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@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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GenAI.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3a800e93-1e4a-40de-a211-4244e8d1a161",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip install -qU langchain-google-genai"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "764db45e-0ed0-480b-b338-2d7747d7746d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_google_genai import ChatGoogleGenerativeAI\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain.chains import LLMChain\n",
+    "import os\n",
+    "#from google.colab import userdata "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "d5b8529b-7206-4f51-804c-0a49b3242310",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ[\"MY_SECRET_KEY\"] = \"AIzaSyDRj3wAgqOCjc_D45W_u-G3y9dk5YDgxEo\"\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f484e386-e7a8-44a9-9ad5-0ec977a8b618",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#pip install fastapi uvicorn google-generativeai"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "bcb32fd9-805c-409c-aa27-2745867daf41",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from fastapi import FastAPI\n",
+    "import google.generativeai as genai\n",
+    "from fastapi.middleware.cors import CORSMiddleware\n",
+    "\n",
+    "# Configure Google Gemini API\n",
+    "from langchain_google_genai import ChatGoogleGenerativeAI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "34b28dd0-5675-48ac-b07a-5ee27b5a04dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "google_api_key = os.environ[\"MY_SECRET_KEY\"]\n",
+    "\n",
+    "# Check if the API key was found\n",
+    "if google_api_key:\n",
+    "    # Set the environment variable if the API key was found\n",
+    "    os.environ[\"GOOGLE_API_KEY\"] = google_api_key\n",
+    "\n",
+    "    llm = ChatGoogleGenerativeAI(\n",
+    "        model=\"gemini-pro\",  # Specify the model name\n",
+    "        google_api_key=os.environ[\"GOOGLE_API_KEY\"]\n",
+    "    )\n",
+    "else:\n",
+    "    print(\"Error: GOOGLE_API_KEY not found in Colab secrets. Please store your API key.\")\n",
+    "\n",
+    "\n",
+    "\n",
+    "genai.configure(api_key=google_api_key)\n",
+    "model = genai.GenerativeModel(\"gemini-pro\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c452755f-fad6-455f-9822-7c66b36d724f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize FastAPI\n",
+    "app = FastAPI()\n",
+    "\n",
+    "# Enable CORS for frontend access\n",
+    "app.add_middleware(\n",
+    "    CORSMiddleware,\n",
+    "    allow_origins=[\"*\"],\n",
+    "    allow_credentials=True,\n",
+    "    allow_methods=[\"*\"],\n",
+    "    allow_headers=[\"*\"],\n",
+    ")\n",
+    "\n",
+    "@app.get(\"/chat\")\n",
+    "def chat(query: str):\n",
+    "    response = model.generate_content(query)\n",
+    "    return {\"response\": response.text}\n",
+    "\n",
+    "# Run the server: uvicorn backend:app --reload"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "cde9c8d0-80a4-4943-a78a-a75ca4825e34",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/y9/krs1m7td1p33yj75p9f1s5740000gn/T/ipykernel_11301/2071011701.py:7: LangChainDeprecationWarning: The class `LLMChain` was deprecated in LangChain 0.1.17 and will be removed in 1.0. Use :meth:`~RunnableSequence, e.g., `prompt | llm`` instead.\n",
+      "  chain = LLMChain(llm=llm, prompt=prompt)\n",
+      "/var/folders/y9/krs1m7td1p33yj75p9f1s5740000gn/T/ipykernel_11301/2071011701.py:11: LangChainDeprecationWarning: The method `Chain.run` was deprecated in langchain 0.1.0 and will be removed in 1.0. Use :meth:`~invoke` instead.\n",
+      "  response = chain.run(query=query)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Chatbot Response: The capital of France is Paris.\n"
+     ]
+    }
+   ],
+   "source": [
+    "prompt = PromptTemplate.from_template(\"Answer the following query: {query}\")\n",
+    "\n",
+    "# Initialize LLM\n",
+    "llm = ChatGoogleGenerativeAI(model=\"gemini-2.0-flash\", temperature=0)\n",
+    "\n",
+    "# Create an LLM Chain\n",
+    "chain = LLMChain(llm=llm, prompt=prompt)\n",
+    "\n",
+    "# Run chatbot\n",
+    "query = \"What is the capital of France?\"\n",
+    "response = chain.run(query=query)\n",
+    "print(\"Chatbot Response:\", response)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "da1d9899-328b-47c2-87a5-4175519bacdc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Chatbot Response: The capital of India is **New Delhi**.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Run chatbot\n",
+    "query = \"What is the capital of India?\"\n",
+    "response = chain.run(query=query)\n",
+    "print(\"Chatbot Response:\", response)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "d42c4cf2-8b96-4310-8692-350d0d9c85b4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/Users/saurabhverma/GENAI'"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "os.getcwd()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "161cd070-2ac4-422a-8199-2a7cc42ac335",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# UI with Gradio\n",
+    "def chat_interface(question):\n",
+    "    return rag_pipeline(question)\n",
+    "\n",
+    "ui = gr.Interface(fn=chat_interface, inputs=\"text\", outputs=\"text\", title=\"RAG Chat with Gemini\")\n",
+    "ui.launch()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "cc81ff71-c152-4780-bb90-31f1df623f7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "    Uninstalling typer-0.9.0:\n",
+      "      Successfully uninstalled typer-0.9.0\n",
+      "Successfully installed aiofiles-23.2.1 ffmpy-0.5.0 gradio-5.20.1 gradio-client-1.7.2 groovy-0.1.2 huggingface-hub-0.29.2 pydub-0.25.1 python-multipart-0.0.20 ruff-0.9.10 safehttpx-0.1.6 semantic-version-2.10.0 tomlkit-0.13.2 typer-0.15.2 websockets-15.0.1\n"
+     ]
+    }
+   ],
+   "source": [
+    "#pip install -U langchain-community\n",
+    "!pip install gradio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "8c816fe4-5454-4e30-bbf9-cda7214943f5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/y9/krs1m7td1p33yj75p9f1s5740000gn/T/ipykernel_16004/3095841870.py:27: LangChainDeprecationWarning: The class `OpenAIEmbeddings` was deprecated in LangChain 0.0.9 and will be removed in 1.0. An updated version of the class exists in the :class:`~langchain-openai package and should be used instead. To use it run `pip install -U :class:`~langchain-openai` and import as `from :class:`~langchain_openai import OpenAIEmbeddings``.\n",
+      "  vector_store = Chroma.from_documents(docs, embedding=OpenAIEmbeddings())\n"
+     ]
+    },
+    {
+     "ename": "ValidationError",
+     "evalue": "1 validation error for OpenAIEmbeddings\n  Value error, Did not find openai_api_key, please add an environment variable `OPENAI_API_KEY` which contains it, or pass `openai_api_key` as a named parameter. [type=value_error, input_value={'model_kwargs': {}, 'cli...20, 'http_client': None}, input_type=dict]\n    For further information visit https://errors.pydantic.dev/2.10/v/value_error",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValidationError\u001b[0m                           Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[11], line 27\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[38;5;66;03m# Create Vector Database\u001b[39;00m\n\u001b[1;32m     26\u001b[0m docs \u001b[38;5;241m=\u001b[39m load_docs()\n\u001b[0;32m---> 27\u001b[0m vector_store \u001b[38;5;241m=\u001b[39m Chroma\u001b[38;5;241m.\u001b[39mfrom_documents(docs, embedding\u001b[38;5;241m=\u001b[39mOpenAIEmbeddings())\n\u001b[1;32m     29\u001b[0m \u001b[38;5;66;03m# RAG Pipeline: Retrieve and Generate\u001b[39;00m\n\u001b[1;32m     30\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrag_pipeline\u001b[39m(query):\n",
+      "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/langchain_core/_api/deprecation.py:214\u001b[0m, in \u001b[0;36mdeprecated.<locals>.deprecate.<locals>.finalize.<locals>.warn_if_direct_instance\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    212\u001b[0m     warned \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m    213\u001b[0m     emit_warning()\n\u001b[0;32m--> 214\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m wrapped(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/pydantic/main.py:214\u001b[0m, in \u001b[0;36mBaseModel.__init__\u001b[0;34m(self, **data)\u001b[0m\n\u001b[1;32m    212\u001b[0m \u001b[38;5;66;03m# `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks\u001b[39;00m\n\u001b[1;32m    213\u001b[0m __tracebackhide__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m--> 214\u001b[0m validated_self \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__pydantic_validator__\u001b[38;5;241m.\u001b[39mvalidate_python(data, self_instance\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m    215\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m validated_self:\n\u001b[1;32m    216\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m    217\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mA custom validator is returning a value other than `self`.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    218\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mReturning anything other than `self` from a top level model validator isn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt supported when validating via `__init__`.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    219\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSee the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m    220\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m,\n\u001b[1;32m    221\u001b[0m     )\n",
+      "\u001b[0;31mValidationError\u001b[0m: 1 validation error for OpenAIEmbeddings\n  Value error, Did not find openai_api_key, please add an environment variable `OPENAI_API_KEY` which contains it, or pass `openai_api_key` as a named parameter. [type=value_error, input_value={'model_kwargs': {}, 'cli...20, 'http_client': None}, input_type=dict]\n    For further information visit https://errors.pydantic.dev/2.10/v/value_error"
+     ]
+    }
+   ],
+   "source": [
+    "import google.generativeai as genai\n",
+    "from langchain.vectorstores import Chroma\n",
+    "from langchain.embeddings import OpenAIEmbeddings\n",
+    "from langchain.schema import Document\n",
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+    "from langchain_community.document_loaders import TextLoader\n",
+    "import gradio as gr\n",
+    "\n",
+    "# Configure Google Gemini API\n",
+    "GOOGLE_API_KEY = \"AIzaSyDRj3wAgqOCjc_D45W_u-G3y9dk5YDgxEo\"\n",
+    "genai.configure(api_key=GOOGLE_API_KEY)\n",
+    "model = genai.GenerativeModel(\"gemini-pro\")\n",
+    "\n",
+    "# Load and process documents\n",
+    "def load_docs():\n",
+    "    raw_text = \"\"\"\n",
+    "    Machine learning is a branch of artificial intelligence (AI) focused on building applications that learn from data and improve their accuracy over time.\n",
+    "    Supervised learning uses labeled data, while unsupervised learning finds hidden patterns.\n",
+    "    Reinforcement learning is based on rewards and penalties.\n",
+    "    \"\"\"\n",
+    "    text_splitter = RecursiveCharacterTextSplitter(chunk_size=100, chunk_overlap=10)\n",
+    "    docs = [Document(page_content=text) for text in text_splitter.split_text(raw_text)]\n",
+    "    return docs\n",
+    "\n",
+    "# Create Vector Database\n",
+    "docs = load_docs()\n",
+    "vector_store = Chroma.from_documents(docs, embedding=OpenAIEmbeddings())\n",
+    "\n",
+    "# RAG Pipeline: Retrieve and Generate\n",
+    "def rag_pipeline(query):\n",
+    "    results = vector_store.similarity_search(query, k=2)\n",
+    "    context = \" \".join([doc.page_content for doc in results])\n",
+    "    \n",
+    "    # Pass context + query to Gemini\n",
+    "    full_prompt = f\"Context: {context}\\n\\nQuestion: {query}\\nAnswer:\"\n",
+    "    response = model.generate_content(full_prompt)\n",
+    "    \n",
+    "    return response.text\n",
+    "\n",
+    "# UI with Gradio\n",
+    "def chat_interface(question):\n",
+    "    return rag_pipeline(question)\n",
+    "\n",
+    "ui = gr.Interface(fn=chat_interface, inputs=\"text\", outputs=\"text\", title=\"RAG Chat with Gemini\")\n",
+    "ui.launch()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "93bcb8cd-9202-45c1-98e3-9f9b06387fc2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

GenAI_1.ipynb

@@ -0,0 +1,315 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9633aea7-5c45-44f9-a78b-b5bc39984754",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_google_genai import ChatGoogleGenerativeAI\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain.chains import LLMChain\n",
+    "\n",
+    "import os\n",
+    "\n",
+    "import google.generativeai as genai\n",
+    "from langchain.document_loaders import PyPDFLoader\n",
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+    "from langchain_google_genai import ChatGoogleGenerativeAI, GoogleGenerativeAIEmbeddings\n",
+    "from langchain.vectorstores import FAISS\n",
+    "import gradio as gr\n",
+    "\n",
+    "\n",
+    "os.environ[\"MY_SECRET_KEY\"] = \"AIzaSyDRj3wAgqOCjc_D45W_u-G3y9dk5YDgxEo\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "41abde7b-366d-427e-8938-35ce7a4ed778",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#pip install pypdf\n",
+    "#!pip install faiss-cpu"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "b7e3810f-c5fb-44d7-b4b7-a30ac507d78b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "google_api_key = os.environ[\"MY_SECRET_KEY\"]\n",
+    "\n",
+    "# Check if the API key was found\n",
+    "if google_api_key:\n",
+    "    # Set the environment variable if the API key was found\n",
+    "    os.environ[\"GOOGLE_API_KEY\"] = google_api_key\n",
+    "\n",
+    "    llm = ChatGoogleGenerativeAI(\n",
+    "        model=\"gemini-pro\",  # Specify the model name\n",
+    "        google_api_key=os.environ[\"GOOGLE_API_KEY\"]\n",
+    "    )\n",
+    "else:\n",
+    "    print(\"Error: GOOGLE_API_KEY not found in Colab secrets. Please store your API key.\")\n",
+    "\n",
+    "\n",
+    "\n",
+    "genai.configure(api_key=google_api_key)\n",
+    "model = genai.GenerativeModel(\"gemini-pro\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ef330936-8c45-4aff-b2cf-fe9dfaaf2764",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "work_dir=os.getcwd()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a55af811-7758-4090-a5f8-748b6192971b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current Working Directory: /Users/saurabhverma/GENAI\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Verify file existence\n",
+    "assert \"Team1.pdf\" in os.listdir(work_dir), \"Team1.pdf not found in the specified directory!\"\n",
+    "print(f\"Current Working Directory: {os.getcwd()}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "7a0a4457-2f9c-40db-9dd4-d57e3edf1fd0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load PDF and split text\n",
+    "pdf_path = \"Team1.pdf\"  # Ensure this file is uploaded to Colab\n",
+    "loader = PyPDFLoader(pdf_path)\n",
+    "documents = loader.load()\n",
+    "\n",
+    "# Split text into chunks\n",
+    "text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=10)\n",
+    "text_chunks = text_splitter.split_documents(documents)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b5387499-a756-49de-86b0-96a5ce712ba7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Generate embeddings\n",
+    "embeddings = GoogleGenerativeAIEmbeddings(model=\"models/embedding-001\")\n",
+    "\n",
+    "# Store embeddings in FAISS index\n",
+    "vectorstore = FAISS.from_documents(text_chunks, embeddings)\n",
+    "retriever = vectorstore.as_retriever(search_kwargs={\"k\": 4})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "35554163-75cd-4f0b-a538-565a48700245",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set up Gemini model\n",
+    "llm = ChatGoogleGenerativeAI(model=\"gemini-2.0-flash-001\", temperature=0)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e95b424b-11c1-46f3-9b4e-9e2d42d1f05d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import gradio as gr\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain.chains import LLMChain\n",
+    "\n",
+    "def rag_query(query):\n",
+    "    # Retrieve relevant documents\n",
+    "    docs = retriever.get_relevant_documents(query)\n",
+    "    \n",
+    "    # Otherwise, use RAG\n",
+    "    context = \"\\n\".join([doc.page_content for doc in docs])\n",
+    "    prompt = f\"Context:\\n{context}\\n\\nQuestion: {query}\\nAnswer directly and concisely:\"\n",
+    "\n",
+    "    try:\n",
+    "        response = llm.invoke(prompt)\n",
+    "    except Exception as e:\n",
+    "        response = f\"Error in RAG processing: {str(e)}\"\n",
+    "\n",
+    "    return response.content\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "552ff2fa-3c70-4054-803e-633efc7601f4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import gradio as gr\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain.chains import LLMChain\n",
+    "from langchain_google_genai import ChatGoogleGenerativeAI\n",
+    "\n",
+    "# Initialize LLM once (avoid repeated initialization)\n",
+    "llm = ChatGoogleGenerativeAI(model=\"gemini-2.0-flash\", temperature=0)\n",
+    "\n",
+    "# Define the general query function\n",
+    "def general_query(query):\n",
+    "    try:\n",
+    "        # Define the prompt correctly\n",
+    "        prompt = PromptTemplate.from_template(\"Answer the following query: {query}\")\n",
+    "        \n",
+    "        # Create an LLM Chain\n",
+    "        chain = LLMChain(llm=llm, prompt=prompt)\n",
+    "        \n",
+    "        # Run chatbot and return response\n",
+    "        response = chain.run(query=query)\n",
+    "        \n",
+    "        return response  # Return response directly (not response.content)\n",
+    "    \n",
+    "    except Exception as e:\n",
+    "        return f\"Error: {str(e)}\"\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "ab63a509-e927-405a-985b-d07039e05e9f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "* Running on local URL:  http://127.0.0.1:7860\n",
+      "* Running on public URL: https://efeff91c52754b11ed.gradio.live\n",
+      "\n",
+      "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><iframe src=\"https://efeff91c52754b11ed.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import gradio as gr\n",
+    "\n",
+    "\n",
+    "# Function to call the selected query method\n",
+    "def query_router(query, method):\n",
+    "    if method == \"Team Query\":  # Ensure exact match with dropdown options\n",
+    "        return rag_query(query)\n",
+    "    elif method == \"General Query\":\n",
+    "        return general_query(query)\n",
+    "    return \"Invalid selection!\"\n",
+    "\n",
+    "# Define local image paths\n",
+    "logo_path = \"equinix-sign.jpg\"  # Ensure this file exists\n",
+    "\n",
+    "# Custom CSS for background styling\n",
+    "custom_css = \"\"\"\n",
+    ".gradio-container {\n",
+    "    background-color: #f0f0f0;\n",
+    "    text-align: center;\n",
+    "}\n",
+    "#logo img {\n",
+    "    display: block;\n",
+    "    margin: 0 auto;\n",
+    "    max-width: 200px; /* Adjust size */\n",
+    "}\n",
+    "\"\"\"\n",
+    "\n",
+    "# Create Gradio UI\n",
+    "with gr.Blocks(css=custom_css) as ui:\n",
+    "    gr.Image(logo_path, elem_id=\"logo\", show_label=False, height=100, width=200)  # Display Logo\n",
+    "    \n",
+    "    # Title & Description\n",
+    "    gr.Markdown(\"<h1 style='text-align: center; color: black;'>Equinix Chatbot for Automation Team</h1>\")\n",
+    "    gr.Markdown(\"<p style='text-align: center; color: black;'>Ask me anything!</p>\")\n",
+    "\n",
+    "    # Input & Dropdown Section\n",
+    "    with gr.Row():\n",
+    "        query_input = gr.Textbox(label=\"Enter your query\")\n",
+    "        query_method = gr.Dropdown([\"Team Query\", \"General Query\"], label=\"Select Query Type\")\n",
+    "    \n",
+    "    # Button for submitting query\n",
+    "    submit_button = gr.Button(\"Submit\")\n",
+    "\n",
+    "    # Output Textbox\n",
+    "    output_box = gr.Textbox(label=\"Response\", interactive=False)\n",
+    "\n",
+    "    # Button Click Event\n",
+    "    submit_button.click(query_router, inputs=[query_input, query_method], outputs=output_box)\n",
+    "\n",
+    "# Launch UI\n",
+    "ui.launch(share=True)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

RAG.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5b88dad7546bc3b74af2f8fa99991933639d8ca6c9d9b29dc40f15f835e7d1ee
+size 100412

Team.pdf

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