Delete app.py

app.py

@@ -1,110 +0,0 @@
-import streamlit as st
-import pandas as pd
-import sqlite3
-from llama_index.core import StorageContext, load_index_from_storage
-from llama_index.llms.ollama import Ollama
-from llama_index.embeddings.huggingface import HuggingFaceEmbedding
-from llama_index.core import PromptTemplate
-import os
-
-version = 2.2
-
-# Initialize the SQLite3 database
-conn = sqlite3.connect('qa.db')
-c = conn.cursor()
-# Update the table creation to include the version column
-c.execute('CREATE TABLE IF NOT EXISTS qa (question TEXT, answer TEXT, version REAL)')
-conn.commit()
-
-# Read the LLM Model Description from a file
-def read_description_from_file(file_path):
-    with open(file_path, 'r') as file:
-        return file.read()
-
-# Define the folder containing the saved index
-INDEX_OUTPUT_PATH = "./output_index"
-
-# Ensure the output directory exists
-if not os.path.exists(INDEX_OUTPUT_PATH):
-    raise ValueError(f"Index directory {INDEX_OUTPUT_PATH} does not exist")
-
-# Setup LLM and embedding model
-llm = Ollama(model="llama3", request_timeout=120.0)
-embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-large-en-v1.5", trust_remote_code=True)
-
-# To load the index later, set up the storage context
-storage_context = StorageContext.from_defaults(persist_dir=INDEX_OUTPUT_PATH)
-loaded_index = load_index_from_storage(embed_model=embed_model, storage_context=storage_context)
-
-# Define a query engine (assuming it needs the LLM and embedding model)
-query_engine = loaded_index.as_query_engine(llm=llm, embed_model=embed_model)
-
-# Customise prompt template
-# Read the prompt template from a file
-qa_prompt_tmpl_str = read_description_from_file("tab2_pe.txt")
-qa_prompt_tmpl = PromptTemplate(qa_prompt_tmpl_str)
-
-query_engine.update_prompts(
-    {"response_synthesizer:text_qa_template": qa_prompt_tmpl}
-)
-
-# Save the question and answer to the SQLite3 database
-def save_to_db(question, answer, version):
-    c.execute('INSERT INTO qa (question, answer, version) VALUES (?, ?, ?)', (question, answer, version))
-    conn.commit()
-
-# Fetch all data from the SQLite3 database
-def fetch_from_db():
-    c.execute('SELECT * FROM qa')
-    return c.fetchall()
-
-def main():
-    st.title("How Much Does Mistral 7B Model Know About Wandsworth Council?")
-
-    tab1, tab2, tab3 = st.tabs(["LLM Model Description", "Ask a Question", "View Q&A History"])
-
-    with tab1:
-        st.subheader("LLM Model Description")
-        description = read_description_from_file("tab1_intro.txt")
-        st.write(description)
-
-    with tab2:
-        st.subheader("Ask a Question")
-        question = st.text_input("Enter your question:")
-        if st.button("Get Answer"):
-            if question:
-                try:
-                    response = query_engine.query(question)
-
-                    # Try to extract the generated text
-                    try:
-                        # Extract the text from the response object (assuming it has a `text` attribute or method)
-                        if hasattr(response, 'text'):
-                            answer = response.text
-                        else:
-                            answer = str(response)
-
-                    except AttributeError as e:
-                        st.error(f"Error extracting text from response: {e}")
-                        answer = "Sorry, could not generate an answer."
-
-                    st.write(f"**Answer:** {answer}")
-
-                    # Save question and answer to database
-                    save_to_db(question, answer, version)
-                except Exception as e:
-                    st.error(f"An error occurred: {e}")
-            else:
-                st.warning("Please enter a question")
-
-    with tab3:
-        st.subheader("View Q&A History")
-        qa_data = fetch_from_db()
-        if qa_data:
-            df = pd.DataFrame(qa_data, columns=["Question", "Answer", "Version"])
-            st.dataframe(df)
-        else:
-            st.write("No data available")
-
-if __name__ == "__main__":
-    main()