} if HF_TOKEN else {}
payload = {"inputs": prompt, "parameters": {"max_new_tokens": 150}}
response = requests.post(
f"https://api-inference.huggingface.co/models/{model_id}",
headers=headers,
json=payload
)
if response.status_code == 200:
result = response.json()
return result[0]["generated_text"] if isinstance(result, list) else result.get("generated_text", "No output")
else:
raise RuntimeError(f"Failed to call remote model: {response.text}")
# Unified query dispatcher
def query_model(model_entry, prompt):
if model_entry["local"]:
return query_local_model(model_entry["id"], prompt)
else:
return query_remote_model(model_entry["id"], prompt)
# --- Streamlit UI ---
st.title("💼 Financial LLM Evaluation Interface")
model_choice = st.selectbox("Select a Financial Model", list(model_map.keys()))
user_question = st.text_area("Enter your financial question:", "What is EBITDA?")
if st.button("Get Response"):
with st.spinner("Generating response..."):
try:
model_entry = model_map[model_choice]
answer = query_model(model_entry, user_question)
st.subheader(f"Response from {model_choice}:")
st.write(answer)
except Exception as e:
st.error(f"Something went wrong: {e}")
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