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import os
import datetime
import google.generativeai as genai
import streamlit as st
from dotenv import load_dotenv
from typing import Any, Dict, List
from db_ops import get_recent_conversations, init_db, store_conversation_summary, store_message
# Loading environment variables from .env file
load_dotenv()
# Configure Gemini API
gemini_api_key = os.getenv("GEMINI_API_KEY")
genai.configure(api_key=gemini_api_key)
# Intialize the gemini Model
model = genai.GenerativeModel("gemini-2.0-flash")
def chat_with_gemini(prompt:str, chat_history:List[Dict[str, str]]) -> str:
try:
# Format the message for Gemini
messages = []
for msg in chat_history:
if msg["role"] == "user":
messages.append({"role": "user", "parts": msg["content"]})
else:
messages.append({"role": "model", "parts": [msg["content"]]})
# Add current prompt
messages.append({"role": "user", "parts": [prompt]})
# Generate the response from Gemini
chat = model.start_chat(history=messages[:-1])
response = chat.send_message(prompt)
return response.text
except Exception as e:
return f"Error communicating with GEMINI API: {str(e)}"
def summarize_conversations(conversations:List[Dict[str, Any]])->str:
if not conversations:
return "No recent conversations to summarize."
# Format conversations for the model
conversation_texts = []
for idx, conv in enumerate(conversations, 1):
conv_text = f"Conversation {idx} ({conv['timestamp']}):\n"
for msg in conv["messages"]:
conv_text += f"{msg['role'].upper()}: {msg['content']}\n"
conversation_texts.append(conv_text)
prompt = f"""
Please provide a concise summary of the following recent conversations:
{"\n\n".join(conversation_texts)}
Focus on key topics discussed, questions asked, and information provided.
Highlight any recurring themes or import points.
"""
response = model.generate_content(prompt.strip())
return response.text
# Streamlit UI
def main():
st.set_page_config(page_title="Gemini-Chatbot", page_icon="🤖")
st.title("🤖 Gemini AI Chatbot")
# Intialize the database
init_db()
# Intialize session state for chat history and session ID
if "session_id" not in st.session_state:
st.session_state.session_id = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
if "chat_history" not in st.session_state:
st.session_state.chat_history = []
# Chat input area
with st.container():
user_input = st.chat_input("Type your message here...")
if user_input:
# Add user message to chat history
st.session_state.chat_history.append({"role": "user", "content": user_input})
store_message(st.session_state.session_id, "user", user_input)
# Get response from Gemini AI
with st.spinner("Thinking..."):
response = chat_with_gemini(user_input, st.session_state.chat_history)
# Add assistant message to chat history
st.session_state.chat_history.append({"role": "assistant", "content": response})
store_message(st.session_state.session_id, "assistant", response)
# Display the chat history
for message in st.session_state.chat_history:
with st.chat_message(message["role"]):
st.write(message["content"])
# Sidebar for recent conversations
with st.sidebar:
st.title("Conversation Recall")
if st.button("🌸 Summarize Recent conversations"):
with st.spinner("Generating summary..."):
# Get recent conversations
recent_convs = get_recent_conversations(st.session_state.session_id, limit=5)
# Generate summary
summary = summarize_conversations(recent_convs)
# Store summary for recent conversations
if recent_convs:
store_conversation_summary(st.session_state.session_id, recent_convs[0]["id"], summary)
# Display summary
st.subheader("Summary of Recent Conversations")
st.write(summary)
# Clear chat button
if st.button("🗑️ Clear Chat"):
st.session_state.chat_history = []
st.session_state.session_id = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
st.success("Chat history cleared!")
st.rerun()
if __name__ == "__main__":
main() |