app.py

import gradio as gr
import openai
import os
from langchain.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import Chroma
from langchain.schema import Document

# Load the Sentence Transformer Embedding Model
model_name = "intfloat/e5-small"
embedding_model = HuggingFaceEmbeddings(model_name=model_name)

# Set up OpenAI API Key (Replace with your own API key)
openai.api_key = os.getenv("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")  # Add in Hugging Face Secrets

# Load ChromaDB with RunGalileo Dataset
persist_directory = "./docs/chroma/"
vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding_model)

from langchain_community.vectorstores import Chroma
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.schema import Document

# Load the embedding model
model_name = "intfloat/e5-small"
embedding_model = HuggingFaceEmbeddings(model_name=model_name)

# Define the ChromaDB persist directory
persist_directory = "./docs/chroma/"

#  Load ChromaDB (or create if empty)
vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding_model)

# Check if documents exist
if vectordb._collection.count() == 0:
    print("⚠️ No documents found in ChromaDB. Re-indexing dataset...")

    # Sample dataset (Replace with real RunGalileo dataset)
    documents = [
        Document(page_content="HVAC systems help regulate indoor temperature."),
        Document(page_content="Chiller plants are used in large buildings for cooling."),
        Document(page_content="BACnet is a common protocol for building automation."),
        Document(page_content="Heat pumps are essential in modern energy-efficient HVAC designs."),
        Document(page_content="Smart thermostats improve energy efficiency through AI-based control.")
    ]

    #  Insert documents into ChromaDB
    vectordb.add_documents(documents)

    print(" Documents successfully indexed into ChromaDB.")
else:
    print(f" ChromaDB contains {vectordb._collection.count()} documents.")


# Function to Retrieve Top-K Relevant Documents
def retrieve_documents(question, k=5):
    """Retrieve top K relevant documents from ChromaDB"""
    docs = vectordb.similarity_search(question, k=k)
    
    if not docs:
        return ["⚠️ No relevant documents found. Try a different query."]
    
    return [doc.page_content for doc in docs]


#  Function to Generate AI Response
import openai

def generate_response(question, context):
    """Generate AI response using OpenAI GPT-4"""
    
    if not context or "No relevant documents found." in context:
        return "No relevant context available. Try a different query."

    full_prompt = f"Context: {context}\n\nQuestion: {question}"

    try:
        client = openai.OpenAI()  # New OpenAI client format
        response = client.chat.completions.create(
            model="gpt-4",
            messages=[
                {"role": "system", "content": "You are an AI assistant that answers user queries based on the given context."},
                {"role": "user", "content": full_prompt}
            ],
            max_tokens=300,
            temperature=0.7
        )
        return response.choices[0].message.content.strip()
    except Exception as e:
        return f"Error generating response: {str(e)}"


#  Full RAG Pipeline
def rag_pipeline(question):
    retrieved_docs = retrieve_documents(question, k=5)
    context = " ".join(retrieved_docs)
    response = generate_response(question, context)
    return response, "\n\n".join(retrieved_docs)

#  Gradio UI Interface
iface = gr.Interface(
    fn=rag_pipeline,
    inputs=gr.Textbox(label="Enter your question"),
    outputs=[
        gr.Textbox(label="Generated Response"),
        gr.Textbox(label="Retrieved Documents")
    ],
    title="RAG-Based Question Answering System ",
    description="Enter a question and retrieve relevant documents with AI-generated response."
)

# ✅ Launch the Gradio App
if __name__ == "__main__":
    iface.launch()