app.py

from langchain_groq import ChatGroq
import os
import gradio as gr
from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory
from langchain_community.embeddings.fastembed import FastEmbedEmbeddings
from langchain.document_loaders import PyPDFLoader
from langchain_experimental.text_splitter import SemanticChunker
from langchain.vectorstores import FAISS
from gtts import gTTS
import tempfile

# Set your API key from Hugging Face Secrets
# DO NOT hardcode your API key here
GROQ_API_KEY = os.environ.get('GROQ_API_KEY')

# Initialize Groq LLM
llm = ChatGroq(
    model_name="llama3-70b-8192",
    temperature=0.7,
    api_key=GROQ_API_KEY
)

# Initialize memory
memory = ConversationBufferMemory()
conversation = ConversationChain(llm=llm, memory=memory)

# Load PDF and create embeddings
def initialize_rag():
    try:
        # Load the PDF document
        loader = PyPDFLoader("TourismChatbot.pdf")
        pages = loader.load_and_split()
        
        # Create embeddings
        embed_model = FastEmbedEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")
        
        # Create semantic chunks
        semantic_chunker = SemanticChunker(embed_model, breakpoint_threshold_type="percentile")
        semantic_chunks = semantic_chunker.create_documents([d.page_content for d in pages])
        
        # Create vector store
        vectorstore = FAISS.from_documents(documents=semantic_chunks, embedding=embed_model)
        
        return vectorstore, embed_model
    except Exception as e:
        print(f"Error initializing RAG: {e}")
        # Return None if initialization fails
        return None, None

# Initialize RAG components
vectorstore, embed_model = initialize_rag()

# Function to retrieve relevant information from the vector store
def retrieve_relevant_chunks(query, top_k=3):
    try:
        if vectorstore is not None:
            documents = vectorstore.similarity_search(query, k=top_k)
            return [doc.page_content for doc in documents]
        else:
            # Fallback content if vectorstore is not available
            return ["Rajasthan is a state in India known for its forts, palaces, and desert landscapes."]
    except Exception as e:
        print(f"Error retrieving chunks: {e}")
        return ["Rajasthan is a state in India known for its forts, palaces, and desert landscapes."]

def generate_rag_response(query, language="English"):
    retrieved_chunks = retrieve_relevant_chunks(query)
    context = "\n".join(retrieved_chunks)
    prompt = f"""
    Please provide the answer in **{language}**.

    You are a helpful AI assistant providing tourism information about Rajasthan.
    Answer based on the following context. If information is unavailable, say "I don't know."

    Context: {context}
    Question: {query}

    Answer:
    """
    response = conversation.run(prompt)
    return response.strip()

def generate_speech(text, language):
    lang_map = {"English": "en", "Hindi": "hi", "Spanish": "es", "French": "fr", "German": "de", "Tamil": "ta"}
    lang_code = lang_map.get(language, "en")
    tts = gTTS(text, lang=lang_code)
    temp_audio_path = tempfile.NamedTemporaryFile(suffix=".mp3", delete=False).name
    tts.save(temp_audio_path)
    return temp_audio_path

def chatbot_interface(query, language, chat_history):
    response = generate_rag_response(query, language)
    speech_path = generate_speech(response, language)

    # Just append a 🔊 icon and use Gradio to handle the file
    response_with_audio = f"{response} 🔊 (Click play below)"
    chat_history.append((query, response_with_audio))

    return chat_history, speech_path, ""  # Return file path as separate gr.Audio

def handle_menu_click(topic, language, chat_history):
    query = f"Give me information about {topic} in Rajasthan."
    return chatbot_interface(query, language, chat_history)

# Define language and menu options
language_options = ['English', 'Hindi', 'Spanish', 'French', 'German', 'Tamil']
menu_options = ["Places to Visit", "Best Time to Visit", "Festivals", "Cuisine", "Travel Tips"]

# Create the Gradio interface
with gr.Blocks(css="""
    body {background-color: #FFF2E1; font-family: Arial, sans-serif;}
    .gradio-container {max-width: 800px; margin: auto; padding: 20px; background: #FFF2E1;
        border-radius: 15px; box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);}
    .gradio-title {color: #462f22; text-align: center; font-size: 24px; font-weight: bold;
        padding-bottom: 10px;}
    .gradio-chat {border: 1px solid #e1c7a6; border-radius: 10px; padding: 10px; background: #fff;
        min-height: 250px; color:#462f22}
    .gr-button {background-color:#FFFCF5; color: #ec8d12; font-size: 14px; border-radius: 8px;
        padding: 8px; border: 2px solid #e6ac55; cursor: pointer;}
    .gr-button:hover {background-color: #ec8d12;color:#fff}
    .clear-chat {float: right; background: #fff3e0; border: 1px solid #ed5722; color: #ed5722;
        font-weight: bold; border-radius: 6px; padding: 5px 10px; cursor: pointer;}
    .chat-input {width: 100%; padding: 10px; border-radius: 8px; border: 1px solid #e1c7a6;}
""") as demo:

    gr.Markdown("<h2 class='gradio-title'>🐪 Rajasthan Tourism Chatbot</h2>")

    language_selector = gr.Dropdown(language_options, value="English", label="Select Language")
    chatbot = gr.Chatbot(label="Rajasthan Tourism Assistant", elem_classes="gradio-chat")

    with gr.Row():
        for topic in menu_options:
            btn = gr.Button(topic, elem_classes="gr-button")
            btn.click(handle_menu_click,
                      inputs=[gr.Textbox(value=topic, visible=False), language_selector, chatbot],
                      outputs=[chatbot, gr.Audio(label="🔊 Audio Response", type="filepath"), gr.Textbox()])

    query_input = gr.Textbox(placeholder="Ask about Rajasthan...", label="Enter your query", elem_classes="chat-input")
    audio_output = gr.Audio(label="🔊 Audio Response", type="filepath", visible=True)

    query_input.submit(
        chatbot_interface,
        inputs=[query_input, language_selector, chatbot],
        outputs=[chatbot, audio_output, query_input]
    )

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