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"""
App to take in image and output a list of objects in the image
"""
import os
from pathlib import Path
import google.generativeai as genai
import gradio as gr
from dotenv import load_dotenv
load_dotenv() # Load environment variables from .env file
genai.configure(api_key=os.environ["GOOGLE_API_KEY"])
input_prompt = """
Extract the objects in the provided image and output them in a list in alphabetical order
"""
# Set up the model
generation_config = {
"temperature": 0,
"top_p": 1,
"top_k": 32,
"max_output_tokens": 4096,
}
safety_settings = [
{"category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_MEDIUM_AND_ABOVE"},
{"category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "BLOCK_MEDIUM_AND_ABOVE"},
{
"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
"threshold": "BLOCK_MEDIUM_AND_ABOVE",
},
{
"category": "HARM_CATEGORY_DANGEROUS_CONTENT",
"threshold": "BLOCK_MEDIUM_AND_ABOVE",
},
]
model = genai.GenerativeModel(
model_name="gemini-pro-vision",
generation_config=generation_config,
safety_settings=safety_settings,
)
def input_image_setup(file_loc):
if not (img := Path(file_loc)).exists():
raise FileNotFoundError(f"Could not find image: {img}")
image_parts = [{"mime_type": "image/jpeg", "data": Path(file_loc).read_bytes()}]
return image_parts
def generate_gemini_response(input_prompt, image_loc):
image_prompt = input_image_setup(image_loc)
prompt_parts = [input_prompt, image_prompt[0]]
response = model.generate_content(prompt_parts)
output = "The objects in the image are: \n" + response.text
# print(response.text)
return output
def upload_file(file_path):
# print(file_path)
output = generate_gemini_response(input_prompt, file_path)
return file_path, output
with gr.Blocks() as demo:
header = gr.Label("Gemini Pro Vision testing")
image_output = gr.Image()
submit = gr.UploadButton(label="Click to upload the image to be studied", file_count="single", file_types=["image"])
output = gr.Textbox(label="Output")
print("here")
combined_output = [image_output, output]
submit.upload(upload_file, submit, combined_output)
demo.launch(debug=True)
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