app.py

import streamlit as st
import pandas as pd
import numpy as np
import google.generativeai as genai

# API key 
genai.configure(api_key="AIzaSyC70u1sN87IkoxOoIj4XCAPw97ae2LwNM")

# Model settings
generation_config = {
  "temperature": 0.9,
  "max_output_tokens": 2048    
}

safety_settings = [
  {
     "category": "HARM_CATEGORY_HARASSMENT",
     "threshold": "BLOCK_MEDIUM_AND_ABOVE"  
  },
  { 
     "category": "HARM_CATEGORY_HATE_SPEECH",
     "threshold": "BLOCK_MEDIUM_AND_ABOVE"
  },
  {
     "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT", 
     "threshold": "BLOCK_MEDIUM_AND_ABOVE"
  },
  {
     "category": "HARM_CATEGORY_DANGEROUS_CONTENT",  
     "threshold": "BLOCK_MEDIUM_AND_ABOVE"
  }
]

model = genai.GenerativeModel(
  model_name="gemini-pro", 
  generation_config=generation_config,
  safety_settings=safety_settings
)

# Chatbot interface
st.title("Gemini API Chatbot")

chat_history = st.session_state.get("chat_history", []) 

chat_container = st.container()

for message in chat_history:
  # Display message
  if message.parts:
    role = "assistant" if message.role == "system" else "user"
    text = message.parts[0].text 
  else:
    role = message.role
    text = message.content
  
  chat_container.markdown(f"**{role}:** {text}")

# Input  
user_input = st.text_input("You")

if user_input:

  # Create message
  user_message = genai.GenerativeContent(
      parts=[genai.Part(text=user_input)], # Wrap in a list
      role=genai.Role.USER
  )

  # Append message  
  chat_history.append(user_message)

  # Display message
  chat_container.markdown(f"**user:** {user_input}")

  # Get response
  with st.spinner("Thinking..."):
    convo = model.start_chat(chat_history)
    response = convo.last

  # Extract response text
  response_text = response.parts[0].text
  
  # Create response message
  assistant_message = genai.GenerativeContent(
      parts=[genai.Part(text=response_text)], # Wrap in a list
      role=genai.Role.ASSISTANT
  )

  # Append response 
  chat_history.append(assistant_message)

  # Display response 
  chat_container.markdown(f"**assistant:** {response_text}")
  
  # Update session state
  st.session_state["chat_history"] = chat_history