GenAI_1.py

#!/usr/bin/env python
# coding: utf-8

# In[144]:


from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain

import os

import google.generativeai as genai
from langchain.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_google_genai import ChatGoogleGenerativeAI, GoogleGenerativeAIEmbeddings
from langchain.vectorstores import FAISS
import gradio as gr


os.environ["MY_SECRET_KEY"] = "AIzaSyDRj3wAgqOCjc_D45W_u-G3y9dk5YDgxEo"


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#pip install pypdf
#!pip install faiss-cpu


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google_api_key = os.environ["MY_SECRET_KEY"]

# Check if the API key was found
if google_api_key:
    # Set the environment variable if the API key was found
    os.environ["GOOGLE_API_KEY"] = google_api_key

    llm = ChatGoogleGenerativeAI(
        model="gemini-pro",  # Specify the model name
        google_api_key=os.environ["GOOGLE_API_KEY"]
    )
else:
    print("Error: GOOGLE_API_KEY not found in Colab secrets. Please store your API key.")



genai.configure(api_key=google_api_key)
model = genai.GenerativeModel("gemini-pro")


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work_dir=os.getcwd()


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# Verify file existence
assert "RAG.pdf" in os.listdir(work_dir), "RAG.pdf not found in the specified directory!"
print(f"Current Working Directory: {os.getcwd()}")


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# Load PDF and split text
pdf_path = "RAG.pdf"  # Ensure this file is uploaded to Colab
loader = PyPDFLoader(pdf_path)
documents = loader.load()

# Split text into chunks
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=10)
text_chunks = text_splitter.split_documents(documents)


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# Generate embeddings
embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")

# Store embeddings in FAISS index
vectorstore = FAISS.from_documents(text_chunks, embeddings)
retriever = vectorstore.as_retriever(search_kwargs={"k": 4})


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# Set up Gemini model
llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash-001", temperature=0)
#llm = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0)


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import gradio as gr
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain

def rag_query(query):
    # Retrieve relevant documents
    docs = retriever.get_relevant_documents(query)
    
    # Otherwise, use RAG
    context = "\n".join([doc.page_content for doc in docs])
    prompt = f"Context:\n{context}\n\nQuestion: {query}\nAnswer directly and concisely:"

    try:
        response = llm.invoke(prompt)
    except Exception as e:
        response = f"Error in RAG processing: {str(e)}"

    return response.content




# In[153]:


import gradio as gr
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain_google_genai import ChatGoogleGenerativeAI

# Initialize LLM once (avoid repeated initialization)
llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)

# Define the general query function
def general_query(query):
    try:
        # Define the prompt correctly
        prompt = PromptTemplate.from_template("Answer the following query: {query}")
        
        # Create an LLM Chain
        chain = LLMChain(llm=llm, prompt=prompt)
        
        # Run chatbot and return response
        response = chain.run(query=query)
        
        return response  # Return response directly (not response.content)
    
    except Exception as e:
        return f"Error: {str(e)}"



# In[154]:


import gradio as gr


# Function to call the selected query method
def query_router(query, method):
    if method == "Team Query":  # Ensure exact match with dropdown options
        return rag_query(query)
    elif method == "General Query":
        return general_query(query)
    return "Invalid selection!"

# Define local image paths
logo_path = "Equinix-LOGO.jpeg"  # Ensure this file exists

# Custom CSS for background styling
custom_css = """
.gradio-container {
    background-color: #f0f0f0;
    text-align: center;
}
#logo img {
    display: block;
    margin: 0 auto;
    max-width: 200px; /* Adjust size */
}
"""

# Create Gradio UI
with gr.Blocks(css=custom_css) as ui:
    gr.Image(logo_path, elem_id="logo", show_label=False, height=100, width=200)  # Display Logo
    
    # Title & Description
    gr.Markdown("<h1 style='text-align: center; color: black;'>Equinix Chatbot for Automation Team</h1>")
    gr.Markdown("<p style='text-align: center; color: black;'>Ask me anything!</p>")

    # Input & Dropdown Section
    with gr.Row():
        query_input = gr.Textbox(label="Enter your query")
        query_method = gr.Dropdown(["Team Query", "General Query"], label="Select Query Type")
    
    # Button for submitting query
    submit_button = gr.Button("Submit")

    # Output Textbox
    output_box = gr.Textbox(label="Response", interactive=False)

    # Button Click Event
    submit_button.click(query_router, inputs=[query_input, query_method], outputs=output_box)

# Launch UI
ui.launch(share=True)


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get_ipython().system('jupyter nbconvert --to script GenAI_1.ipynb')