app.py

import os
import gradio as gr
from langchain_redis import RedisConfig, RedisVectorStore
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_groq import ChatGroq
from langchain_community.embeddings import OpenAIEmbeddings
import logging
from huggingface_hub import login

hf_token = os.getenv("HF_TOKEN")
if hf_token is None:
    print("Please set your Hugging Face token in the environment variables.")
else:
    login(token=hf_token)

logging.basicConfig(level=logging.DEBUG)
   



# Set API keys
openai_api_key=os.environ["OPENAI_API_KEY"]
groq_api_key=os.environ["GROQ_API_KEY"]

# Define Redis configuration
REDIS_URL = "redis://:KWq0uAoBYjBGErKvyMvexMqB9ep7v2Ct@redis-11044.c266.us-east-1-3.ec2.redns.redis-cloud.com:11044"
config = RedisConfig(
    index_name="radar_data_index",
    redis_url=REDIS_URL,
    metadata_schema=[
        {"name": "category", "type": "tag"},
        {"name": "name", "type": "text"},
        {"name": "address", "type": "text"},
        {"name": "phone", "type": "text"},
    ],
)


# Initialize OpenAI Embeddings
embeddings = OpenAIEmbeddings(api_key=os.environ['OPENAI_API_KEY'])

# Initialize Redis Vector Store with Hugging Face embeddings
vector_store = RedisVectorStore(embeddings, config=config)
retriever = vector_store.as_retriever(search_type="similarity", search_kwargs={"k": 5})


# Define the language model
llm = ChatGroq(model="llama-3.2-1b-preview")

# Define prompt
prompt = ChatPromptTemplate.from_messages(
    [
        (
           "human",
           """"You’re Annie, a country music voicebot and media personality created by Amit Lamba, guiding folks around Birmingham, Alabama. 
           Provide complete, accurate, and relevant information, ensuring no key details are missed. Keep responses concise and engaging, with a touch of Southern charm and humor, while strictly avoiding irrelevant content, engaging answers that encourage follow-up questions.
Question: {question}
Context: {context}
Answer:""",
        ),
    ]
)



def format_docs(docs):
    return "\n\n".join(doc.page_content for doc in docs)

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


#



# Function to handle chatbot interaction
def rag_chain_response(messages, user_message):
    # Generate a response using the RAG chain
    response = rag_chain.invoke(user_message)
    
    # Append the user's message and the response to the chat
    messages.append((user_message, response))
    
    # Return the updated chat and clear the input box
    return messages, ""



    

# Define the Gradio app
with gr.Blocks(theme="rawrsor1/Everforest") as app:
    
    
    chatbot = gr.Chatbot([], elem_id="RADAR", bubble_full_width=False)
    question_input = gr.Textbox(label="Ask a Question", placeholder="Type your question here...")
    submit_btn = gr.Button("Submit")
    
    # Set up interaction for both Enter key and Submit button
    question_input.submit(
        rag_chain_response,          # Function to handle input and generate response
        inputs=[chatbot, question_input],  # Pass current conversation state and user input
        outputs=[chatbot, question_input],  # Update conversation state and clear the input
        api_name="api_get_response_on_enter"
    )
    submit_btn.click(
        rag_chain_response,          # Function to handle input and generate response
        inputs=[chatbot, question_input],  # Pass current conversation state and user input
        outputs=[chatbot, question_input],  # Update conversation state and clear the input
        api_name="api_get_response_on_submit_button"
    )

# Launch the Gradio app
app.launch(show_error=True)