mirror of
https://github.com/Shubhamsaboo/awesome-llm-apps.git
synced 2026-05-02 01:00:07 -05:00
code is perfect
This commit is contained in:
Madhu
@@ -1,202 +1,214 @@
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import os
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import json
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import re
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import sys
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import io
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import contextlib
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import warnings
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from typing import Optional, List, Any, Tuple
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from dotenv import load_dotenv
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from PIL import Image
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import streamlit as st
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import pandas as pd
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import tempfile
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import re
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from together import Together
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import csv
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from dotenv import load_dotenv
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import base64
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import matplotlib.pyplot as plt
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import io
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import seaborn as sns
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from io import BytesIO
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from together import Together
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from e2b_code_interpreter import Sandbox
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# Load environment variables
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load_dotenv()
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# Suppress Pydantic warnings globally
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warnings.filterwarnings("ignore", category=UserWarning, module="pydantic")
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# Function to preprocess and save the uploaded file to a temporary file
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def preprocess_and_save(file):
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try:
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# Read the uploaded file into a DataFrame
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if file.name.endswith('.csv'):
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df = pd.read_csv(file, encoding='utf-8', na_values=['NA', 'N/A', 'missing'])
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elif file.name.endswith('.xlsx'):
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df = pd.read_excel(file, na_values=['NA', 'N/A', 'missing'])
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else:
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st.error("Unsupported file format. Please upload a CSV or Excel file.")
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return None, None, None
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# Ensure string columns are properly quoted
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for col in df.select_dtypes(include=['object']):
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df[col] = df[col].astype(str).replace({r'"': '""'}, regex=True)
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# Parse dates and numeric columns
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for col in df.columns:
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if 'date' in col.lower():
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df[col] = pd.to_datetime(df[col], errors='coerce')
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elif df[col].dtype == 'object':
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try:
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# Handle columns with values like "4.1/5"
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if df[col].str.contains('/').any():
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# Split the values and take the first part (e.g., "4.1/5" -> 4.1)
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df[col] = df[col].str.split('/').str[0]
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# Convert to numeric, coerce errors to NaN
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df[col] = pd.to_numeric(df[col], errors='coerce')
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except (ValueError, TypeError):
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# Keep as is if conversion fails
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st.warning(f"Could not convert column '{col}' to numeric. Keeping as string.")
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pass
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# Drop rows with all NaN values
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df.dropna(how='all', inplace=True)
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# Create a temporary file to save the preprocessed data
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with tempfile.NamedTemporaryFile(delete=False, suffix=".csv") as temp_file:
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temp_path = temp_file.name
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# Save the DataFrame to the temporary CSV file
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df.to_csv(temp_path, index=False, quoting=csv.QUOTE_ALL)
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return temp_path, df.columns.tolist(), df # Return the DataFrame as well
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except Exception as e:
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st.error(f"Error processing file: {e}")
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return None, None, None
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# Regex pattern to extract code from LLM response
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pattern = re.compile(r"```python\n(.*?)\n```", re.DOTALL)
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# Function to execute Python code and generate plots
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def execute_code(code: str, df):
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try:
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# Define locals with necessary imports and the DataFrame
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local_env = {
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'pd': pd,
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'df': df,
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'plt': plt,
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'sns': sns # if seaborn is needed
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}
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# Execute the code in the local environment
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exec(code, globals(), local_env)
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def code_interpret(e2b_code_interpreter: Sandbox, code: str) -> Optional[List[Any]]:
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"""
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Runs the given Python code in the E2B sandbox.
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Args:
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e2b_code_interpreter: The E2B sandbox instance
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code: Python code to execute
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# Check if a plot was generated
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if 'plt' in local_env:
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# Save the plot to a BytesIO object
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buf = io.BytesIO()
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plt.savefig(buf, format='png')
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plt.close()
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buf.seek(0)
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# Encode the plot as base64
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base64_image = base64.b64encode(buf.read()).decode('utf-8')
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return base64_image
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else:
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st.warning("No plot generated. Ensure the data being plotted is numeric.")
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Returns:
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Optional[List[Any]]: Results from code execution
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"""
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with st.spinner('Executing code in E2B sandbox...'):
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# Capture stdout and stderr
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stdout_capture = io.StringIO()
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stderr_capture = io.StringIO()
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with contextlib.redirect_stdout(stdout_capture), contextlib.redirect_stderr(stderr_capture):
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# Suppress warnings during code execution
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with warnings.catch_warnings():
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warnings.simplefilter("ignore")
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exec = e2b_code_interpreter.run_code(code)
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# Log stderr (warnings and errors) to the terminal
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if stderr_capture.getvalue():
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print("[Code Interpreter Warnings/Errors]", file=sys.stderr)
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print(stderr_capture.getvalue(), file=sys.stderr)
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# Log stdout (normal output) to the terminal
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if stdout_capture.getvalue():
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print("[Code Interpreter Output]", file=sys.stdout)
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print(stdout_capture.getvalue(), file=sys.stdout)
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if exec.error:
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print(f"[Code Interpreter ERROR] {exec.error}", file=sys.stderr)
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return None
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except Exception as e:
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st.error(f"Error executing code: {e}")
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return None
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return exec.results
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# Function to communicate with Together AI
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def chat_with_llm(user_message, file_path, columns, df):
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print(f"\n{'='*50}\nUser message: {user_message}\n{'='*50}")
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def match_code_blocks(llm_response: str) -> str:
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"""
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Extracts Python code blocks from the LLM response.
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Args:
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llm_response: The response from the LLM
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Returns:
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str: Extracted Python code or empty string
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"""
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match = pattern.search(llm_response)
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if match:
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code = match.group(1)
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return code
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return ""
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# Update the system prompt with the file path, columns, and plot path
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system_prompt = SYSTEM_PROMPT.format(
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file_path=file_path,
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columns=columns,
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)
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# Add a hint to include a plot if the user asks for visualization
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if "plot" in user_message.lower():
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system_prompt += " Include a plot in your response and output the base64 string of the plot image."
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def chat_with_llm(e2b_code_interpreter: Sandbox, user_message: str, dataset_path: str) -> Tuple[Optional[List[Any]], str]:
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"""
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Sends the user message to the LLM and executes the generated code.
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Args:
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e2b_code_interpreter: The E2B sandbox instance
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user_message: User's query message
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dataset_path: Path to the uploaded dataset
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Returns:
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Tuple[Optional[List[Any]], str]: Code execution results and LLM response
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"""
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# Update system prompt to include dataset path information
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system_prompt = f"""You're a Python data scientist and data visualization expert. You are given a dataset at path '{dataset_path}' and also the user's query.
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You need to analyze the dataset and answer the user's query with a response and you run Python code to solve them.
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IMPORTANT: Always use the dataset path variable '{dataset_path}' in your code when reading the CSV file."""
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messages = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_message},
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]
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# Use the Together API key from session state
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response = client.chat.completions.create(
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model="meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo",
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messages=messages,
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)
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with st.spinner('Getting response from Together AI LLM model...'):
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client = Together(api_key=st.session_state.together_api_key)
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response = client.chat.completions.create(
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model=st.session_state.model_name,
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messages=messages,
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)
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response_message = response.choices[0].message.content
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print("LLM Response:", response_message) # Debug: Print the LLM's response
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python_code = match_code_blocks(response_message)
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print("Extracted Python Code:", python_code) # Debug: Print the extracted code
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if python_code:
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# Execute the code and generate the plot
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base64_image = execute_code(python_code, df)
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return response_message, base64_image
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else:
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print(f"Failed to match any Python code in model's response {response_message}")
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return response_message, None
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# Set up Streamlit app
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st.title("AI Data Visualisation Agent")
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# Sidebar for API keys and file upload
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st.sidebar.header("API Keys")
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together_api_key = st.sidebar.text_input("Together AI API Key", type="password")
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# Store API key in session state
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if 'together_api_key' not in st.session_state:
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st.session_state.together_api_key = None
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uploaded_file = st.sidebar.file_uploader("Upload CSV or Excel File", type=['csv', 'xlsx'])
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# System prompt (dynamic based on the uploaded file)
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SYSTEM_PROMPT = """
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You are a Python data scientist and Visualisation expert. You have access to a CSV file located at '{file_path}'.
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The dataset has the following columns: {columns}.
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You can read this file into a DataFrame using `df = pd.read_csv('{file_path}')` and perform data analysis tasks based on user queries.
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Make sure to handle missing values and data type inconsistencies. When generating plots,
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use matplotlib or seaborn and output the plot as a base64 string.
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Always check if the data being plotted is numeric. If the data is not numeric, preprocess it to convert it to numeric values.
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Always respond with the Python code to answer the user's query, and include visualizations only if explicitly requested.
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"""
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# Function to match Python code blocks
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pattern = re.compile(r"```python\n(.*?)\n```", re.DOTALL)
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def match_code_blocks(llm_response):
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match = pattern.search(llm_response)
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if match:
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code = match.group(1)
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# Remove comments and extra text
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code = "\n".join([line for line in code.split("\n") if not line.strip().startswith("#")])
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return code
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return ""
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# Main app logic
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if uploaded_file:
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if not together_api_key:
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st.warning("Please provide the Together AI API key.")
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else:
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# Update session state with API key
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st.session_state.together_api_key = together_api_key
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response_message = response.choices[0].message
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python_code = match_code_blocks(response_message.content)
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# Initialize Together AI client only after confirming API key exists
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try:
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client = Together(api_key=together_api_key)
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# Preprocess and save the uploaded file
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temp_path, columns, df = preprocess_and_save(uploaded_file)
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if temp_path:
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# Rest of your code for user query handling
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user_query = st.text_input("Ask a query about the data:")
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if st.button("Submit Query"):
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response_message, base64_image = chat_with_llm(user_query, temp_path, columns, df)
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# Display AI's response
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st.write("AI's Response:")
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st.write(response_message)
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# Display the plot if generated
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if base64_image:
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st.image(base64.b64decode(base64_image), use_container_width=True)
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else:
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st.write("No plot generated.")
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if python_code:
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code_interpreter_results = code_interpret(e2b_code_interpreter, python_code)
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return code_interpreter_results, response_message.content
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else:
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st.warning(f"Failed to match any Python code in model's response")
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return None, response_message.content
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def upload_dataset(code_interpreter: Sandbox, uploaded_file) -> str:
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"""
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Uploads the dataset to the E2B sandbox.
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Args:
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code_interpreter: The E2B sandbox instance
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uploaded_file: Streamlit uploaded file
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Returns:
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str: Path where file was uploaded
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"""
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dataset_path = f"./{uploaded_file.name}"
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try:
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code_interpreter.files.write(dataset_path, uploaded_file)
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return dataset_path
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except Exception as error:
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st.error(f"Error during file upload: {error}")
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raise error
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def main():
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"""Main Streamlit application."""
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st.title("AI Data Visualization Agent")
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st.write("Upload your dataset and ask questions about it!")
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# Sidebar for API keys and model selection
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with st.sidebar:
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st.header("API Keys and Model Configuration")
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st.session_state.together_api_key = st.text_input("Enter Together API Key", type="password")
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st.session_state.e2b_api_key = st.text_input("Enter E2B API Key", type="password")
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# Add model selection dropdown
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model_options = {
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"Meta-Llama 3.1 405B": "meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo",
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"DeepSeek V3": "deepseek-ai/DeepSeek-V3",
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"Qwen 2.5 7B": "Qwen/Qwen2.5-7B-Instruct-Turbo",
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"Meta-Llama 3.3 70B": "meta-llama/Llama-3.3-70B-Instruct-Turbo"
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}
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selected_model = st.selectbox(
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"Select Model",
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options=list(model_options.keys()),
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index=0 # Default to first option
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)
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st.session_state.model_name = model_options[selected_model]
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uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
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if uploaded_file is not None:
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# Display dataset with toggle
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df = pd.read_csv(uploaded_file)
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st.write("Dataset:")
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show_full = st.checkbox("Show full dataset")
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if show_full:
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st.dataframe(df)
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else:
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st.write("Preview (first 5 rows):")
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st.dataframe(df.head())
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# Query input
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query = st.text_area("What would you like to know about your data?",
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"Can you compare the average cost for two people between different categories?")
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if st.button("Analyze"):
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if not st.session_state.together_api_key or not st.session_state.e2b_api_key:
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st.error("Please enter both API keys in the sidebar.")
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else:
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st.error("Failed to preprocess and save the data.")
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except Exception as e:
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st.error(f"Error initializing Together AI client: {str(e)}")
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else:
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st.warning("Please upload a file.")
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with Sandbox(api_key=st.session_state.e2b_api_key) as code_interpreter:
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# Upload the dataset
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dataset_path = upload_dataset(code_interpreter, uploaded_file)
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# Pass dataset_path to chat_with_llm
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code_results, llm_response = chat_with_llm(code_interpreter, query, dataset_path)
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# Display LLM's text response
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st.write("AI Response:")
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st.write(llm_response)
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# Display results/visualizations
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if code_results:
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for result in code_results:
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if hasattr(result, 'png') and result.png: # Check if PNG data is available
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# Decode the base64-encoded PNG data
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png_data = base64.b64decode(result.png)
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# Convert PNG data to an image and display it
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image = Image.open(BytesIO(png_data))
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st.image(image, caption="Generated Visualization", use_container_width=False)
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elif hasattr(result, 'figure'): # For matplotlib figures
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fig = result.figure # Extract the matplotlib figure
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st.pyplot(fig) # Display using st.pyplot
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elif hasattr(result, 'show'): # For plotly figures
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st.plotly_chart(result)
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elif isinstance(result, (pd.DataFrame, pd.Series)):
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st.dataframe(result)
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else:
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st.write(result)
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if __name__ == "__main__":
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main()
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@@ -1,179 +0,0 @@
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||||
import os
|
||||
import json
|
||||
import re
|
||||
from typing import Optional, List, Any, Tuple
|
||||
from dotenv import load_dotenv
|
||||
from PIL import Image
|
||||
import io
|
||||
import streamlit as st
|
||||
import pandas as pd
|
||||
import base64
|
||||
from io import BytesIO
|
||||
from PIL import Image
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||||
from together import Together
|
||||
from e2b_code_interpreter import Sandbox
|
||||
|
||||
# Load environment variables
|
||||
load_dotenv()
|
||||
|
||||
# Regex pattern to extract code from LLM response
|
||||
pattern = re.compile(r"```python\n(.*?)\n```", re.DOTALL)
|
||||
|
||||
def code_interpret(e2b_code_interpreter: Sandbox, code: str) -> Optional[List[Any]]:
|
||||
"""
|
||||
Runs the given Python code in the E2B sandbox.
|
||||
|
||||
Args:
|
||||
e2b_code_interpreter: The E2B sandbox instance
|
||||
code: Python code to execute
|
||||
|
||||
Returns:
|
||||
Optional[List[Any]]: Results from code execution
|
||||
"""
|
||||
with st.spinner('Executing code in E2B sandbox...'):
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||||
exec = e2b_code_interpreter.run_code(code,
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||||
on_stderr=lambda stderr: st.error(f"[Code Interpreter] {stderr}"),
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||||
on_stdout=lambda stdout: st.info(f"[Code Interpreter] {stdout}"))
|
||||
|
||||
if exec.error:
|
||||
st.error(f"[Code Interpreter ERROR] {exec.error}")
|
||||
return None
|
||||
return exec.results
|
||||
|
||||
def match_code_blocks(llm_response: str) -> str:
|
||||
"""
|
||||
Extracts Python code blocks from the LLM response.
|
||||
|
||||
Args:
|
||||
llm_response: The response from the LLM
|
||||
|
||||
Returns:
|
||||
str: Extracted Python code or empty string
|
||||
"""
|
||||
match = pattern.search(llm_response)
|
||||
if match:
|
||||
code = match.group(1)
|
||||
return code
|
||||
return ""
|
||||
|
||||
def chat_with_llm(e2b_code_interpreter: Sandbox, user_message: str, dataset_path: str) -> Tuple[Optional[List[Any]], str]:
|
||||
"""
|
||||
Sends the user message to the LLM and executes the generated code.
|
||||
|
||||
Args:
|
||||
e2b_code_interpreter: The E2B sandbox instance
|
||||
user_message: User's query message
|
||||
dataset_path: Path to the uploaded dataset
|
||||
|
||||
Returns:
|
||||
Tuple[Optional[List[Any]], str]: Code execution results and LLM response
|
||||
"""
|
||||
# Update system prompt to include dataset path information
|
||||
system_prompt = f"""You're a Python data scientist and data visualization expert. You are given a dataset at path '{dataset_path}' and also the user's query.
|
||||
You need to analyze the dataset and answer the user's query with a response and you run Python code to solve them.
|
||||
IMPORTANT: Always use the dataset path variable '{dataset_path}' in your code when reading the CSV file."""
|
||||
|
||||
messages = [
|
||||
{"role": "system", "content": system_prompt},
|
||||
{"role": "user", "content": user_message},
|
||||
]
|
||||
|
||||
with st.spinner('Getting response from together AI...'):
|
||||
client = Together(api_key=st.session_state.together_api_key)
|
||||
response = client.chat.completions.create(
|
||||
model=st.session_state.model_name,
|
||||
messages=messages,
|
||||
)
|
||||
|
||||
response_message = response.choices[0].message
|
||||
python_code = match_code_blocks(response_message.content)
|
||||
|
||||
if python_code:
|
||||
code_interpreter_results = code_interpret(e2b_code_interpreter, python_code)
|
||||
return code_interpreter_results, response_message.content
|
||||
else:
|
||||
st.warning(f"Failed to match any Python code in model's response")
|
||||
return None, response_message.content
|
||||
|
||||
def upload_dataset(code_interpreter: Sandbox, uploaded_file) -> str:
|
||||
"""
|
||||
Uploads the dataset to the E2B sandbox.
|
||||
|
||||
Args:
|
||||
code_interpreter: The E2B sandbox instance
|
||||
uploaded_file: Streamlit uploaded file
|
||||
|
||||
Returns:
|
||||
str: Path where file was uploaded
|
||||
"""
|
||||
dataset_path = f"./{uploaded_file.name}"
|
||||
|
||||
try:
|
||||
code_interpreter.files.write(dataset_path, uploaded_file)
|
||||
return dataset_path
|
||||
except Exception as error:
|
||||
st.error(f"Error during file upload: {error}")
|
||||
raise error
|
||||
|
||||
|
||||
def main():
|
||||
"""Main Streamlit application."""
|
||||
st.title("AI Data Visualization Assistant")
|
||||
st.write("Upload your dataset and ask questions about it!")
|
||||
|
||||
# Sidebar for API keys and model name
|
||||
with st.sidebar:
|
||||
st.header("API Keys and Model Configuration")
|
||||
st.session_state.together_api_key = st.text_input("Enter Together API Key", type="password")
|
||||
st.session_state.e2b_api_key = st.text_input("Enter E2B API Key", type="password")
|
||||
st.session_state.model_name = st.text_input("Enter Model Name", value="meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo")
|
||||
|
||||
uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
|
||||
|
||||
if uploaded_file is not None:
|
||||
# Display dataset preview
|
||||
df = pd.read_csv(uploaded_file)
|
||||
st.write("Dataset Preview:")
|
||||
st.dataframe(df.head())
|
||||
|
||||
# Query input
|
||||
query = st.text_area("What would you like to know about your data?",
|
||||
"Can you compare the average cost for two people between different categories?")
|
||||
|
||||
if st.button("Analyze"):
|
||||
if not st.session_state.together_api_key or not st.session_state.e2b_api_key:
|
||||
st.error("Please enter both API keys in the sidebar.")
|
||||
else:
|
||||
with Sandbox(api_key=st.session_state.e2b_api_key) as code_interpreter:
|
||||
# Upload the dataset
|
||||
dataset_path = upload_dataset(code_interpreter, uploaded_file)
|
||||
|
||||
# Pass dataset_path to chat_with_llm
|
||||
code_results, llm_response = chat_with_llm(code_interpreter, query, dataset_path)
|
||||
|
||||
# Display LLM's text response
|
||||
st.write("AI Response:")
|
||||
st.write(llm_response)
|
||||
|
||||
# Display results/visualizations
|
||||
if code_results:
|
||||
for result in code_results:
|
||||
if hasattr(result, 'png') and result.png: # Check if PNG data is available
|
||||
# Decode the base64-encoded PNG data
|
||||
png_data = base64.b64decode(result.png)
|
||||
|
||||
# Convert PNG data to an image and display it
|
||||
image = Image.open(BytesIO(png_data))
|
||||
st.image(image, caption="Generated Visualization", use_container_width=False)
|
||||
elif hasattr(result, 'figure'): # For matplotlib figures
|
||||
fig = result.figure # Extract the matplotlib figure
|
||||
st.pyplot(fig) # Display using st.pyplot
|
||||
elif hasattr(result, 'show'): # For plotly figures
|
||||
st.plotly_chart(result)
|
||||
elif isinstance(result, (pd.DataFrame, pd.Series)):
|
||||
st.dataframe(result)
|
||||
else:
|
||||
st.write(result)
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user