# Read the data
import pandas as pd
df = pd.read_csv('./Automobile_data.csv')
#df = df.drop(columns = ['normalized-losses','symboling'], axis = 1)

context_data = []
for i in range(len(df)):  # Loop over rows
    context = ""
    for j in range(26):  # Loop over the first 8 columns
        context += df.columns[j]  # Add column name
        context += ": "
        context += str(df.iloc[i][j])  # Convert value to string
        context += " "
    context_data.append(context)


import os

# Get the secret key from the environment
groq_key = os.environ.get('groq_API_Keys')

## LLM used for RAG
from langchain_groq import ChatGroq

llm = ChatGroq(model="llama-3.1-70b-versatile",api_key=groq_key)

## Embedding model!
from langchain_huggingface import HuggingFaceEmbeddings
embed_model = HuggingFaceEmbeddings(model_name="mixedbread-ai/mxbai-embed-large-v1")

# create vector store!
from langchain_chroma import Chroma

vectorstore = Chroma(
    collection_name="car_dataset_store",
    embedding_function=embed_model,
    persist_directory="./",
)

# add data to vector nstore
vectorstore.add_texts(context_data)

retriever = vectorstore.as_retriever()

from langchain_core.prompts import PromptTemplate

template = ("""You are a car expert.
    Use the provided context to answer the question.
    If you don't know the answer, say so. Explain your answer in detail.
    Do not discuss the context in your response; just provide the answer directly.

    Context: {context}

    Question: {question}

    Answer:""")

rag_prompt = PromptTemplate.from_template(template)

from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough

rag_chain = (
    {"context": retriever, "question": RunnablePassthrough()}
    | rag_prompt
    | llm
    | StrOutputParser()
)

import gradio as gr

# Mock for `rag_chain.stream` or similar streaming function
class MockRAGChain:
    @staticmethod
    def stream(message):
        response = f"Processing your message: {message}"
        yield response  # Mock single response for testing

rag_chain = MockRAGChain()  # Replace with your actual RAG chain object

# Function for chatbot responses (simplified for testing)
def rag_memory_stream(message, history):
    for response in rag_chain.stream(message):  # Streaming response
        return response  # Mock response for now

# Examples and app information
examples = ['I need a car', 'What is the make and fuel type of a car?']
description = "An advanced chatbot that helps you choose the right car based on your preferences and budget."
title = "Car Expert :) Let Me Help You Find the Perfect Ride!"

# Custom theme with sky-blue background and black text
custom_theme = gr.themes.Base(primary_hue="blue", secondary_hue="green").set(
    body_background_fill="#87CEEB",  # Sky blue background
    body_text_color="#000000",      # Black text
)

# Interface with Car Preferences and Chat
with gr.Blocks(theme=custom_theme) as demo:
    gr.Markdown(f"# {title}")
    gr.Markdown(description)

    with gr.Tabs():
        # Chat Tab
        with gr.Tab("Chat"):
            chat_interface = gr.ChatInterface(
                fn=rag_memory_stream,  # Simplified chat function
                type="value",          # Use "value" to simplify interactions
                examples=examples,
            )

        # Additional Tabs (optional)
        with gr.Tab("Car Preferences"):
            gr.Markdown("This tab handles car preferences (not modified here).")

# Launch the app
if __name__ == "__main__":
    demo.launch()