Feature/시각화 코드 작성

interface/lang2sql.py

@@ -11,8 +11,10 @@
 
 from llm_utils.connect_db import ConnectDB
 from llm_utils.graph import builder
+from llm_utils.display_chart import DisplayChart
 from llm_utils.llm_response_parser import LLMResponseParser
 
+
 DEFAULT_QUERY = "고객 데이터를 기반으로 유니크한 유저 수를 카운트하는 쿼리"
 SIDEBAR_OPTIONS = {
     "show_total_token_usage": "Show Total Token Usage",
@@ -177,6 +179,19 @@ def should_show(_key: str) -> bool:
             st.dataframe(df.head(10) if len(df) > 10 else df)
         except Exception as e:
             st.error(f"쿼리 실행 중 오류 발생: {e}")
+    if should_show("show_chart"):
+        df = database.run_sql(sql)
+        st.markdown("**쿼리 결과 시각화:**")
+        display_code = DisplayChart(
+            question=res["refined_input"].content,
+            sql=sql,
+            df_metadata=f"Running df.dtypes gives:\n{df.dtypes}",
+        )
+        # plotly_code 변수도 따로 보관할 필요 없이 바로 그려도 됩니다
+        fig = display_code.get_plotly_figure(
+            plotly_code=display_code.generate_plotly_code(), df=df
+        )
+        st.plotly_chart(fig)
 
 
 db = ConnectDB()

llm_utils/display_chart.py

@@ -0,0 +1,133 @@
+import re
+from llm_utils import llm_factory
+from dotenv import load_dotenv
+from langchain.chains.llm import LLMChain
+from langchain_openai import ChatOpenAI
+from langchain_core.prompts import PromptTemplate
+from langchain_core.messages import HumanMessage, SystemMessage
+import pandas as pd
+import os
+
+import plotly
+import plotly.express as px
+import plotly.graph_objects as go
+
+
+# .env 파일 로딩
+load_dotenv()
+
+
+class DisplayChart:
+    """
+    SQL쿼리가 실행된 결과를 그래프로 시각화하는 Class입니다.
+
+    쿼리 결과를 비롯한 유저 질문, sql를 prompt에 입력하여
+    plotly코드를 출력하여 excute한 결과를 fig 객체로 반환합니다.
+    """
+
+    def __init__(self, question, sql, df_metadata):
+        self.question = question
+        self.sql = sql
+        self.df_metadata = df_metadata
+
+    def llm_model_for_chart(self, message_log):
+        provider = os.getenv("LLM_PROVIDER")
+        if provider == "openai":
+            llm = ChatOpenAI(
+                model=os.getenv("OPEN_AI_LLM_MODEL", "gpt-4o"),
+                api_key=os.getenv("OPEN_AI_KEY"),
+            )
+            result = llm.invoke(message_log)
+            return result
+
+    def _extract_python_code(self, markdown_string: str) -> str:
+        # Strip whitespace to avoid indentation errors in LLM-generated code
+        markdown_string = markdown_string.content.split("```")[1][6:].strip()
+
+        # Regex pattern to match Python code blocks
+        pattern = r"```[\w\s]*python\n([\s\S]*?)```|```([\s\S]*?)```"  # 여러 문자와 공백 뒤에 python이 나오고, 줄바꿈 이후의 모든 내용
+
+        # Find all matches in the markdown string
+        matches = re.findall(
+            pattern, markdown_string, re.IGNORECASE
+        )  # 대소문자 구분 안함
+
+        # Extract the Python code from the matches
+        python_code = []
+        for match in matches:
+            python = match[0] if match[0] else match[1]
+            python_code.append(python.strip())
+
+        if len(python_code) == 0:
+            return markdown_string
+
+        return python_code[0]
+
+    def _sanitize_plotly_code(self, raw_plotly_code):
+        # Remove the fig.show() statement from the plotly code
+        plotly_code = raw_plotly_code.replace("fig.show()", "")
+
+        return plotly_code
+
+    def generate_plotly_code(self) -> str:
+        if self.question is not None:
+            system_msg = f"The following is a pandas DataFrame that contains the results of the query that answers the question the user asked: '{self.question}'"
+        else:
+            system_msg = "The following is a pandas DataFrame "
+
+        if self.sql is not None:
+            system_msg += (
+                f"\n\nThe DataFrame was produced using this query: {self.sql}\n\n"
+            )
+
+        system_msg += f"The following is information about the resulting pandas DataFrame 'df': \n{self.df_metadata}"
+
+        message_log = [
+            SystemMessage(content=system_msg),
+            HumanMessage(
+                content="Can you generate the Python plotly code to chart the results of the dataframe? Assume the data is in a pandas dataframe called 'df'. If there is only one value in the dataframe, use an Indicator. Respond with only Python code. Do not answer with any explanations -- just the code."
+            ),
+        ]
+
+        plotly_code = self.llm_model_for_chart(message_log)
+
+        return self._sanitize_plotly_code(self._extract_python_code(plotly_code))
+
+    def get_plotly_figure(
+        self, plotly_code: str, df: pd.DataFrame, dark_mode: bool = True
+    ) -> plotly.graph_objs.Figure:
+
+        ldict = {"df": df, "px": px, "go": go}
+        try:
+            exec(plotly_code, globals(), ldict)
+            fig = ldict.get("fig", None)
+
+        except Exception as e:
+
+            # Inspect data types
+            numeric_cols = df.select_dtypes(include=["number"]).columns.tolist()
+            categorical_cols = df.select_dtypes(
+                include=["object", "category"]
+            ).columns.tolist()
+
+            # Decision-making for plot type
+            if len(numeric_cols) >= 2:
+                # Use the first two numeric columns for a scatter plot
+                fig = px.scatter(df, x=numeric_cols[0], y=numeric_cols[1])
+            elif len(numeric_cols) == 1 and len(categorical_cols) >= 1:
+                # Use a bar plot if there's one numeric and one categorical column
+                fig = px.bar(df, x=categorical_cols[0], y=numeric_cols[0])
+            elif len(categorical_cols) >= 1 and df[categorical_cols[0]].nunique() < 10:
+                # Use a pie chart for categorical data with fewer unique values
+                fig = px.pie(df, names=categorical_cols[0])
+            else:
+                # Default to a simple line plot if above conditions are not met
+                fig = px.line(df)
+
+        if fig is None:
+            return None
+
+        if dark_mode:
+            fig.update_layout(template="plotly_dark")
+
+        return fig

requirements.txt

@@ -17,5 +17,9 @@ langchain-google-genai>=2.1.3,<3.0.0
 langchain-ollama>=0.3.2,<0.4.0
 langchain-huggingface>=0.1.2,<0.2.0
 clickhouse_driver
+plotly
+matplotlib
+ipython
+kaleido
 numpy<2.0
 pytest>=8.3.5