Delete app.py
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app.py
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import PIL.Image
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import gradio as gr
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import base64
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import time
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import os
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import google.generativeai as genai
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import pathlib
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txt_model = genai.GenerativeModel('gemini-pro')
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vis_model = genai.GenerativeModel('gemini-pro-vision')
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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:
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# Sender and Subject #
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1- Action 1 (no more than 20 words)
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2- Action 2 (no more than 20 words)
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3- Action 3 (no more than 20 words)
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For example:
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# From Richard regarding 'Shipping to Customer ABC' #
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1- Pack Product A
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2- Ship before 3:00 PM today
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3- Notify Richard after shipment
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"""
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txt_display_1 = 'content of email'
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import os
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GOOGLE_API_KEY=os.getenv('GOOGLE_API_KEY')
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genai.configure(api_key=GOOGLE_API_KEY)
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# Image to Base 64 Converter
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def image_to_base64(image_path):
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with open(image_path, 'rb') as img:
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encoded_string = base64.b64encode(img.read())
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return encoded_string.decode('utf-8')
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# Function that takes User Inputs and displays it on ChatUI
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def app2_query(history,txt,img):
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if not img:
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history += [(txt,None)]
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return history
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base64 = image_to_base64(img)
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data_url = f"data:image/jpeg;base64,{base64}"
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history += [(f"{txt} ![]({data_url})", None)]
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return history
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# Function that takes User Inputs, generates Response and displays on Chat UI
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def app2_response(history,text,img):
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if not img:
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response = txt_model.generate_content(text)
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history += [(None,response.text)]
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return history
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else:
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img = PIL.Image.open(img)
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response = vis_model.generate_content([text,img])
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history += [(None,response.text)]
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return history
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# Function that takes User Inputs and displays it on ChatUI
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def app3_query(history,img):
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if not img:
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history += [(txt_prompt_1,None)]
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return history
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base64 = image_to_base64(img)
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data_url = f"data:image/jpeg;base64,{base64}"
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history += [(f"![]({data_url})", None)]
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return history
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# Function that takes User Inputs, generates Response and displays on Chat UI
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def app3_response(history,img):
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if not img:
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response = txt_model.generate_content(txt_prompt_1)
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history += [(None,response.text)]
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return history
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else:
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img = PIL.Image.open(img)
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response = vis_model.generate_content([txt_prompt_1,img])
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history += [(None,response.text)]
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return history
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# Interface Code- Selector method
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def sentence_builder(animal, place):
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return f"""how many {animal}s from the {place} are shown in the picture?"""
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# gradio block
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with gr.Blocks(theme='snehilsanyal/scikit-learn') as app1:
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with gr.Column():
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outputbox = gr.Textbox(label="here are the plans...")
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image_box = gr.Image(type="filepath")
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chatbot = gr.Chatbot(
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scale = 2,
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height=750
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)
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btn = gr.Button("Make a Plan")
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clicked = btn.click(app3_query,
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[chatbot,image_box],
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chatbot
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).then(app3_response,
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[chatbot],
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chatbot
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)
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gr.Markdown("""
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# Make a Plan #
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- screen capture (Win + shift + S)
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- click **Make a Plan** to upload
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- await LLM Bot (Gemini, in this case) response
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- receive THREE actionable items
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[demo](https://youtu.be/lJ4jIAEVRNY)
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""")
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with gr.Blocks(theme='snehilsanyal/scikit-learn') as app2:
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gr.Markdown("check the image...")
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with gr.Row():
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image_box = gr.Image(type="filepath")
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chatbot = gr.Chatbot(
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scale = 2,
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height=750
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)
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text_box = gr.Dropdown(
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["what is in the image",
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"provide alternative title for the image",
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"how many parts can be seen in the picture?",
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"check ID and expiration date"],
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label="Select--",
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info="ask Bot"
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)
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btn = gr.Button("Submit")
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clicked = btn.click(app2_query,
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[chatbot,text_box,image_box],
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chatbot
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).then(app2_response,
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[chatbot,text_box],
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chatbot
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)
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with gr.Blocks(theme='snehilsanyal/scikit-learn') as demo:
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gr.Markdown("## Workflow Bot ##")
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gr.TabbedInterface([app1, app2], ["Make a Plan!", "Check This!"])
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demo.queue()
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demo.launch()
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