import streamlit as st # from models_api import get_answer, get_hugging_face_answer from models_api import get_answer_from_context # Define Streamlit app def main(): st.title("Document Analysis Tool") # File upload uploaded_file = st.file_uploader("Upload a text file", type=["txt"]) if uploaded_file: file_contents = uploaded_file.read().decode("utf-8") st.text_area("File Content", file_contents, height=200) # User input question question = st.text_input("Enter your question") if st.button("Generate Llama 3 8b Response"): if question: st.write("Response from Llama3 8b:") answer_llama3 = get_answer_from_context(model_name="llama3-8b-8192",context= file_contents, question=question) # answer_llama3 = get_answer("llama3", file_contents, question) # answer_llama3 = get_hugging_face_answer("meta-llama/Meta-Llama-3-8B-Instruct", file_contents, question) st.write(answer_llama3) if st.button("Generate Mistral 7b Response"): if question: st.write("Response from Mistral 7b:") answer_mistral8x7b = get_answer_from_context(model_name="mixtral-8x7b-32768",context= file_contents, question=question) # answer_mistral7b = get_answer("mistral", file_contents, question) # # answer_mistral7b = get_hugging_face_answer("mistralai/Mistral-7B-Instruct-v0.1", file_contents, question) st.write(answer_mistral8x7b) if st.button("Generate Gemma 7b Response"): if question: st.write("Response from Gemma 7b:") # # answer_gemma7b = get_answer("gemma", file_contents, question) answer_gemma7b = get_answer_from_context(model_name="gemma-7b-it",context= file_contents, question=question) # answer_gemma7b = get_hugging_face_answer("google/gemma-7b-it", file_contents, question) st.write(answer_gemma7b) # Run Streamlit app if __name__ == "__main__": main()