Upload app.py

app.py

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+#!/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')
+