working on adding gemini

.github/workflows/python-tests.yml

@@ -11,7 +11,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [3.8, 3.9, '3.10']
+        python-version: [3.9, '3.10', '3.11', '3.12']
 
     steps:
     - uses: actions/checkout@v2
@@ -32,4 +32,4 @@ jobs:
       run: |
         sudo apt-get update
         sudo apt-get install -y xvfb
-        xvfb-run -a pytest -v tests/ui/test_chat_window.py || echo "UI tests failed but continuing"
+        xvfb-run -a pytest -v tests/ui/test_chat_window.py || echo "UI tests failed but continuing"

README.md

@@ -26,7 +26,7 @@ https://github.com/user-attachments/assets/8aa729ff-c431-4a61-a9ef-d17050a27d02
 
 ### Prerequisites
 
-- Python 3.8 or higher
+- Python 3.9 or higher
 
 ### Setup
 

requirements.txt

@@ -1,6 +1,8 @@
 anthropic==0.34.2
+google-generativeai==0.8.3
 python-decouple==3.8
 virtualenv==20.16.2
+numpy==1.26.4
 openai
 hjson
 pyyaml

sandbox/gemini_sandbox.ipynb

@@ -0,0 +1,144 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "genai.configure(api_key=config('GEMINI_API_KEY'))\n",
+    "\n",
+    "model = genai.GenerativeModel(\"gemini-1.5-pro\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[0;31mSignature:\u001b[0m\n",
+      "\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate_content\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m    \u001b[0mcontents\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'content_types.ContentsType'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m    \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m    \u001b[0mgeneration_config\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'generation_types.GenerationConfigType | None'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m    \u001b[0msafety_settings\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'safety_types.SafetySettingOptions | None'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m    \u001b[0mstream\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'bool'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m    \u001b[0mtools\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'content_types.FunctionLibraryType | None'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m    \u001b[0mtool_config\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'content_types.ToolConfigType | None'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m    \u001b[0mrequest_options\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'helper_types.RequestOptionsType | None'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+      "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;34m'generation_types.GenerateContentResponse'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mDocstring:\u001b[0m\n",
+      "A multipurpose function to generate responses from the model.\n",
+      "\n",
+      "This `GenerativeModel.generate_content` method can handle multimodal input, and multi-turn\n",
+      "conversations.\n",
+      "\n",
+      ">>> model = genai.GenerativeModel('models/gemini-pro')\n",
+      ">>> response = model.generate_content('Tell me a story about a magic backpack')\n",
+      ">>> response.text\n",
+      "\n",
+      "### Streaming\n",
+      "\n",
+      "This method supports streaming with the `stream=True`. The result has the same type as the non streaming case,\n",
+      "but you can iterate over the response chunks as they become available:\n",
+      "\n",
+      ">>> response = model.generate_content('Tell me a story about a magic backpack', stream=True)\n",
+      ">>> for chunk in response:\n",
+      "...   print(chunk.text)\n",
+      "\n",
+      "### Multi-turn\n",
+      "\n",
+      "This method supports multi-turn chats but is **stateless**: the entire conversation history needs to be sent with each\n",
+      "request. This takes some manual management but gives you complete control:\n",
+      "\n",
+      ">>> messages = [{'role':'user', 'parts': ['hello']}]\n",
+      ">>> response = model.generate_content(messages) # \"Hello, how can I help\"\n",
+      ">>> messages.append(response.candidates[0].content)\n",
+      ">>> messages.append({'role':'user', 'parts': ['How does quantum physics work?']})\n",
+      ">>> response = model.generate_content(messages)\n",
+      "\n",
+      "For a simpler multi-turn interface see `GenerativeModel.start_chat`.\n",
+      "\n",
+      "### Input type flexibility\n",
+      "\n",
+      "While the underlying API strictly expects a `list[protos.Content]` objects, this method\n",
+      "will convert the user input into the correct type. The hierarchy of types that can be\n",
+      "converted is below. Any of these objects can be passed as an equivalent `dict`.\n",
+      "\n",
+      "* `Iterable[protos.Content]`\n",
+      "* `protos.Content`\n",
+      "* `Iterable[protos.Part]`\n",
+      "* `protos.Part`\n",
+      "* `str`, `Image`, or `protos.Blob`\n",
+      "\n",
+      "In an `Iterable[protos.Content]` each `content` is a separate message.\n",
+      "But note that an `Iterable[protos.Part]` is taken as the parts of a single message.\n",
+      "\n",
+      "Arguments:\n",
+      "    contents: The contents serving as the model's prompt.\n",
+      "    generation_config: Overrides for the model's generation config.\n",
+      "    safety_settings: Overrides for the model's safety settings.\n",
+      "    stream: If True, yield response chunks as they are generated.\n",
+      "    tools: `protos.Tools` more info coming soon.\n",
+      "    request_options: Options for the request.\n",
+      "\u001b[0;31mFile:\u001b[0m      ~/opt/anaconda3/envs/codeaide/lib/python3.11/site-packages/google/generativeai/generative_models.py\n",
+      "\u001b[0;31mType:\u001b[0m      method"
+     ]
+    }
+   ],
+   "source": [
+    "# Define the system prompt (context)\n",
+    "system_prompt = \"\"\"You are an AI assistant specialized in explaining complex topics in simple terms.\n",
+    "Your responses should be clear, concise, and easy to understand for a general audience.\"\"\"\n",
+    "\n",
+    "# Define the user's question\n",
+    "user_question = \"Can you explain quantum computing in simple terms?\"\n",
+    "\n",
+    "# Set up the chat\n",
+    "chat = model.start_chat(context=system_prompt)\n",
+    "\n",
+    "# Generate a response\n",
+    "response = chat.send_message(user_question)\n",
+    "\n",
+    "# Print the response\n",
+    "print(response.text)\n",
+    "\n",
+    "# You can continue the conversation\n",
+    "follow_up = chat.send_message(\"How does that compare to classical computing?\")\n",
+    "print(follow_up.text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "codeaide",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

sandbox/prototype_gemini.py

@@ -0,0 +1,39 @@
+"""
+This is a little sandbox script to test out the Gemini API.
+"""
+
+import argparse
+from decouple import config
+import google.generativeai as genai
+
+genai.configure(api_key=config("GEMINI_API_KEY"))
+
+model = genai.GenerativeModel("gemini-1.5-flash")
+
+
+def generate_a_story():
+    response = model.generate_content("Write a story about a magic backpack.")
+    print(response.text)
+
+
+def request_code():
+    response = model.generate_content(
+        "Write a Python function to calculate the Fibonacci sequence."
+    )
+    print(response.text)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Gemini API prototype script")
+    parser.add_argument(
+        "action",
+        choices=["story", "code"],
+        help="Action to perform: generate a story or request code",
+    )
+
+    args = parser.parse_args()
+
+    if args.action == "story":
+        generate_a_story()
+    elif args.action == "code":
+        request_code()