import os import time from typing import List, Tuple, Optional import google.generativeai as genai import gradio as gr from PIL import Image import tempfile import os GOOGLE_API_KEY = os.environ.get("GEMINI_API_KEY") IMAGE_WIDTH = 512 IMAGE_WIDTH = 512 system_instruction_analysis = "You are an expert of the given topic. Analyze the provided text with a focus on the topic, identifying recent issues, recent insights, or improvements relevant to academic standards and effectiveness. Offer actionable advice for enhancing knowledge and suggest real-life examples." model_name = "gemini-2.0-flash-exp" model = genai.GenerativeModel(model_name, system_instruction=system_instruction_analysis) #genai.configure(api_key=google_key) # Helper Functions def preprocess_stop_sequences(stop_sequences: str) -> Optional[List[str]]: return [seq.strip() for seq in stop_sequences.split(",")] if stop_sequences else None def preprocess_image(image: Image.Image) -> Image.Image: image_height = int(image.height * IMAGE_WIDTH / image.width) return image.resize((IMAGE_WIDTH, image_height)) def user(text_prompt: str, chatbot: List[Tuple[str, str]]): return "", chatbot + [[text_prompt, None]] def bot( google_key: str, image_prompt: Optional[Image.Image], temperature: float, max_output_tokens: int, stop_sequences: str, top_k: int, top_p: float, chatbot: List[Tuple[str, str]] ): google_key = google_key or GOOGLE_API_KEY if not google_key: raise ValueError("GOOGLE_API_KEY is not set. Please set it up.") text_prompt = chatbot[-1][0].strip() if chatbot[-1][0] else None # Handle cases for text and/or image input if not text_prompt and not image_prompt: chatbot[-1][1] = "Prompt cannot be empty. Please provide input text or an image." yield chatbot return elif image_prompt and not text_prompt: # If only an image is provided text_prompt = "Describe the image" elif image_prompt and text_prompt: # If both text and image are provided, combine them text_prompt = f"{text_prompt}. Also, analyze the provided image." # Configure the model genai.configure(api_key=google_key) generation_config = genai.types.GenerationConfig( temperature=temperature, max_output_tokens=max_output_tokens, stop_sequences=preprocess_stop_sequences(stop_sequences), top_k=top_k, top_p=top_p, ) # Prepare inputs inputs = [text_prompt] if image_prompt is None else [text_prompt, preprocess_image(image_prompt)] # Generate response try: response = model.generate_content(inputs, stream=True, generation_config=generation_config) response.resolve() except Exception as e: chatbot[-1][1] = f"Error occurred: {str(e)}" yield chatbot return # Stream the response back to the chatbot chatbot[-1][1] = "" for chunk in response: for i in range(0, len(chunk.text), 10): chatbot[-1][1] += chunk.text[i:i + 10] time.sleep(0.01) yield chatbot # Components google_key_component = gr.Textbox( label="Google API Key", type="password", placeholder="Enter your Google API Key", visible=GOOGLE_API_KEY is None ) image_prompt_component = gr.Image(type="pil", label="Input Image (Optional: Figure/Graph)") chatbot_component = gr.Chatbot(label="Chatbot", bubble_full_width=False) text_prompt_component = gr.Textbox( placeholder="Type your question here...", label="Ask", lines=3 ) run_button_component = gr.Button("Submit") temperature_component = gr.Slider( minimum=0, maximum=1.0, value=0.4, step=0.05, label="Creativity (Temperature)", info="Controls the randomness of the response. Higher values result in more creative answers." ) max_output_tokens_component = gr.Slider( minimum=1, maximum=2048, value=1024, step=1, label="Response Length (Token Limit)", info="Sets the maximum number of tokens in the output response." ) stop_sequences_component = gr.Textbox( label="Stop Sequences (Optional)", placeholder="Enter stop sequences, e.g., STOP, END", info="Specify sequences to stop the generation." ) top_k_component = gr.Slider( minimum=1, maximum=40, value=32, step=1, label="Top-K Sampling", info="Limits token selection to the top K most probable tokens. Lower values produce conservative outputs." ) top_p_component = gr.Slider( minimum=0, maximum=1, value=1, step=0.01, label="Top-P Sampling", info="Limits token selection to tokens with a cumulative probability up to P. Lower values produce conservative outputs." ) example_scenarios = [ "Describe Multimodal AI", "What are the difference between muliagent llm and multiagent system", "Why it's difficult to intgrate multimodality in prompt"] example_images = [["ex1.png"],["ex2.png"]] # Gradio Interface user_inputs = [text_prompt_component, chatbot_component] bot_inputs = [ google_key_component, image_prompt_component, temperature_component, max_output_tokens_component, stop_sequences_component, top_k_component, top_p_component, chatbot_component, ] with gr.Blocks(theme="earneleh/paris") as demo: gr.Markdown("

Gemini 2.0 Multimodal Chatbot

") with gr.Row(): google_key_component.render() with gr.Row(): chatbot_component.render() with gr.Row(): with gr.Column(scale=0.5): text_prompt_component.render() with gr.Column(scale=0.5): image_prompt_component.render() with gr.Column(scale=0.5): run_button_component.render() with gr.Accordion("🧪Example Text 💬", open=False): example_radio = gr.Radio( choices=example_scenarios, label="Example Queries", info="Select an example query.") # Debug callback example_radio.change( fn=lambda query: query if query else "No query selected.", inputs=[example_radio], outputs=[text_prompt_component]) # Custom examples section with blue styling with gr.Accordion("🧪Example Image 🩻", open=False): gr.Examples( examples=example_images, inputs=[image_prompt_component], label="Example Figures", ) with gr.Accordion("🛠️Customize", open=False): temperature_component.render() max_output_tokens_component.render() stop_sequences_component.render() top_k_component.render() top_p_component.render() run_button_component.click( fn=user, inputs=user_inputs, outputs=[text_prompt_component, chatbot_component] ).then( fn=bot, inputs=bot_inputs, outputs=[chatbot_component] ) demo.launch()