Update app.py

app.py

@@ -1,29 +1,39 @@
 import streamlit as st
 from transformers import AutoTokenizer, AutoModelForCausalLM
+from huggingface_hub import login
 import torch
+import os
 
+
+# Set model map
+model_map = {
+    "FinGPT": "AI4Finance/FinGPT",
+    "FinanceConnect": "ceadar-ie/FinanceConnect-13B",
+    "Sujet-Finance": "sujet-ai/Sujet-Finance-8B-v0.1"
+}
+
+# Cache model loading for performance
 @st.cache_resource
 def load_model_and_tokenizer(model_id):
     tokenizer = AutoTokenizer.from_pretrained(model_id)
-    model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32)
+    model = AutoModelForCausalLM.from_pretrained(
+        model_id,
+        torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
+    )
     return model, tokenizer
 
+# Query model
 def query_model(model_id, question):
     model, tokenizer = load_model_and_tokenizer(model_id)
-    inputs = tokenizer.encode(question, return_tensors="pt")
-    outputs = model.generate(inputs, max_new_tokens=100)
+    inputs = tokenizer(question, return_tensors="pt")
+    outputs = model.generate(**inputs, max_new_tokens=150)
     return tokenizer.decode(outputs[0], skip_special_tokens=True)
 
-model_map = {
-    "FinGPT": "second-state/FinGPT-MT-Llama-3-8B-LoRA-GGUF",
-    "InvestLM": "yixuantt/InvestLM-mistral-AWQ",
-    "FinLlama": "roma2025/FinLlama-3-8B"
-}
-
+# Streamlit app layout
 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 the market outlook for the next quarter?")
+user_question = st.text_area("Enter your financial question:", "What is EBITDA?")
 
 if st.button("Get Response"):
     with st.spinner("Generating response..."):
@@ -33,4 +43,3 @@ if st.button("Get Response"):
             st.write(answer)
         except Exception as e:
             st.error(f"Something went wrong: {e}")
-