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ai_agent_tutorials/ai_data_visualisation_agent/README.md

@@ -0,0 +1,39 @@
+# AI Data Visualization Agent
+
+This Assistant is designed to help anyone create and visualize data using natural language commands, and it is built using Together AI and E2B Code Interpreter. User gets to upload a dataset and ask questions to the LLM to get the data visualized. This demo can be considered as a demo for the E2B Code Interpreter and Together AI, for anyone who's getting started with these libraries!
+
+## Features
+
+- 🎨 Natural language-driven visualization creation
+- 📊 Support for multiple chart types (line, bar, scatter, pie, bubble)
+- 📈 Automatic data preprocessing and cleaning
+- 🎯 Available Models:
+  - Meta-Llama 3.1 405B
+  - DeepSeek V3
+  - Qwen 2.5 7B
+  - Meta-Llama 3.3 70B
+- 📱 The Code runs in the E2B Sandbox environment, so it is secure and fast
+- Streamlit for clear and interactive user interface
+
+## How to Run
+
+Follow the steps below to set up and run the application:
+Before anything else, Please get a free Together AI API Key here: https://api.together.ai/signin
+Get a free E2B API Key here: https://e2b.dev/ ; https://e2b.dev/docs/legacy/getting-started/api-key
+
+1. **Clone the Repository**:
+   ```bash
+   git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
+   cd ai_agent_tutorials/ai_data_visualisation_agent
+   ```
+
+2. **Install the dependencies**
+    ```bash
+    pip install -r requirements.txt
+    ```
+
+3. **Run the Streamlit app**
+    ```bash
+    streamlit run ai_data_visualisation_agent.py
+    ```
+

ai_agent_tutorials/ai_data_visualisation_agent/ai_data_visualisation_agent.py

@@ -6,7 +6,6 @@ import io
 import contextlib
 import warnings
 from typing import Optional, List, Any, Tuple
-from dotenv import load_dotenv
 from PIL import Image
 import streamlit as st
 import pandas as pd
@@ -15,40 +14,24 @@ from io import BytesIO
 from together import Together
 from e2b_code_interpreter import Sandbox
 
-# Suppress Pydantic warnings globally
 warnings.filterwarnings("ignore", category=UserWarning, module="pydantic")
 
-# 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...'):
-        # Capture stdout and stderr
         stdout_capture = io.StringIO()
         stderr_capture = io.StringIO()
 
         with contextlib.redirect_stdout(stdout_capture), contextlib.redirect_stderr(stderr_capture):
-            # Suppress warnings during code execution
             with warnings.catch_warnings():
                 warnings.simplefilter("ignore")
                 exec = e2b_code_interpreter.run_code(code)
 
-        # Log stderr (warnings and errors) to the terminal
         if stderr_capture.getvalue():
             print("[Code Interpreter Warnings/Errors]", file=sys.stderr)
             print(stderr_capture.getvalue(), file=sys.stderr)
 
-        # Log stdout (normal output) to the terminal
         if stdout_capture.getvalue():
             print("[Code Interpreter Output]", file=sys.stdout)
             print(stdout_capture.getvalue(), file=sys.stdout)
@@ -59,15 +42,6 @@ def code_interpret(e2b_code_interpreter: Sandbox, code: str) -> Optional[List[An
         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)
@@ -75,17 +49,6 @@ def match_code_blocks(llm_response: str) -> str:
     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.
@@ -114,16 +77,6 @@ IMPORTANT: Always use the dataset path variable '{dataset_path}' in your code wh
             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:
@@ -139,11 +92,22 @@ def main():
     st.title("AI Data Visualization Agent")
     st.write("Upload your dataset and ask questions about it!")
 
-    # Sidebar for API keys and model selection
+    # Initialize session state variables
+    if 'together_api_key' not in st.session_state:
+        st.session_state.together_api_key = ''
+    if 'e2b_api_key' not in st.session_state:
+        st.session_state.e2b_api_key = ''
+    if 'model_name' not in st.session_state:
+        st.session_state.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.together_api_key = st.sidebar.text_input("Together AI API Key", type="password")
-        st.session_state.e2b_api_key = st.text_input("Enter E2B API Key", type="password")
+        st.sidebar.info("💡 Everyone gets a free $1 credit by Together AI - AI Acceleration Cloud platform")
+        st.sidebar.markdown("[Get Together AI API Key](https://api.together.ai/signin)")
+        
+        st.session_state.e2b_api_key = st.sidebar.text_input("Enter E2B API Key", type="password")
+        st.sidebar.markdown("[Get E2B API Key](https://e2b.dev/docs/legacy/getting-started/api-key)")
         
         # Add model selection dropdown
         model_options = {
@@ -152,12 +116,12 @@ def main():
             "Qwen 2.5 7B": "Qwen/Qwen2.5-7B-Instruct-Turbo",
             "Meta-Llama 3.3 70B": "meta-llama/Llama-3.3-70B-Instruct-Turbo"
         }
-        selected_model = st.selectbox(
+        st.session_state.model_name = st.selectbox(
             "Select Model",
             options=list(model_options.keys()),
             index=0  # Default to first option
         )
-        st.session_state.model_name = model_options[selected_model]
+        st.session_state.model_name = model_options[st.session_state.model_name]
 
     uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
     

ai_agent_tutorials/ai_data_visualisation_agent/requirements.txt

@@ -1,9 +1,7 @@
-together>=0.2.8
+together==1.3.10
-e2b>=0.12.0
+e2b-code-interpreter==1.0.3
-python-dotenv
+e2b==1.0.5
-Pillow
+Pillow==10.4.0
 streamlit
 pandas
 matplotlib
-plotly
-seaborn>=0.12.0