examples/code_editor_scripts.py

import streamlit as st
from streamlit_elements import elements, mui, editor, dashboard
from stqdm import stqdm
import textgrad as tg
import os

class CodeEditor:
    def __init__(self, data) -> None:
        self.data = data
        # Initialize only if not already set to ensure it retains the original content
        if 'original_code_content' not in st.session_state:
            st.session_state.original_code_content = self.data["default_initial_solution"]

        self.llm_engine = tg.get_engine("gpt-4o")
        print("="*50, "init", "="*50)
        self.loss_value = ""
        self.code_gradients = ""
        if 'iteration' not in st.session_state:
            st.session_state.iteration = 0
        if 'results' not in st.session_state:
            st.session_state.results = []
        tg.set_backward_engine(self.llm_engine, override=True)
        

    def load_layout(self):
        # Initialize session state for problem description and other fields if not already set
        if 'problem' not in st.session_state:
            st.session_state.problem = self.data["default_problem_description"]
        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"]

        col1, col2 = st.columns([1, 1])
        with col1:
            st.session_state.problem = st.text_area("Problem description:", st.session_state.problem, height=300)
        with col2:
            st.session_state.loss_system_prompt = st.text_area("Loss system prompt:", st.session_state.loss_system_prompt, height=150)
            st.session_state.instruction = st.text_area("Instruction for formatted LLM call:", st.session_state.instruction, height=100)

        # Assume the code content also needs to be persistent
        if 'code_content' not in st.session_state:
            st.session_state.code_content = self.data["default_initial_solution"]

        def update_code_content(value):
            if st.session_state.iteration == 0:
                st.session_state.code_content = value
            # print(f"Code updated: {st.session_state.code_content}")

        col1, col2 = st.columns(2)
        with col1:
            with elements("monaco_editors_widget_original"):
                st.markdown(f"**Initial solution:**")
                # code = editor.Monaco(
                #     height=300,
                #     defaultLanguage="python",
                #     defaultValue=st.session_state.original_code_content,
                #     onChange=update_code_content,
                #     label="Initial Solution Viewer",
                # )

                code = st.text_area("Edit your code here:", st.session_state.original_code_content, height=300)
                # Update session state when text changes
                if code is not None and st.session_state.original_code_content != code:
                    update_code_content(code)

                # if st.session_state.code_content != code:
                #     update_code_content(code)
        # with col2:

    def _run(self):
        # Code is the variable of interest we want to optimize -- so requires_grad=True
        solution = st.session_state.code_content
        code = tg.Variable(value=solution,
                        requires_grad=True,
                        role_description="code instance to optimize")

        # We are not interested in optimizing the problem -- so requires_grad=False
        problem = tg.Variable(st.session_state.problem, 
                            requires_grad=False, 
                            role_description="the coding problem")

        # Let TGD know to update code!
        optimizer = tg.TGD(parameters=[code])


        instruction = st.session_state.instruction
        llm_engine = self.llm_engine
        loss_system_prompt = st.session_state.loss_system_prompt
        loss_system_prompt = tg.Variable(loss_system_prompt, requires_grad=False, role_description="system prompt to the loss function")

        format_string = "{instruction}\nProblem: {{problem}}\nCurrent Code: {{code}}"
        format_string = format_string.format(instruction=st.session_state.instruction)

        fields = {"problem": None, "code": None}
        formatted_llm_call = tg.autograd.FormattedLLMCall(engine=self.llm_engine,
                                                        format_string=format_string,
                                                        fields=fields,
                                                        system_prompt=loss_system_prompt)
        # Finally, the loss function
        def loss_fn(problem: tg.Variable, code: tg.Variable) -> tg.Variable:
            inputs = {"problem": problem, "code": code}
            
            return formatted_llm_call(inputs=inputs,
                                    response_role_description=f"evaluation of the {code.get_role_description()}")
        loss = loss_fn(problem, code)
        self.loss_value = loss.value
        self.graph = loss.generate_graph()

        loss.backward()
        self.gradients = code.gradients
        optimizer.step() # Let's update the code

        st.session_state.code_content = code.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,
            'gradients': self.gradients,
            'code_content': st.session_state.code_content,
        })

        tabs = st.tabs([f"Iteration {i+1}" for i in range(st.session_state.iteration)])

        # Include Highlight.js library and a theme CSS
        st.markdown("""
        <link rel="stylesheet"
            href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.5.0/styles/default.min.css">
        <script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.5.0/highlight.min.js"></script>
        <script>hljs.highlightAll();</script>
        """, unsafe_allow_html=True)

        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(f"""
                    <pre><code class="language-python">{result["code_content"]}</code></pre>
                """, unsafe_allow_html=True)
                

                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 {j}")
                        st.markdown(g.value)
import streamlit as st
from streamlit_elements import elements, mui, editor, dashboard
from stqdm import stqdm
import textgrad as tg
import os

class CodeEditor:
    def __init__(self, data) -> None:
        self.data = data
        # Initialize only if not already set to ensure it retains the original content
        if 'original_code_content' not in st.session_state:
            st.session_state.original_code_content = self.data["default_initial_solution"]

        self.llm_engine = tg.get_engine("gpt-4o")
        print("="*50, "init", "="*50)
        self.loss_value = ""
        self.code_gradients = ""
        if 'iteration' not in st.session_state:
            st.session_state.iteration = 0
        if 'results' not in st.session_state:
            st.session_state.results = []
        tg.set_backward_engine(self.llm_engine, override=True)
        

    def load_layout(self):
        # Initialize session state for problem description and other fields if not already set
        if 'problem' not in st.session_state:
            st.session_state.problem = self.data["default_problem_description"]
        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"]

        col1, col2 = st.columns([1, 1])
        with col1:
            st.session_state.problem = st.text_area("Problem description:", st.session_state.problem, height=300)
        with col2:
            st.session_state.loss_system_prompt = st.text_area("Loss system prompt:", st.session_state.loss_system_prompt, height=150)
            st.session_state.instruction = st.text_area("Instruction for formatted LLM call:", st.session_state.instruction, height=100)

        # Assume the code content also needs to be persistent
        if 'code_content' not in st.session_state:
            st.session_state.code_content = self.data["default_initial_solution"]

        def update_code_content(value):
            if st.session_state.iteration == 0:
                st.session_state.code_content = value
            # print(f"Code updated: {st.session_state.code_content}")

        col1, col2 = st.columns(2)
        with col1:
            with elements("monaco_editors_widget_original"):
                st.markdown(f"**Initial solution:**")
                # code = editor.Monaco(
                #     height=300,
                #     defaultLanguage="python",
                #     defaultValue=st.session_state.original_code_content,
                #     onChange=update_code_content,
                #     label="Initial Solution Viewer",
                # )

                code = st.text_area("Edit your code here:", st.session_state.original_code_content, height=300)
                # Update session state when text changes
                if code is not None and st.session_state.original_code_content != code:
                    update_code_content(code)

                # if st.session_state.code_content != code:
                #     update_code_content(code)
        # with col2:

    def _run(self):
        # Code is the variable of interest we want to optimize -- so requires_grad=True
        solution = st.session_state.code_content
        code = tg.Variable(value=solution,
                        requires_grad=True,
                        role_description="code instance to optimize")

        # We are not interested in optimizing the problem -- so requires_grad=False
        problem = tg.Variable(st.session_state.problem, 
                            requires_grad=False, 
                            role_description="the coding problem")

        # Let TGD know to update code!
        optimizer = tg.TGD(parameters=[code])


        instruction = st.session_state.instruction
        llm_engine = self.llm_engine
        loss_system_prompt = st.session_state.loss_system_prompt
        loss_system_prompt = tg.Variable(loss_system_prompt, requires_grad=False, role_description="system prompt to the loss function")

        format_string = "{instruction}\nProblem: {{problem}}\nCurrent Code: {{code}}"
        format_string = format_string.format(instruction=st.session_state.instruction)

        fields = {"problem": None, "code": None}
        formatted_llm_call = tg.autograd.FormattedLLMCall(engine=self.llm_engine,
                                                        format_string=format_string,
                                                        fields=fields,
                                                        system_prompt=loss_system_prompt)
        # Finally, the loss function
        def loss_fn(problem: tg.Variable, code: tg.Variable) -> tg.Variable:
            inputs = {"problem": problem, "code": code}
            
            return formatted_llm_call(inputs=inputs,
                                    response_role_description=f"evaluation of the {code.get_role_description()}")
        loss = loss_fn(problem, code)
        self.loss_value = loss.value
        self.graph = loss.generate_graph()

        loss.backward()
        self.gradients = code.gradients
        optimizer.step() # Let's update the code

        st.session_state.code_content = code.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,
            'gradients': self.gradients,
            'code_content': st.session_state.code_content,
        })

        tabs = st.tabs([f"Iteration {i+1}" for i in range(st.session_state.iteration)])

        # Include Highlight.js library and a theme CSS
        st.markdown("""
        <link rel="stylesheet"
            href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.5.0/styles/default.min.css">
        <script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.5.0/highlight.min.js"></script>
        <script>hljs.highlightAll();</script>
        """, unsafe_allow_html=True)

        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(f"""
                    <pre><code class="language-python">{result["code_content"]}</code></pre>
                """, unsafe_allow_html=True)
                

                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 {j}")
                        st.markdown(g.value)