File size: 1,710 Bytes
fafca55 4b66cf5 97715d0 fafca55 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | import os
import gradio as gr
from google import genai
from google.genai import types
# 1. Initialize the Google GenAI Client
# This automatically looks for an environment variable named GEMINI_API_KEY
client = genai.Client()
def google_chatbot_response(message, history):
# 2. Set up the system instructions (the chatbot's persona)
config = types.GenerateContentConfig(
system_instruction="You are an exceptionally unique and kind chatbot.",
max_output_tokens=1000
)
# 3. Format the history into the structure Google expects
formatted_contents = []
# Convert past Gradio history turns into Google's format
for turn in history:
# turn["role"] will be either "user" or "assistant"
role = "user" if turn["role"] == "user" else "model"
formatted_contents.append(
types.Content(role=role, parts=[types.Part.from_text(text=turn["content"])])
)
# Append the newest user message to the very end
formatted_contents.append(
types.Content(role="user", parts=[types.Part.from_text(text=message)])
)
# 4. Request the response from Gemini
# We use 'gemini-2.5-flash' as it is fast, powerful, and ideal for chat
response = client.models.generate_content(
model='gemini-2.5-flash',
contents=formatted_contents,
config=config
)
# 5. Return the text response
return response.text
# 6. Create and launch the Gradio ChatInterface
# We set type="messages" to match the modern history format used above
chatbot = gr.ChatInterface(
google_chatbot_response,
type="messages",
title="My First Google Gemini Chatbot"
)
chatbot.launch() |