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| import gradio as gr | |
| import cv2 | |
| import numpy as np | |
| from PIL import Image | |
| import google.generativeai as genai | |
| from sympy import sympify, solve | |
| import os | |
| from dotenv import load_dotenv | |
| # Load environment variables | |
| load_dotenv() | |
| # Initialize Google Generative AI | |
| def initialize_genai(): | |
| api_key = os.getenv("GOOGLE_API_KEY") | |
| if not api_key: | |
| raise ValueError("Google API Key not found in environment variables.") | |
| genai.configure(api_key=api_key) | |
| def create_prompt(image): | |
| # Adjust the prompt based on how the model expects the input | |
| return "Analyze the following image of an equation. Recognize and solve the equation. Image:" | |
| def recognize_equation_with_genai(image): | |
| try: | |
| # Convert image to text using Google Generative AI with a prompt template | |
| prompt = create_prompt(image) | |
| response = genai.text_detect(image, prompt=prompt) | |
| recognized_text = response.get('text', '') | |
| return recognized_text.strip() | |
| except Exception as e: | |
| return f"Error recognizing text: {str(e)}" | |
| def solve_equation(equation): | |
| try: | |
| expr = sympify(equation) | |
| solutions = solve(expr) | |
| return str(solutions) | |
| except Exception as e: | |
| return f"Error solving equation: {str(e)}" | |
| def process_frame(frame): | |
| # Convert frame to grayscale | |
| gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) | |
| _, thresholded = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY_INV) | |
| # Convert OpenCV image to PIL image | |
| image = Image.fromarray(thresholded) | |
| # Recognize and solve the equation | |
| recognized_equation = recognize_equation_with_genai(image) | |
| solutions = solve_equation(recognized_equation) | |
| return recognized_equation, solutions, image | |
| def main(): | |
| # Initialize Google Generative AI | |
| initialize_genai() | |
| with gr.Blocks() as demo: | |
| gr.Markdown("## Virtual Math Calculator with Google Generative AI") | |
| with gr.Row(): | |
| video_input = gr.Video(source="webcam", type="numpy", label="Record your video") | |
| output_text = gr.Textbox(label="Recognized Equation") | |
| output_solutions = gr.Textbox(label="Solution") | |
| output_image = gr.Image(label="Captured Image") | |
| def process_video(video): | |
| frame = video[0] # Take the first frame from the video | |
| recognized_equation, solutions, image = process_frame(frame) | |
| return recognized_equation, solutions, image | |
| video_input.change(process_video, inputs=video_input, outputs=[output_text, output_solutions, output_image]) | |
| demo.launch() | |
| if __name__ == "__main__": | |
| main() | |