This commit introduces a new community contribution: a ticket price agent that utilizes Google Search to find flight information.

week2/community-contributions/TicketPriceWithGoogleSearch/README.md

@@ -0,0 +1,77 @@
+# Flight Ticket Price Agent
+
+This project implements a conversational AI agent that can find and analyze flight ticket prices. Users can ask for flight prices between two cities in a natural language chat interface, and the agent will use a combination of web search and language models to provide a summary of the costs.
+
+## Features
+
+- **Conversational Interface:** A user-friendly chat interface built with Gradio.
+- **Multi-Model Support:** Can be configured to use different Large Language Models (LLMs) for analysis, including:
+  - OpenAI (e.g., GPT-4o-mini)
+  - Google Gemini (e.g., Gemini 2.5 Flash)
+  - Ollama (e.g., Llama 3.1)
+- **Tool-Based Architecture:** The agent uses a `get_ticket_price` tool to understand when the user is asking for flight information.
+- **Web Scraping & Analysis:**
+  - Uses Google Custom Search to find relevant web pages with flight information.
+  - Scrapes the content of the search results.
+  - Leverages an LLM to analyze the scraped text and extract the lowest, highest, and average prices for one-way and round-trip flights.
+- **Caching:** Caches search results to provide faster responses for repeated queries.
+- **Currency Conversion:** Includes a basic currency conversion table to standardize prices to INR.
+
+## Requirements
+
+The project is built with Python and requires the following libraries:
+
+- `python-dotenv`
+- `openai`
+- `google-generativeai`
+- `ollama`
+- `gradio`
+- `requests`
+- `beautifulsoup4`
+- `google-api-python-client`
+- `ipython`
+
+You can install these dependencies using pip:
+```bash
+pip install python-dotenv openai google-generativeai ollama gradio requests beautifulsoup4 google-api-python-client ipython
+```
+
+## Setup
+
+1.  **Clone the repository (optional):**
+    ```bash
+    git clone <repository_url>
+    cd <repository_directory>/ticket_price_agent
+    ```
+
+2.  **Create a `.env` file:**
+    Create a file named `.env` in the `ticket_price_agent` directory and add your API keys:
+    ```env
+    OPENAI_API_KEY="your_openai_api_key"
+    GEMINI_API_KEY="your_gemini_api_key"
+    GOOGLE_SEARCH_KEY="your_google_search_api_key"
+    GOOGLE_CSE_ID="your_google_custom_search_engine_id"
+    ```
+    *   `GOOGLE_SEARCH_KEY` and `GOOGLE_CSE_ID` are required for the Google Custom Search API.
+
+3.  **Install Dependencies:**
+    Run the `pip install` command mentioned in the "Requirements" section.
+
+## Usage
+
+1.  **Open the Notebook:** Launch Jupyter Notebook or JupyterLab and open `ticket_price_agent.ipynb`.
+2.  **Run the Cells:** Execute the cells in the notebook sequentially.
+3.  **Interact with the Agent:** The final cell will start a Gradio chat interface. You can select the model you want to use (OpenAI or Gemini) from the dropdown menu.
+4.  **Ask for Prices:** Start a conversation by asking for flight prices, for example:
+    - "How much is a ticket from Delhi to Mumbai?"
+    - "What's the flight cost to Kathmandu from Delhi?"
+
+## How It Works
+
+1.  **User Input:** The user enters a message in the Gradio chat interface.
+2.  **Model Selection:** The selected LLM (OpenAI or Gemini) processes the input.
+3.  **Tool Call:** The model's function-calling/tool-using capability identifies that the user is asking for a price and calls the `get_ticket_price` function with the extracted departure and destination cities.
+4.  **Google Search:** The `get_ticket_price` function constructs a search query and uses the Google Custom Search API to find relevant links.
+5.  **Web Scraping:** The agent scrapes the content from the top search result pages.
+6.  **Price Analysis:** The scraped content is passed to another LLM instance with a specific prompt to analyze the text and extract price information (lowest, highest, average).
+7.  **Response Generation:** The final price summary is returned to the main chat model, which then formulates a user-friendly response.