Download examples/example_math_scripts.py from TextGrad/demo: direct link, hf CLI and curl.
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https://huggingface.co/spaces/TextGrad/demo/resolve/main/examples/example_math_scripts.py
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curl -L -o example_math_scripts.py https://huggingface.co/spaces/TextGrad/demo/resolve/main/examples/example_math_scripts.py
4.08 kB
| import streamlit as st | |
| from streamlit_elements import elements, mui, editor, dashboard | |
| from stqdm import stqdm | |
| import textgrad as tg | |
| import os | |
| class MathSolution: | |
| def __init__(self, data) -> None: | |
| self.data = data | |
| if 'default_initial_solution' not in st.session_state: | |
| st.session_state.default_initial_solution = self.data["default_initial_solution"] | |
| if 'loss_system_prompt' not in st.session_state: | |
| st.session_state.loss_system_prompt = self.data["default_loss_system_prompt"] | |
| if 'instruction' not in st.session_state: | |
| st.session_state.instruction = self.data["instruction"] | |
| if 'current_solution' not in st.session_state: | |
| st.session_state.current_solution = self.data["default_initial_solution"] | |
| self.llm_engine = tg.get_engine("gpt-4o") | |
| print("="*50, "init", "="*50) | |
| self.loss_value = "" | |
| self.gradients = "" | |
| if 'iteration' not in st.session_state: | |
| st.session_state.iteration = 0 | |
| st.session_state.results = [] | |
| tg.set_backward_engine(self.llm_engine, override=True) | |
| def load_layout(self): | |
| def update_solution_content(value): | |
| if st.session_state.iteration == 0: | |
| st.session_state.current_solution = value | |
| # print(f"Code updated: {st.session_state.code_content}") | |
| col1, col2 = st.columns([1, 1]) | |
| with col1: | |
| # self.initial_solution = st.text_area("Initial solution:", self.data["default_initial_solution"], height=300) | |
| solution = st.text_area("Initial solution:", st.session_state.default_initial_solution, height=300) | |
| # Update session state when text changes | |
| if solution is not None and st.session_state.default_initial_solution != solution: | |
| update_solution_content(solution) | |
| with col2: | |
| self.loss_system_prompt = st.text_area("Loss system prompt:", st.session_state.loss_system_prompt, height=300) | |
| def _run(self): | |
| # Set up the textgrad variables | |
| current_solution = st.session_state.current_solution | |
| self.response = tg.Variable(current_solution, | |
| requires_grad=True, | |
| role_description="solution to the math question") | |
| loss_fn = tg.TextLoss(tg.Variable(self.loss_system_prompt, | |
| requires_grad=False, | |
| role_description="system prompt")) | |
| optimizer = tg.TGD([self.response]) | |
| loss = loss_fn(self.response) | |
| self.loss_value = loss.value | |
| self.graph = loss.generate_graph() | |
| loss.backward() | |
| self.gradients = self.response.gradients | |
| optimizer.step() # Let's update the solution | |
| st.session_state.current_solution = self.response.value | |
| def show_results(self): | |
| self._run() | |
| st.session_state.iteration += 1 | |
| st.session_state.results.append({ | |
| 'iteration': st.session_state.iteration, | |
| 'loss_value': self.loss_value, | |
| 'response': self.response.value, | |
| 'gradients': self.gradients | |
| }) | |
| tabs = st.tabs([f"Iteration {i+1}" for i in range(st.session_state.iteration)]) | |
| for i, tab in enumerate(tabs): | |
| with tab: | |
| result = st.session_state.results[i] | |
| st.markdown(f"Current iteration: **{result['iteration']}**") | |
| st.markdown("## Current solution:") | |
| st.markdown(result['response']) | |
| col1, col2 = st.columns([1, 1]) | |
| with col1: | |
| st.markdown("### Loss value") | |
| st.markdown("**Loss value is based on previous code.**") | |
| st.markdown(result['loss_value']) | |
| with col2: | |
| st.markdown("### Code gradients") | |
| for j, g in enumerate(result['gradients']): | |
| st.markdown(f"### Gradient") | |
| st.markdown(g.value) |