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from typing import List, Tuple, Optional
import google.generativeai as genai
import gradio as gr
from PIL import Image
TITLE = """<h1 align="center">Gemini Pro and Pro Vision via API 🚀</h1>"""
DUPLICATE = """
<div style="text-align: center; display: flex; justify-content: center; align-items: center;">
<a href="https://huggingface.co/spaces/SkalskiP/ChatGemini?duplicate=true">
<img src="https://bit.ly/3gLdBN6" alt="Duplicate Space" style="margin-right: 10px;">
</a>
<span>Duplicate the Space and run securely with your GOOGLE API KEY.</span>
</div>
"""
def predict(
google_key: str,
text_prompt: str,
image_prompt: Optional[Image.Image],
chatbot: List[Tuple[str, str]]
) -> Tuple[str, List[Tuple[str, str]]]:
if not google_key:
raise ValueError(
"GOOGLE_API_KEY is not set. "
"Please follow the instructions in the README to set it up.")
genai.configure(api_key=google_key)
if image_prompt is None:
model = genai.GenerativeModel('gemini-pro')
response = model.generate_content(text_prompt, stream=True)
response.resolve()
else:
model = genai.GenerativeModel('gemini-pro-vision')
response = model.generate_content([text_prompt, image_prompt], stream=True)
response.resolve()
chatbot.append((text_prompt, response.text))
return "", chatbot
google_key_component = gr.Textbox(
label="GOOGLE API KEY",
value="",
type="password",
placeholder="...",
info="You have to provide your own GOOGLE_API_KEY for this app to function properly",
)
image_prompt_component = gr.Image(type="pil", label="Image", scale=1)
chatbot_component = gr.Chatbot(label='Gemini', scale=2)
text_prompt_component = gr.Textbox(
placeholder="Hi there!",
label="Ask me anything and press Enter"
)
run_button_component = gr.Button()
inputs = [
google_key_component,
text_prompt_component,
image_prompt_component,
chatbot_component
]
with gr.Blocks() as demo:
gr.HTML(TITLE)
gr.HTML(DUPLICATE)
with gr.Column():
google_key_component.render()
with gr.Row():
image_prompt_component.render()
chatbot_component.render()
text_prompt_component.render()
run_button_component.render()
run_button_component.click(
fn=predict,
inputs=inputs,
outputs=[text_prompt_component, chatbot_component],
)
text_prompt_component.submit(
fn=predict,
inputs=inputs,
outputs=[text_prompt_component, chatbot_component],
)
demo.queue(max_size=99).launch(debug=True)
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