refactor: Update Dockerfile and README, remove unused files, and enhance agent configuration

Dockerfile

@@ -1,40 +1,53 @@
+# Start from a minimal Python image. Slim reduces size while keeping Python.
 FROM python:3.12-slim
 
-# Create a non-root user
+# Make Python output unbuffered (better real-time logs)
+ENV PYTHONUNBUFFERED=1
+
+# Create a non-root user to run the app (security best-practice).
 RUN useradd --create-home --shell /bin/bash app
 
-# Set working directory
+# Set working directory for subsequent commands and the container runtime.
 WORKDIR /app
 
-# Install system dependencies
-RUN apt-get update && apt-get install -y \
+# Install system dependencies needed to build Python packages and tooling.
+# --no-install-recommends keeps the image smaller by avoiding extra packages.
+RUN apt-get update && apt-get install -y --no-install-recommends \
     gcc \
     g++ \
     curl \
     && rm -rf /var/lib/apt/lists/*
 
-# Install uv
-RUN pip install uv
+# Upgrade pip and related build tools, and install uv (project's runtime manager).
+# --no-cache-dir avoids leaving pip cache in the image.
+RUN python -m pip install --upgrade pip setuptools wheel && \
+    pip install --no-cache-dir uv
 
-# Copy project files and change ownership
+# Copy project files into the image.
 COPY . .
+
+# Ensure the non-root user owns the app directory so it can run/install files there.
 RUN chown -R app:app /app
 
-# Switch to non-root user
+# Switch to the non-root user for better security for subsequent steps.
 USER app
 
-# Set uv cache directory to a writable location
+# Set uv cache dir to somewhere writable by the app user. This avoids permission errors
+# when uv manages its own cache during `uv sync` or runtime.
 ENV UV_CACHE_DIR=/app/.uv-cache
 
-# Install dependencies using uv
+# Install application dependencies using uv. Running this as the non-root `app` user
+# keeps the image consistent with runtime permissions. `--frozen` ensures deterministic installs.
 RUN uv sync --frozen
 
-# Expose the port that Gradio will run on
+# Expose the port Gradio uses (default in the project). This is informational and
+# useful for container orchestration and documentation.
 EXPOSE 7860
 
-# Set environment variables for Gradio
+# Set Gradio environment variables so the app binds to all interfaces in containers.
 ENV GRADIO_SERVER_NAME="0.0.0.0"
 ENV GRADIO_SERVER_PORT=7860
 
-# Run the Gradio app
+# Default command: run the Gradio app via uv. Using uv run keeps the environment consistent
+# with development workflows that rely on uv.
 CMD ["uv", "run", "python", "src/sales_assistant/ui_dashboard/gradio_app.py"]

README.md

@@ -10,15 +10,16 @@ license: mit
 
 # StreamNet Sales Assistant 🤖
 
-A sophisticated AI-powered sales assistant that helps customers explore products, get pricing information, and generate professional quotes. Built with LangGraph, LangChain, and Gradio.
+AI-powered sales assistant that helps the sales department with e.g.: get pricing information, and generate professional quotes.
 
 ## Features
 
 - 🔍 **Product Search**: Advanced search across our product database
 - 📊 **Data Analysis**: Statistical insights and product comparisons  
-- 💰 **Quote Generation**: Professional quote creation with current exchange rates
 - 🌍 **Exchange Rates**: Real-time currency conversion
-- ❓ **Natural Language**: Chat naturally about products and services
+- 🌐 **Web Search with Tavaily**: Instantly search the web for up-to-date product information
+- 💰 **Quote Generation**: Professional quote creation with current exchange rates
+
 
 ## How to Use
 
@@ -35,7 +36,6 @@ Simply start a conversation with the assistant! You can:
 - **Language Model**: GPT-5-mini
 - **Database**: MySQL with SQLAlchemy
 - **UI**: Gradio
-- **Validation**: Pydantic
 
 ## Repository
 

README_HF.md

@@ -1,42 +0,0 @@
----
-title: StreamNet Sales Assistant
-emoji: 🤖
-colorFrom: blue
-colorTo: purple
-sdk: docker
-pinned: false
-license: mit
----
-
-# StreamNet Sales Assistant 🤖
-
-A sophisticated AI-powered sales assistant that helps customers explore products, get pricing information, and generate professional quotes. Built with LangGraph, LangChain, and Gradio.
-
-## Features
-
-- 🔍 **Product Search**: Advanced search across our product database
-- 📊 **Data Analysis**: Statistical insights and product comparisons  
-- 💰 **Quote Generation**: Professional quote creation with current exchange rates
-- 🌍 **Exchange Rates**: Real-time currency conversion
-- ❓ **Natural Language**: Chat naturally about products and services
-
-## How to Use
-
-Simply start a conversation with the assistant! You can:
-
-- Ask about specific products: "Can you give me a price for a Saber 4k+?"
-- Request quotes: "I need a 55 inch Samsung TV, what are my options?"
-- Get exchange rates: "What are the current exchange rates?"
-- Explore categories: "What categories of Samsung products do you have?"
-
-## Technology Stack
-
-- **AI Framework**: LangGraph + LangChain
-- **Language Model**: GPT-4
-- **Database**: MySQL with SQLAlchemy
-- **UI**: Gradio
-- **Validation**: Pydantic
-
-## Repository
-
-Full source code is available at: [https://github.com/krinya/sales_assistant_with_quote](https://github.com/krinya/sales_assistant_with_quote)

docs/HUGGINGFACE_DEPLOYMENT.md

@@ -1,148 +0,0 @@
-# Hugging Face Deployment Guide
-
-This guide explains how to deploy your sales assistant application on Hugging Face Spaces with secure SSH key management.
-
-## Setting Up Secrets in Hugging Face
-
-1. **Go to your Hugging Face Space settings**
-   - Navigate to your space on Hugging Face
-   - Click on "Settings" tab
-   - Go to "Repository secrets"
-
-2. **Add the following secrets:**
-
-### Required Database Secrets
-```
-SSH_HOSTNAME=your.ssh.hostname.com
-SSH_PORT=22
-SSH_USERNAME=your-ssh-username
-SSH_PEM_CONTENT=-----BEGIN RSA PRIVATE KEY-----\nMIIEpAIBAAKCAQEA...\n-----END RSA PRIVATE KEY-----
-MYSQL_HOST=your.mysql.hostname.com
-MYSQL_PORT=3306
-MYSQL_USER=your-mysql-user
-MYSQL_PASSWORD=your-mysql-password
-MYSQL_DB=your-database-name
-```
-
-### Required API Keys
-```
-OPENAI_API_KEY=your-openai-api-key
-HUGGINGFACE_API_KEY=your-hf-api-key
-TAVILY_API_KEY=your-tavily-api-key
-BREVO_API_KEY=your-brevo-api-key
-COMPANY_EMAIL=your-company@email.com
-```
-
-### Optional Configuration
-```
-MODEL_NAME=gpt-5-mini
-MODEL_PROVIDER=openai
-LANGSMITH_API_KEY=your-langsmith-key
-LANGSMITH_TRACING_V2=true
-LANGSMITH_ENDPOINT=https://api.smith.langchain.com
-LANGSMITH_PROJECT=your-project-name
-```
-
-## SSH PEM Content Format
-
-When adding your SSH private key as `SSH_PEM_CONTENT`, use this format:
-
-```
------BEGIN RSA PRIVATE KEY-----\nMIIEpAIBAAKCAQEA0dtZotnwrz2jVSz2X3cZ...\n-----END RSA PRIVATE KEY-----
-```
-
-**Important Notes:**
-- Replace actual line breaks with `\n` 
-- Don't include quotes around the content
-- Keep the BEGIN/END lines intact
-- Include the `\n` characters exactly as shown
-
-## How It Works
-
-Our implementation now uses **paramiko directly** instead of temporary files:
-
-### ✅ Advantages of Current Implementation
-- **No temporary files**: PEM content is parsed directly in memory
-- **Hugging Face friendly**: Works seamlessly with HF secrets
-- **More secure**: No files written to disk
-- **Cloud native**: Perfect for containerized environments
-- **Fallback support**: Still works with file-based keys if needed
-
-### 🔄 Process Flow
-1. **Load environment variables** from HF secrets
-2. **Parse PEM content** directly using paramiko
-3. **Create SSH tunnel** using the parsed key object
-4. **Connect to database** through the secure tunnel
-5. **Clean shutdown** with no temporary files to clean up
-
-## Testing Locally
-
-To test your HF deployment locally:
-
-1. **Copy your secrets** to a local `.env` file (temporarily)
-2. **Run the test**: `uv run python test_files/db_test.py`
-3. **Verify output**: Look for "✅ Successfully created SSH key from PEM content"
-4. **Delete local .env**: Don't commit secrets to git!
-
-## Deployment Files for HF
-
-Make sure your Hugging Face space includes:
-
-- `app.py` (or your main application file)
-- `requirements.txt` (or `pyproject.toml`)
-- `README.md`
-
-Example `app.py` for Gradio:
-```python
-import gradio as gr
-from src.sales_assistant.ui_dashboard.gradio_app import create_app
-
-if __name__ == "__main__":
-    app = create_app()
-    app.launch()
-```
-
-## Troubleshooting
-
-### Common Issues:
-
-1. **"No SSH key available"**
-   - Check that `SSH_PEM_CONTENT` is properly set in HF secrets
-   - Verify the PEM format includes `\n` characters
-
-2. **"Could not parse PEM content"**
-   - Ensure the PEM content is properly formatted
-   - Check that the private key is valid
-
-3. **Connection timeout**
-   - Verify SSH hostname and port
-   - Check that the SSH server allows connections from HF infrastructure
-
-4. **MySQL connection failed**
-   - Verify MySQL credentials
-   - Check that MySQL server allows connections from your SSH server
-
-### Debug Mode
-
-Add this to your app for debugging:
-```python
-import os
-print(f"SSH_PEM_CONTENT loaded: {bool(os.getenv('SSH_PEM_CONTENT'))}")
-print(f"SSH_HOSTNAME: {os.getenv('SSH_HOSTNAME')}")
-```
-
-## Security Best Practices
-
-1. **Never commit secrets** to your git repository
-2. **Use HF secrets** for all sensitive information
-3. **Rotate SSH keys** periodically
-4. **Monitor access logs** on your SSH and database servers
-5. **Use strong passwords** for database accounts
-6. **Consider IP whitelisting** if possible
-
-## Performance Tips
-
-1. **Connection pooling**: The implementation reuses connections efficiently
-2. **Query optimization**: Use indexed columns for faster queries
-3. **Caching**: Consider caching frequently accessed data
-4. **Monitoring**: Set up monitoring for your database and SSH server

src/sales_assistant/agent_main/agent_runner.py

@@ -16,7 +16,7 @@ class ConversationConfig(BaseModel):
     """Enhanced configuration with validation for GPT-5-mini conversations."""
     session_id: str = Field(default_factory=lambda: str(uuid.uuid4()))
     user_id: str = Field(default="default_user")
-    langsmith_project: str = Field(default_factory=lambda: os.getenv("LANGSMITH_PROJECT", "sales-assistant-gpt5"))
+    langsmith_project: str = Field(default_factory=lambda: os.getenv("LANGSMITH_PROJECT", "sales-assistant"))
     enable_langsmith: bool = Field(default=True)
     model_name: str = Field(default_factory=lambda: os.getenv("MODEL_NAME", "gpt-5-mini"))
     model_provider: str = Field(default_factory=lambda: os.getenv("MODEL_PROVIDER", "openai"))
@@ -235,7 +235,6 @@ def clear_conversation_history(compiled_graph, thread_id: str) -> None:
     try:
         config = {"configurable": {"thread_id": thread_id}}
         # Note: LangGraph's MemorySaver doesn't have a direct clear method
-        # You would need to implement this based on your specific checkpointer
         print(f"Note: To clear history, restart with a new thread_id")
     except Exception as e:
         print(f"Error clearing conversation history: {e}")

src/sales_assistant/agent_tools/get_exchange_rates.py

@@ -3,6 +3,7 @@ from langchain_core.tools import tool
 from .tool_schemas import CurrencyConversionInput, CurrencyConversionOutput, ErrorOutput
 
 # Fixed exchange rates relative to HUF (kept simple and deterministic)
+## This will be replaced with a real API call in a production scenario now for testing it is hardcoded to see tool functionality
 _EXCHANGE_RATES = {
     "HUF": 1.0,
     "EUR": 400.0,

src/sales_assistant/ui_dashboard/gradio_app.py

@@ -9,10 +9,8 @@ import gradio as gr
 from typing import List, Tuple, Dict
 from datetime import datetime
 import glob
-import threading
-import time
 import logging
-import io
+
 
 # Add the src directory to the path
 sys.path.insert(0, os.path.join(os.path.dirname(__file__), '../..'))

src/sales_assistant/ui_dashboard/streamlit_app.py

@@ -1,273 +0,0 @@
-"""
-Streamlit Chat UI for Sales Assistant
-
-This module provides a simple Streamlit-based chat interface for the sales assistant.
-"""
-import os
-import sys
-import streamlit as st
-from typing import Dict, Any, List
-
-# Add the src directory to the path
-sys.path.insert(0, os.path.join(os.path.dirname(__file__), '../..'))
-
-from sales_assistant.main import (
-    check_environment_setup,
-    create_custom_config
-)
-from sales_assistant.agent_main.agent_runner import (
-    create_agent_runner,
-    run_conversation_turn
-)
-
-
-# Page configuration
-st.set_page_config(
-    page_title="Sales Assistant with Quote Generation",
-    page_icon="🤖",
-    layout="wide",
-    initial_sidebar_state="expanded"
-)
-
-
-def initialize_session_state():
-    """Initialize Streamlit session state variables."""
-    if "messages" not in st.session_state:
-        st.session_state.messages = []
-    
-    if "compiled_graph" not in st.session_state:
-        st.session_state.compiled_graph = None
-    
-    if "checkpointer" not in st.session_state:
-        st.session_state.checkpointer = None
-    
-    if "langsmith_client" not in st.session_state:
-        st.session_state.langsmith_client = None
-    
-    if "thread_id" not in st.session_state:
-        st.session_state.thread_id = None
-    
-    if "agent_initialized" not in st.session_state:
-        st.session_state.agent_initialized = False
-
-
-def initialize_agent():
-    """Initialize the sales assistant agent."""
-    if st.session_state.agent_initialized:
-        return True
-    
-    try:
-        with st.spinner("Initializing Sales Assistant..."):
-            # Check environment setup
-            if not check_environment_setup():
-                st.error("❌ Environment setup failed. Please check your configuration.")
-                return False
-            
-            # Create configuration
-            config = create_custom_config(
-                session_id="streamlit_session",
-                user_id="streamlit_user",
-                enable_langsmith=True
-            )
-            
-            # Create agent runner
-            compiled_graph, checkpointer, langsmith_client, thread_id = create_agent_runner(config)
-            
-            # Store in session state
-            st.session_state.compiled_graph = compiled_graph
-            st.session_state.checkpointer = checkpointer
-            st.session_state.langsmith_client = langsmith_client
-            st.session_state.thread_id = thread_id
-            st.session_state.agent_initialized = True
-            
-            st.success("✅ Sales Assistant initialized successfully!")
-            return True
-            
-    except Exception as e:
-        st.error(f"❌ Failed to initialize sales assistant: {e}")
-        return False
-
-
-def display_sidebar():
-    """Display the sidebar with information and controls."""
-    with st.sidebar:
-        st.markdown("# 🤖 Sales Assistant")
-        st.markdown("---")
-        
-        # Status indicator
-        if st.session_state.agent_initialized:
-            st.success("🟢 Agent Online")
-        else:
-            st.error("🔴 Agent Offline")
-        
-        st.markdown("---")
-        
-        # Information section
-        st.markdown("### 🔧 Capabilities")
-        st.markdown("""
-        - 🔍 **Product Search**: Find products by criteria
-        - 📊 **Data Analysis**: Statistical insights
-        - 💰 **Quote Generation**: Professional quotes
-        - 🌍 **Exchange Rates**: Current currency rates
-        - 📈 **Database Insights**: Explore product data
-        """)
-        
-        st.markdown("---")
-        
-        # Example queries
-        st.markdown("### 💡 Example Queries")
-        example_queries = [
-            "Show me product information",
-            "Generate a quote for equipment",
-            "What are current exchange rates?",
-            "Find products under $1000",
-            "Show product statistics"
-        ]
-        
-        for query in example_queries:
-            if st.button(query, key=f"example_{hash(query)}", use_container_width=True):
-                st.session_state.messages.append({"role": "user", "content": query})
-                st.rerun()
-        
-        st.markdown("---")
-        
-        # Clear chat button
-        if st.button("🗑️ Clear Chat", use_container_width=True):
-            st.session_state.messages = []
-            st.rerun()
-        
-        # Restart agent button
-        if st.button("🔄 Restart Agent", use_container_width=True):
-            st.session_state.agent_initialized = False
-            st.session_state.compiled_graph = None
-            st.session_state.checkpointer = None
-            st.session_state.langsmith_client = None
-            st.session_state.thread_id = None
-            st.rerun()
-
-
-def display_chat_messages():
-    """Display the chat messages."""
-    for message in st.session_state.messages:
-        with st.chat_message(message["role"]):
-            st.markdown(message["content"])
-
-
-def process_user_input(user_input: str) -> str:
-    """
-    Process user input and get response from the sales assistant.
-    
-    Args:
-        user_input: The user's message
-        
-    Returns:
-        The assistant's response
-    """
-    if not st.session_state.compiled_graph:
-        return "❌ Sales Assistant is not properly initialized. Please restart the agent."
-    
-    try:
-        # Run conversation turn
-        response = run_conversation_turn(
-            compiled_graph=st.session_state.compiled_graph,
-            thread_id=st.session_state.thread_id,
-            user_input=user_input,
-            langsmith_client=st.session_state.langsmith_client
-        )
-        return response
-        
-    except Exception as e:
-        return f"❌ Error processing your request: {str(e)}"
-
-
-def main():
-    """Main Streamlit application."""
-    # Initialize session state
-    initialize_session_state()
-    
-    # Main title
-    st.title("🤖 Sales Assistant with Quote Generation")
-    st.markdown("---")
-    
-    # Initialize agent if not done
-    if not st.session_state.agent_initialized:
-        if not initialize_agent():
-            st.stop()
-    
-    # Display sidebar
-    display_sidebar()
-    
-    # Main chat area
-    col1, col2 = st.columns([3, 1])
-    
-    with col1:
-        # Introduction message
-        if not st.session_state.messages:
-            st.markdown("""
-            ### Welcome to the Sales Assistant! 👋
-            
-            I'm here to help you with:
-            - 🔍 **Product Information**: Search and explore our product database
-            - 📊 **Data Analysis**: Get insights and statistics about products
-            - 💰 **Quote Generation**: Create professional quotes with current pricing
-            - 🌍 **Exchange Rates**: Get up-to-date currency information
-            - ❓ **General Questions**: Ask me anything about our products and services
-            
-            **To get started**, type your question in the chat box below or click one of the example queries in the sidebar.
-            """)
-            st.markdown("---")
-        
-        # Display chat messages
-        display_chat_messages()
-        
-        # Chat input
-        if prompt := st.chat_input("Ask me about products, request a quote, or explore our database..."):
-            # Add user message to chat history
-            st.session_state.messages.append({"role": "user", "content": prompt})
-            
-            # Display user message
-            with st.chat_message("user"):
-                st.markdown(prompt)
-            
-            # Get and display assistant response
-            with st.chat_message("assistant"):
-                with st.spinner("Thinking..."):
-                    response = process_user_input(prompt)
-                st.markdown(response)
-            
-            # Add assistant response to chat history
-            st.session_state.messages.append({"role": "assistant", "content": response})
-    
-    with col2:
-        # Stats section
-        st.markdown("### 📊 Chat Statistics")
-        total_messages = len(st.session_state.messages)
-        user_messages = len([m for m in st.session_state.messages if m["role"] == "user"])
-        assistant_messages = len([m for m in st.session_state.messages if m["role"] == "assistant"])
-        
-        st.metric("Total Messages", total_messages)
-        st.metric("Your Messages", user_messages)
-        st.metric("Assistant Responses", assistant_messages)
-        
-        # Recent activity
-        if st.session_state.messages:
-            st.markdown("### 🕒 Recent Activity")
-            recent_messages = st.session_state.messages[-3:]
-            for msg in recent_messages:
-                role_icon = "👤" if msg["role"] == "user" else "🤖"
-                st.text(f"{role_icon} {msg['content'][:50]}...")
-    
-    # Footer
-    st.markdown("---")
-    st.markdown(
-        """
-        <div style='text-align: center; color: #666;'>
-        Sales Assistant powered by AI | Built with Streamlit
-        </div>
-        """,
-        unsafe_allow_html=True
-    )
-
-
-if __name__ == "__main__":
-    main()