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app.py
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# import required packages
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import google.generativeai as genai
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import os
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import PIL.Image
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import gradio as gr
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from gradio_multimodalchatbot import MultimodalChatbot
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from gradio.data_classes import FileData
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# For better security practices, retrieve sensitive information like API keys from environment variables.
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# Fetch an environment variable.
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GOOGLE_API_KEY = os.environ.get('AIzaSyCXIlf8IiA0Ew8i0-Ki1ObN2kJCIzLghfU')
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genai.configure(api_key=AIzaSyCXIlf8IiA0Ew8i0-Ki1ObN2kJCIzLghfU)
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# These codelines are just to verify if your api key is correct or not
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# Use them when you clone the repo and build locally
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#!curl \
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#-H 'Content-Type: application/json' \
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#-d '{ "prompt": { "text": "Write a very short story about a magic backpack"} }' \
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#"https://generativelanguage.googleapis.com/v1beta3/models/text-bison-001:generateText?key=<enter-your-key-here>"
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# Initialize genai models
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model = genai.GenerativeModel('gemini-pro')
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modelvis = genai.GenerativeModel('gemini-pro-vision')
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def gemini(input, file, chatbot=[]):
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"""
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Function to handle gemini model and gemini vision model interactions.
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Parameters:
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input (str): The input text.
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file (File): An optional file object for image processing.
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chatbot (list): A list to keep track of chatbot interactions.
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Returns:
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tuple: Updated chatbot interaction list, an empty string, and None.
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"""
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messages = []
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print(chatbot)
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# Process previous chatbot messages if present
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if len(chatbot) != 0:
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for user, bot in chatbot:
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user, bot = user.text, bot.text
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messages.extend([
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{'role': 'user', 'parts': [user]},
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{'role': 'model', 'parts': [bot]}
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])
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messages.append({'role': 'user', 'parts': [input]})
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else:
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messages.append({'role': 'user', 'parts': [input]})
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try:
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# Process image if file is provided
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if file is not None:
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with PIL.Image.open(file.name) as img:
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message = [{'role': 'user', 'parts': [input, img]}]
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response = modelvis.generate_content(message)
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gemini_video_resp = response.text
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messages.append({'role': 'model', 'parts': [gemini_video_resp]})
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# Construct list of messages in the required format
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user_msg = {"text": input, "files": [{"file": FileData(path=file.name)}]}
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bot_msg = {"text": gemini_video_resp, "files": []}
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chatbot.append([user_msg, bot_msg])
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else:
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response = model.generate_content(messages)
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gemini_resp = response.text
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# Construct list of messages in the required format
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user_msg = {"text": input, "files": []}
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bot_msg = {"text": gemini_resp, "files": []}
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chatbot.append([user_msg, bot_msg])
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except Exception as e:
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# Handling exceptions and raising error to the modal
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print(f"An error occurred: {e}")
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raise gr.Error(e)
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return chatbot, "", None
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# Define the Gradio Blocks interface
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with gr.Blocks() as demo:
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# Add a centered header using HTML
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gr.HTML("<center><h1>Gemini-PRO & Gemini-PRO-Vision API</h1></center>")
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# Initialize the MultimodalChatbot component
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multi = MultimodalChatbot(value=[], height=800)
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with gr.Row():
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# Textbox for user input with increased scale for better visibility
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tb = gr.Textbox(scale=4, placeholder='Input text and press Enter')
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# Upload button for image files
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up = gr.UploadButton("Upload Image", file_types=["image"], scale=1)
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# Define the behavior on text submission
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tb.submit(gemini, [tb, up, multi], [multi, tb, up])
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# Define the behavior on image upload
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# Using chained then() calls to update the upload button's state
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up.upload(lambda: gr.UploadButton("Uploading Image..."), [], up) \
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.then(lambda: gr.UploadButton("Image Uploaded"), [], up) \
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.then(lambda: gr.UploadButton("Upload Image"), [], up)
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# Launch the demo with a queue to handle multiple users
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demo.queue().launch()
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