Updated repo structure

starter_ai_agents/ai_data_visualisation_agent/README.md

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+# 📊 AI Data Visualization Agent
+A Streamlit application that acts as your personal data visualization expert, powered by LLMs. Simply upload your dataset and ask questions in natural language - the AI agent will analyze your data, generate appropriate visualizations, and provide insights through a combination of charts, statistics, and explanations.
+
+## Features
+#### Natural Language Data Analysis
+- Ask questions about your data in plain English
+- Get instant visualizations and statistical analysis
+- Receive explanations of findings and insights
+- Interactive follow-up questioning
+
+#### Intelligent Visualization Selection
+- Automatic choice of appropriate chart types
+- Dynamic visualization generation
+- Statistical visualization support
+- Custom plot formatting and styling
+
+#### Multi-Model AI Support
+- Meta-Llama 3.1 405B for complex analysis
+- DeepSeek V3 for detailed insights
+- Qwen 2.5 7B for quick analysis
+- Meta-Llama 3.3 70B for advanced queries
+
+## 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
+    ```

starter_ai_agents/ai_data_visualisation_agent/ai_data_visualisation_agent.py

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+import os
+import json
+import re
+import sys
+import io
+import contextlib
+import warnings
+from typing import Optional, List, Any, Tuple
+from PIL import Image
+import streamlit as st
+import pandas as pd
+import base64
+from io import BytesIO
+from together import Together
+from e2b_code_interpreter import Sandbox
+
+warnings.filterwarnings("ignore", category=UserWarning, module="pydantic")
+
+pattern = re.compile(r"```python\n(.*?)\n```", re.DOTALL)
+
+def code_interpret(e2b_code_interpreter: Sandbox, code: str) -> Optional[List[Any]]:
+    with st.spinner('Executing code in E2B sandbox...'):
+        stdout_capture = io.StringIO()
+        stderr_capture = io.StringIO()
+
+        with contextlib.redirect_stdout(stdout_capture), contextlib.redirect_stderr(stderr_capture):
+            with warnings.catch_warnings():
+                warnings.simplefilter("ignore")
+                exec = e2b_code_interpreter.run_code(code)
+
+        if stderr_capture.getvalue():
+            print("[Code Interpreter Warnings/Errors]", file=sys.stderr)
+            print(stderr_capture.getvalue(), file=sys.stderr)
+
+        if stdout_capture.getvalue():
+            print("[Code Interpreter Output]", file=sys.stdout)
+            print(stdout_capture.getvalue(), file=sys.stdout)
+
+        if exec.error:
+            print(f"[Code Interpreter ERROR] {exec.error}", file=sys.stderr)
+            return None
+        return exec.results
+
+def match_code_blocks(llm_response: str) -> str:
+    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]:
+    # 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 LLM model...'):
+        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:
+    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 Agent")
+    st.write("Upload your dataset and ask questions about it!")
+
+    # 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.sidebar.text_input("Together AI 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 = {
+            "Meta-Llama 3.1 405B": "meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo",
+            "DeepSeek V3": "deepseek-ai/DeepSeek-V3",
+            "Qwen 2.5 7B": "Qwen/Qwen2.5-7B-Instruct-Turbo",
+            "Meta-Llama 3.3 70B": "meta-llama/Llama-3.3-70B-Instruct-Turbo"
+        }
+        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[st.session_state.model_name]
+
+    uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
+    
+    if uploaded_file is not None:
+        # Display dataset with toggle
+        df = pd.read_csv(uploaded_file)
+        st.write("Dataset:")
+        show_full = st.checkbox("Show full dataset")
+        if show_full:
+            st.dataframe(df)
+        else:
+            st.write("Preview (first 5 rows):")
+            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()

starter_ai_agents/ai_data_visualisation_agent/requirements.txt

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+together==1.3.10
+e2b-code-interpreter==1.0.3
+e2b==1.0.5
+Pillow==10.4.0
+streamlit
+pandas
+matplotlib