app.py

import gradio as gr
import pytesseract
import cv2
import re
from sympy import sympify

# Function to extract math problems from an image
def extract_text_from_image(image):
    # Convert image to grayscale for better OCR performance
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    
    # Perform OCR on the grayscale image
    text = pytesseract.image_to_string(gray)
    print("OCR Output:", text)  # Print raw OCR output
    
    # Filter out potential math expressions
    math_problems = re.findall(r'[\d+\-*/().÷]+', text)
    print("Recognized Problems:", math_problems)  # Print recognized problems
    
    return math_problems

# Function to solve the extracted math problems
def solve_math_problem(problem):
    try:
        # Replace any OCR misinterpretations if needed
        problem = problem.replace("÷", "/")
        print("Processing Problem:", problem)  # Print each problem being processed
        
        # Convert the string expression to a symbolic expression
        expression = sympify(problem)
        
        # Evaluate the expression
        result = expression.evalf()
        print("Result:", result)  # Print result
        
        return result
    except Exception as e:
        print(f"Error solving problem '{problem}':", e)  # Print specific error
        return f"Error: {e}"

# Main function to recognize and solve math problems from an image
def recognize_and_solve(image):
    problems = extract_text_from_image(image)
    solutions = [f"{p} = {solve_math_problem(p)}" for p in problems]
    
    return "\n".join(solutions) if solutions else "No math problems detected."

# Gradio interface
interface = gr.Interface(
    fn=recognize_and_solve,
    inputs="image",
    outputs="text",
    title="Math Problem Recognizer and Solver",
    description="Upload an image containing math problems, and this app will recognize and solve them."
)

# Launch the Gradio app
interface.launch()