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app.py

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+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()

requirements.txt

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+streamlit==1.36.0
+pandas==2.2.2
+llama_index==0.10.50
+transformers==4.41.2
+llama_index.llms.ollama
+llama_index.embeddings.huggingface