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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 = "AIzaSyA0SnGcdEuesDusLiM93N68-vaFF14RCYg" # public api
# 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",
}
# Simplified safety settings (or try removing them to test)
safety_settings = [
{"category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_NONE"},
{"category": "HARM_CATEGORY_HATE_SPEECH", "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", user_input))
# Limit history length to the last 10 messages
chat_history = chat_history[-10:]
retry_attempts = 3
for attempt in range(retry_attempts):
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([f"{role}: {msg}" for role, msg in chat_history]))
chat_history.append(("assistant", response.text))
return chat_history
except Exception as e:
if attempt < retry_attempts - 1:
continue
else:
chat_history.append(("assistant", f"Error after {retry_attempts} attempts: {str(e)}"))
return chat_history
# Build the Gradio interface using ChatInterface
with gr.Blocks() as iface:
chatbot = gr.Chatbot() # Create a Chatbot component
user_input = gr.Textbox(label="Talk to AI", placeholder="Enter your message here...")
chat_history_state = gr.State([]) # State input for chat history
# Define the layout and components
user_input.submit(
fn=generate_response,
inputs=[user_input, chat_history_state],
outputs=chatbot
)
iface.launch()
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