Delete GenAI_1.py

GenAI_1.py

@@ -1,215 +0,0 @@
-#!/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"
-
-
-# In[145]:
-
-
-#pip install pypdf
-#!pip install faiss-cpu
-
-
-# In[146]:
-
-
-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")
-
-
-# In[147]:
-
-
-work_dir=os.getcwd()
-
-
-# In[148]:
-
-
-# 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()}")
-
-
-# In[149]:
-
-
-# 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)
-
-
-# In[150]:
-
-
-# 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})
-
-
-# In[151]:
-
-
-# Set up Gemini model
-llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash-001", temperature=0)
-#llm = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0)
-
-
-# In[152]:
-
-
-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)
-
-
-# In[168]:
-
-
-get_ipython().system('jupyter nbconvert --to script GenAI_1.ipynb')
-