app.py

import os
import gradio as gr
import google.generativeai as genai
from dotenv import load_dotenv

# Load environment variables from .env file
load_dotenv()

# Retrieve API key from environment variable
GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")

# Configure Google Gemini API
genai.configure(api_key=GEMINI_API_KEY)

# Create the model configuration
generation_config = {
    "temperature": 0.7,
    "top_p": 0.95,
    "top_k": 64,
    "max_output_tokens": 512,  # Adjust as needed
    "response_mime_type": "text/plain",
}

# Define safety settings for the model
safety_settings = [
    {"category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_NONE"},
    {"category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "BLOCK_NONE"},
    {"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT", "threshold": "BLOCK_NONE"},
    {"category": "HARM_CATEGORY_DANGEROUS_CONTENT", "threshold": "BLOCK_NONE"},
]

# Function to generate a response based on user input and chat history
def generate_response(user_input, chat_history):
    """Generates a response based on user input and chat history."""

    # Update system content with the full character description
    updated_system_content = "You are Shadow the Hedgehog and you must act like Shadow the Hedgehog's personality."

    # Create the generative model
    model = genai.GenerativeModel(
        model_name="gemini-1.5-pro", 
        generation_config=generation_config,
        safety_settings=safety_settings,
        system_instruction=updated_system_content,
    )

    # Add user input to history
    chat_history.append(user_input)

    # Limit history length to the last 10 messages
    chat_history = chat_history[-10:]

    try:
        # Start a new chat session
        chat_session = model.start_chat()

        # Send the entire chat history as the first message
        response = chat_session.send_message("\n".join(chat_history))
        return response.text, chat_history

    except Exception as e:
        return f"Error: {str(e)}", chat_history

# Build the Gradio interface
with gr.Blocks(theme="Hev832/Applio") as iface:
    chat_input = gr.Textbox(lines=2, label="Talk to AI", placeholder="Enter your message here...")
    chat_history_state = gr.State([])  # State input for chat history
    response_output = gr.Textbox(label="Response")

    iface.interface(
        fn=generate_response,
        inputs=[chat_input, chat_history_state],
        outputs=[response_output, chat_history_state],
        title="Shadow Chat",
        description="<center>Chat with Shadow the Hedgehog!</center>"
    )

iface.launch()