app.py created

app.py

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+import streamlit as st
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Load the model and tokenizer
+@st.cache(allow_output_mutation=True)
+def load_model():
+    model = AutoModelForCausalLM.from_pretrained("AdaptLLM/finance-chat")
+    tokenizer = AutoTokenizer.from_pretrained("AdaptLLM/finance-chat", use_fast=False)
+    return model, tokenizer
+
+model, tokenizer = load_model()
+
+# Streamlit interface
+st.title("Finance Chatbot")
+
+# User input
+user_input = st.text_area("Enter your query:")
+
+if st.button("Submit"):
+    if user_input:
+        # Prepare the prompt
+        prompt = f"<s>[INST] <<SYS>>\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.  Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\n<</SYS>>\n\n{user_input} [/INST]"
+
+        # Tokenize and generate response
+        inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).input_ids.to(model.device)
+        outputs = model.generate(input_ids=inputs, max_length=4096)[0]
+
+        answer_start = int(inputs.shape[-1])
+        pred = tokenizer.decode(outputs[answer_start:], skip_special_tokens=True)
+
+        # Display the output
+        st.write("### Assistant Output:")
+        st.write(pred)
+    else:
+        st.write("Please enter a query.")