Upload app.py

app.py

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+import os
+import pandas as pd
+import streamlit as st
+from llama_index.experimental.query_engine import PandasQueryEngine
+from prompts import new_prompt, instruction_str, context
+from note_engine import note_engine
+from llama_index.core.tools import QueryEngineTool, ToolMetadata
+from llama_index.core.agent import ReActAgent
+from llama_index.llms.openai import OpenAI
+from data_summary import data_summary_tool
+from pdf import bangladesh_engine
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+
+# File paths
+conversation_file = os.path.join("data", "conversation.txt")
+summary_file = os.path.join("data", "data_summary.txt")
+population_path = os.path.join("data", "Population.csv")
+population_df = pd.read_csv(population_path)
+
+# Set up the Streamlit app
+st.title("🌎 Population and Bangladesh Data Assistant")
+
+# Sidebar for OpenAI API key
+api_key = st.sidebar.text_input("Enter your OpenAI API Key", type="password")
+if api_key:
+    os.environ["OPENAI_API_KEY"] = api_key
+
+# Initialize query engines
+population_query_engine = PandasQueryEngine(
+    df=population_df, verbose=True, instruction_str=instruction_str
+)
+
+population_query_engine.update_prompts({"pandas_prompt": new_prompt})
+
+tools = [
+    QueryEngineTool(
+        query_engine=population_query_engine,
+        metadata=ToolMetadata(
+            name="population_data",
+            description="Provides information about world population and demographics",
+        ),
+    ),
+    QueryEngineTool(
+        query_engine=bangladesh_engine,
+        metadata=ToolMetadata(
+            name="bangladesh_data",
+            description="Provides detailed information about Bangladesh",
+        ),
+    ),
+]
+
+llm = OpenAI(model="gpt-3.5-turbo")
+agent = ReActAgent.from_tools(tools, llm=llm, verbose=True, context=context)
+
+# Sidebar options
+st.sidebar.header("Options")
+option = st.sidebar.selectbox("Choose an action:", [
+    "Ask a Question",
+    "View Previous Conversations",
+    "View Data Summary",
+    "Save a Note"
+])
+
+# Conversation management
+conversation_active = st.session_state.get('conversation_active', False)
+
+if option == "Ask a Question":
+    if not conversation_active:
+        st.session_state.conversation_active = True
+        st.session_state.conversation_history = []
+    
+    prompt = st.text_area("Enter your query:", key="user_input")
+    
+    if st.button("Submit"):
+        if prompt:
+            result = agent.query(prompt)
+            response_text = result.response  # Extract just the response text
+            st.write("Response:", response_text)  # Show only the response text
+
+            # Save the conversation with a timestamp
+            timestamp = pd.Timestamp.now().strftime("%Y-%m-%d %H:%M:%S")
+            st.session_state.conversation_history.append((timestamp, prompt, response_text))
+        else:
+            st.error("Please enter a query.")
+    
+    if st.button("Save this Conversation"):
+        # Save entire conversation history to the file
+        with open(conversation_file, "a") as file:
+            for timestamp, user_prompt, bot_response in st.session_state.conversation_history:
+                file.write(f"Timestamp: {timestamp}\n")
+                file.write(f"Prompt: {user_prompt}\n")
+                file.write(f"Response: {bot_response}\n")
+                file.write("=" * 40 + "\n")
+        st.success("Conversation saved.")
+
+    if st.button("End Conversation"):
+        st.session_state.conversation_active = False
+        st.success("Conversation ended.")
+
+# View previous conversations
+elif option == "View Previous Conversations":
+    if os.path.exists(conversation_file):
+        with open(conversation_file, "r") as file:
+            st.text_area("Previous Conversations", file.read(), height=300)
+    else:
+        st.warning("No previous conversations found.")
+
+# View data summary
+elif option == "View Data Summary":
+    if os.path.exists(summary_file):
+        with open(summary_file, "r") as file:
+            st.text_area("Data Summary", file.read(), height=300)
+    else:
+        st.warning("No data summary found.")
+
+# Save a note
+elif option == "Save a Note":
+    note = st.text_input("Enter a note to save:")
+    if st.button("Save Note"):
+        if note:
+            # Append note to the conversation file with a timestamp
+            timestamp = pd.Timestamp.now().strftime("%Y-%m-%d %H:%M:%S")
+            with open(conversation_file, "a") as file:
+                file.write(f"Timestamp: {timestamp} (Note)\n")
+                file.write(f"Note: {note}\n")
+                file.write("=" * 40 + "\n")  # Separator for readability
+            st.success("Note saved.")
+        else:
+            st.error("Please enter a note.")
+
+# Instructions
+st.sidebar.subheader("Instructions")
+st.sidebar.write(
+    "1. Enter your OpenAI API Key in the sidebar.\n"
+    "2. Use the sidebar to choose an action: ask a question, view previous conversations, view the data summary, or save a note.\n"
+    "3. If you ask a question and click save, the conversation will be saved. If you ask multiple questions and then press save, it will save the whole conversation.\n"
+    "4. The End Conversation button will simply end the conversation without saving anything.\n"
+)