Update app.py

app.py

@@ -6,24 +6,47 @@ from sympy import sympify
 
 # Function to extract math problems from an image
 def extract_text_from_image(image):
-    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)  # Convert image to grayscale
-    text = pytesseract.image_to_string(gray)        # Perform OCR to extract text
-    math_problems = re.findall(r'[\d+\-*/().]+', text)  # Filter for potential math expressions
+    # 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 the raw OCR output for debugging purposes
+    print("OCR Output:", text)
+    
+    # Filter out potential math expressions (numbers, operators, and parentheses)
+    math_problems = re.findall(r'[\d+\-*/().÷]+', text)
+    
+    # Print recognized math problems for debugging
+    print("Recognized Problems:", math_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("÷", "/")  # Replace division symbol with "/"
+        
+        # Convert the string expression to a symbolic expression
         expression = sympify(problem)
+        
+        # Evaluate the expression
         result = expression.evalf()
+        
         return result
     except Exception as e:
+        # Return a clear error message for debugging
+        print(f"Error solving problem '{problem}':", e)
         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]
+    
+    # Format the output or return a message if no math problems were detected
     return "\n".join(solutions) if solutions else "No math problems detected."
 
 # Gradio interface