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import PIL.Image
import gradio as gr
import base64
import time
import os
import google.generativeai as genai
import pathlib
txt_model = genai.GenerativeModel('gemini-pro')
vis_model = genai.GenerativeModel('gemini-pro-vision')
txt_prompt_1 = """The image contains the contents of a letter. I'd like to follow the request mentioned in the letter. Please provide 3 actionable items to assist me. When responding, use the following format:
# Sender and Subject #
1- Action 1 (no more than 20 words)
2- Action 2 (no more than 20 words)
3- Action 3 (no more than 20 words)
For example:
# From Richard regarding 'Shipping to Customer ABC' #
1- Pack Product A
2- Ship before 3:00 PM today
3- Notify Richard after shipment
"""
txt_display_1 = 'content of email'
import os
GOOGLE_API_KEY=os.getenv('GOOGLE_API_KEY')
genai.configure(api_key=GOOGLE_API_KEY)
# Image to Base 64 Converter
def image_to_base64(image_path):
with open(image_path, 'rb') as img:
encoded_string = base64.b64encode(img.read())
return encoded_string.decode('utf-8')
# Function that takes User Inputs and displays it on ChatUI
def app2_query(history,txt,img):
if not img:
history += [(txt,None)]
return history
base64 = image_to_base64(img)
data_url = f"data:image/jpeg;base64,{base64}"
history += [(f"{txt} ", None)]
return history
# Function that takes User Inputs, generates Response and displays on Chat UI
def app2_response(history,text,img):
if not img:
response = txt_model.generate_content(text)
history += [(None,response.text)]
return history
else:
img = PIL.Image.open(img)
response = vis_model.generate_content([text,img])
history += [(None,response.text)]
return history
# Function that takes User Inputs and displays it on ChatUI
def app1_query(img):
if not img:
return txt_prompt_1
base64 = image_to_base64(img)
data_url = f"data:image/jpeg;base64,{base64}"
outputText = [(f"{txt_display_1} ", None)]
return outputText
# Function that takes User Inputs, generates Response and displays on Chat UI
def app1_response(img):
if not img:
response = txt_model.generate_content(txt_prompt_1)
return response
else:
img = PIL.Image.open(img)
response = vis_model.generate_content([txt_prompt_1,img])
return response.txt
# Interface Code- Selector method
def sentence_builder(animal, place):
return f"""how many {animal}s from the {place} are shown in the picture?"""
# gradio block
with gr.Blocks(theme='snehilsanyal/scikit-learn') as app1:
with gr.Column():
outputbox = gr.Textbox(label="here are the plans...")
image_box = gr.Image(type="filepath")
btn = gr.Button("Make a Plan")
clicked = btn.click(app1_query,
[image_box],
outputbox
).then(app1_response,
[image_box],
outputbox
)
gr.Markdown("""
# Make a Plan #
- screen capture (Win + shift + S)
- click **Make a Plan** to upload
- await LLM Bot (Gemini, in this case) response
- receive THREE actionable items
[demo](https://youtu.be/lJ4jIAEVRNY)
""")
with gr.Blocks(theme='snehilsanyal/scikit-learn') as app2:
gr.Markdown("check the image...")
with gr.Row():
image_box = gr.Image(type="filepath")
chatbot = gr.Chatbot(
scale = 2,
height=750
)
text_box = gr.Dropdown(
["what is in the image",
"provide alternative title for the image",
"how many parts can be seen in the picture?",
"check ID and expiration date"],
label="Select--",
info="ask Bot"
)
btn = gr.Button("Submit")
clicked = btn.click(app2_query,
[chatbot,text_box,image_box],
chatbot
).then(app2_response,
[chatbot,text_box],
chatbot
)
with gr.Blocks(theme='snehilsanyal/scikit-learn') as demo:
gr.Markdown("## Workflow Bot ##")
gr.TabbedInterface([app1, app2], ["Make a Plan!", "Check This!"])
demo.queue()
demo.launch() |