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Update app.py
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app.py
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import os
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import gradio as gr
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from PIL import Image
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from pix2tex.cli import LatexOCR
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latex = latex.strip().rstrip(',.')
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latex = re.sub(r'(\d+)\s*\\pi', r'(\1*3.1416)', latex)
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latex = latex.replace(r'\pi', '3.1416')
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latex = re.sub(r'(\d+)\s*e', r'(\1*2.7183)', latex)
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latex = re.sub(r'(?<![a-zA-Z0-9])e(?![a-zA-Z0-9])', '2.7183', latex)
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latex = re.sub(r'(\d)([a-zA-Z])', r'\1*\2', latex)
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latex = re.sub(r'(\d+)\s*i', r'\1*I', latex)
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latex = re.sub(r'(?<![a-zA-Z0-9])i(?![a-zA-Z0-9])', 'I', latex)
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latex = re.sub(r'\(([^()]+?)\)\s*([xX](\^\d+)?)', r'(\1)*\2', latex)
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if '=' not in latex:
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latex += '=0'
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return latex
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expr = parse_latex(cleaned_latex)
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demo = gr.Interface(
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fn=lambda: "Training not completed yet. Please run `train.py` to generate `trained_model/`.",
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inputs=[],
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outputs="text",
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title="β οΈ Model Not Ready",
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description="Run training first to enable polynomial solving."
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)
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demo.launch()
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import os
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import gradio as gr
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from PIL import Image
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import sympy as sp
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from sympy.parsing.latex import parse_latex
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import re
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try:
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from pix2tex.cli import LatexOCR
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if os.path.exists("trained_model"):
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model = LatexOCR(weights="trained_model")
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else:
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model = None
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except Exception as e:
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model = None
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def preprocess_handwritten_image(pil_img):
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return pil_img.convert('RGB')
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def clean_latex(latex):
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latex = re.sub(r'\\(cal|mathcal)\s*X', 'x', latex)
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latex = latex.replace('{', '').replace('}', '')
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latex = latex.strip().rstrip(',.')
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latex = re.sub(r'(\d+)\s*\\pi', r'(\1*3.1416)', latex)
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latex = latex.replace(r'\pi', '3.1416')
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latex = re.sub(r'(\d+)\s*e', r'(\1*2.7183)', latex)
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latex = re.sub(r'(?<![a-zA-Z0-9])e(?![a-zA-Z0-9])', '2.7183', latex)
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latex = re.sub(r'(\d)([a-zA-Z])', r'\1*\2', latex)
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latex = re.sub(r'(\d+)\s*i', r'\1*I', latex)
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latex = re.sub(r'(?<![a-zA-Z0-9])i(?![a-zA-Z0-9])', 'I', latex)
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latex = re.sub(r'\(([^()]+?)\)\s*([xX](\^\d+)?)', r'(\1)*\2', latex)
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if '=' not in latex:
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latex += '=0'
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return latex
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def solve_polynomial(image):
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if model is None:
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return "β No trained model found. Please run training first to create `trained_model/`."
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try:
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img = preprocess_handwritten_image(image)
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latex_result = model(img)
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cleaned_latex = clean_latex(latex_result)
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expr = parse_latex(cleaned_latex)
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output = f"## π Extracted LaTeX\n```\n{latex_result}\n```\n---\n"
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output += f"## π§Ή Cleaned LaTeX Used\n```\n{cleaned_latex}\n```\n---\n"
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output += f"## π§ Parsed Expression\n\n$$ {sp.latex(expr)} $$\n---\n"
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if isinstance(expr, sp.Equality):
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lhs = expr.lhs - expr.rhs
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output += "## βοΈ Step 1: Standard Form\n"
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output += f"$$ {sp.latex(lhs)} = 0 $$\n---\n"
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output += "## π§© Step 2: Factorized\n"
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output += f"$$ {sp.latex(sp.factor(lhs))} = 0 $$\n---\n"
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output += "## β
Step 3: Roots\n"
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roots = sp.solve(sp.Eq(lhs, 0), dict=True)
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if roots:
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output += "$$\n\\begin{aligned}\n"
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for i, sol in enumerate(roots, 1):
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for var, val in sol.items():
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output += f"\\text{{Root {i}}}: {var} &= {sp.latex(val)}\\\\\n"
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output += "\\end{aligned}\n$$"
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else:
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output += "## β Simplified\n"
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output += f"$$ {sp.latex(sp.simplify(expr))} $$"
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return output
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except Exception as e:
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return f"β **Error**: {str(e)}"
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demo = gr.Interface(
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fn=solve_polynomial,
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inputs=gr.Image(type="pil", label="π· Upload Image"),
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outputs=gr.Markdown(label="π Solution"),
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title="π§ Handwritten Polynomial Solver",
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description="Upload a handwritten polynomial. The app extracts, solves, and explains it step-by-step.",
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allow_flagging="never"
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)
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if __name__ == "__main__":
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demo.launch()
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