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, chat history, and selected character."""
    

    # Update system content with the full character description
    updated_system_content = f"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:]

    # 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 and updated chat history
    return response.text, chat_history


# Build the Gradio interface
with gr.Blocks(title="Hev832/Applio") as iface:
    gr.Interface(
        fn=generate_response,
        inputs=[
            gr.Textbox(lines=2, label="Talk to AI", placeholder="Enter your message here..."),
            gr.State([]),  # State input for chat history
        ],
        outputs=[
            gr.Textbox(label="Response"),
            gr.State([])  # State output to update chat history 
        ],
        title="Shadow Chat",
        description=(
            "<center>Chat with Shadow the Hedgehog!<br>"
            
        )
    )

iface.launch()