Delete interim_radio.py

interim_radio.py

@@ -1,161 +0,0 @@
-import streamlit as st
-import pandas as pd
-import sqlite3
-import os
-import json
-from pathlib import Path
-from datetime import datetime, timezone
-from crewai import Agent, Crew, Process, Task
-from crewai_tools import tool
-from langchain_groq import ChatGroq
-from langchain.schema.output import LLMResult
-from langchain_core.callbacks.base import BaseCallbackHandler
-from langchain_community.tools.sql_database.tool import (
-    InfoSQLDatabaseTool,
-    ListSQLDatabaseTool,
-    QuerySQLCheckerTool,
-    QuerySQLDataBaseTool,
-)
-from langchain_community.utilities.sql_database import SQLDatabase
-from datasets import load_dataset
-import tempfile
-
-# API Key
-os.environ["GROQ_API_KEY"] = st.secrets.get("GROQ_API_KEY", "")
-
-# Initialize LLM
-class LLMCallbackHandler(BaseCallbackHandler):
-    def __init__(self, log_path: Path):
-        self.log_path = log_path
-
-    def on_llm_start(self, serialized, prompts, **kwargs):
-        with self.log_path.open("a", encoding="utf-8") as file:
-            file.write(json.dumps({"event": "llm_start", "text": prompts[0], "timestamp": datetime.now().isoformat()}) + "\n")
-
-    def on_llm_end(self, response: LLMResult, **kwargs):
-        generation = response.generations[-1][-1].message.content
-        with self.log_path.open("a", encoding="utf-8") as file:
-            file.write(json.dumps({"event": "llm_end", "text": generation, "timestamp": datetime.now().isoformat()}) + "\n")
-
-llm = ChatGroq(
-    temperature=0,
-    model_name="groq/llama-3.3-70b-versatile",
-    max_tokens=1024,
-    callbacks=[LLMCallbackHandler(Path("prompts.jsonl"))],
-)
-
-st.title("SQL-RAG Using CrewAI 🚀")
-st.write("Analyze datasets using natural language queries powered by SQL and CrewAI.")
-
-# Initialize session state for data persistence
-if "df" not in st.session_state:
-    st.session_state.df = None
-
-# Dataset Input
-input_option = st.radio("Select Dataset Input:", ["Use Hugging Face Dataset", "Upload CSV File"])
-if input_option == "Use Hugging Face Dataset":
-    dataset_name = st.text_input("Enter Hugging Face Dataset Name:", value="Einstellung/demo-salaries")
-    if st.button("Load Dataset"):
-        try:
-            with st.spinner("Loading dataset..."):
-                dataset = load_dataset(dataset_name, split="train")
-                st.session_state.df = pd.DataFrame(dataset)
-                st.success(f"Dataset '{dataset_name}' loaded successfully!")
-                st.dataframe(st.session_state.df.head())
-        except Exception as e:
-            st.error(f"Error: {e}")
-elif input_option == "Upload CSV File":
-    uploaded_file = st.file_uploader("Upload CSV File:", type=["csv"])
-    if uploaded_file:
-        st.session_state.df = pd.read_csv(uploaded_file)
-        st.success("File uploaded successfully!")
-        st.dataframe(st.session_state.df.head())
-
-# SQL-RAG Analysis
-if st.session_state.df is not None:
-    temp_dir = tempfile.TemporaryDirectory()
-    db_path = os.path.join(temp_dir.name, "data.db")
-    connection = sqlite3.connect(db_path)
-    st.session_state.df.to_sql("salaries", connection, if_exists="replace", index=False)
-    db = SQLDatabase.from_uri(f"sqlite:///{db_path}")
-
-    @tool("list_tables")
-    def list_tables() -> str:
-        """List all tables in the database."""
-        return ListSQLDatabaseTool(db=db).invoke("")
-
-    @tool("tables_schema")
-    def tables_schema(tables: str) -> str:
-        """Get schema and sample rows for given tables."""
-        return InfoSQLDatabaseTool(db=db).invoke(tables)
-
-    @tool("execute_sql")
-    def execute_sql(sql_query: str) -> str:
-        """Execute a SQL query against the database."""
-        return QuerySQLDataBaseTool(db=db).invoke(sql_query)
-
-    @tool("check_sql")
-    def check_sql(sql_query: str) -> str:
-        """Check the validity of a SQL query."""
-        return QuerySQLCheckerTool(db=db, llm=llm).invoke({"query": sql_query})
-
-    sql_dev = Agent(
-        role="Senior Database Developer",
-        goal="Extract data using optimized SQL queries.",
-        backstory="An expert in writing optimized SQL queries for complex databases.",
-        llm=llm,
-        tools=[list_tables, tables_schema, execute_sql, check_sql],
-    )
-
-    data_analyst = Agent(
-        role="Senior Data Analyst",
-        goal="Analyze the data and produce insights.",
-        backstory="A seasoned analyst who identifies trends and patterns in datasets.",
-        llm=llm,
-    )
-
-    report_writer = Agent(
-        role="Technical Report Writer",
-        goal="Summarize the insights into a clear report.",
-        backstory="An expert in summarizing data insights into readable reports.",
-        llm=llm,
-    )
-
-    extract_data = Task(
-        description="Extract data based on the query: {query}.",
-        expected_output="Database results matching the query.",
-        agent=sql_dev,
-    )
-
-    analyze_data = Task(
-        description="Analyze the extracted data for query: {query}.",
-        expected_output="Analysis text summarizing findings.",
-        agent=data_analyst,
-        context=[extract_data],
-    )
-
-    write_report = Task(
-        description="Summarize the analysis into an executive report.",
-        expected_output="Markdown report of insights.",
-        agent=report_writer,
-        context=[analyze_data],
-    )
-
-    crew = Crew(
-        agents=[sql_dev, data_analyst, report_writer],
-        tasks=[extract_data, analyze_data, write_report],
-        process=Process.sequential,
-        verbose=True,
-    )
-
-    query = st.text_area("Enter Query:", placeholder="e.g., 'What is the average salary for senior employees?'")
-    if st.button("Submit Query"):
-        with st.spinner("Processing query..."):
-            inputs = {"query": query}
-            result = crew.kickoff(inputs=inputs)
-            st.markdown("### Analysis Report:")
-            st.markdown(result)
-
-    temp_dir.cleanup()
-else:
-    st.info("Please load a dataset to proceed.")