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