🤖 Gemini 1.5 Pro Chatbot 💬

This project implements a simple chatbot using Google's Gemini 1.5 Pro model via the google-generativeai library.

✨ Features

• Interactive Chat: Engage in a conversation with the Gemini 1.5 Pro model. 💬
• Safety Settings: Built-in safety mechanisms to filter content related to harassment, hate speech, sexually explicit material, and dangerous content. 🛡️
• Conversation History: Maintains the context of the conversation for more coherent interactions. 🧠

🧰 Requirements

• Python 3.7 or higher 🐍
• google-generativeai library: Install using pip install google-generativeai
• dotenv library: Install using pip install python-dotenv
• Google Account: You'll need a Google account to be able access the Google AI Studio. ☁️
• Gemini API : Ensure that the Gemini API is active. ✅

🚀 Setup

1. Obtain your API Key: 🗝️

   • Go to the Google AI Studio.
   • Get API Key.
   • Create API Key".
   • Copy.
   • Important: Store this API key securely. 🔐

2. Create a .env file: 📝

   • In the project directory, create a file named .env.

   • Add the following line to the file, replacing YOUR_API_KEY with your actual API key:

     API_KEY=YOUR_API_KEY
     

3. Run the Chatbot: ▶️

   • Execute the Python script (e.g., python chatbot.py).
   • Start typing to chat with the chatbot! 💬
   • To exit the program, type exit. 🚪

💻 Code Explanation

• google.generativeai: Imports the necessary library for interacting with the Gemini API.
• dotenv: Loads the API key from the .env file.
• genai.configure(api_key=api_key): Sets up your authentication for the API. 🔑
• model = genai.GenerativeModel(...): Initializes the Gemini 1.5 Pro model with specified parameters:
  • model_name: Specifies the Gemini model to use.
  • generation_config: Controls model behavior:
    • temperature: Higher values (e.g., 1) make the output more creative/random; lower values (e.g., 0.2) make it more deterministic. 🌡️
    • top_p: Nucleus sampling parameter - influences the diversity of generated text.
    • top_k: Top-k sampling parameter - limits the selection of words to the top k most probable words.
    • max_output_tokens: Sets the maximum length of the model's responses.
  • safety_settings: Defines safety thresholds for various content categories. 🛡️
• convo = model.start_chat(...): Initiates a chat session with the model, optionally providing conversation history. 💬
• convo.send_message(user_input): Sends the user's input to the model. ➡️
• print(convo.last.text): Displays the model's response. ⬅️
• conversation_history: Keeps track of the conversation turns. 🔁

👍 Tips

• Experiment with the temperature parameter in generation_config to adjust the chatbot's creativity. 🌡️
• Explore different safety settings to customize the content filtering. 🛡️
• Consider expanding the chatbot's functionality by adding features like:
  • User authentication 👤
  • Topic-specific responses 🎯
  • Integration with other services 🔗

Remember to replace YOUR_API_KEY in the .env file with your actual API key! 🔑