app.py

import gradio as gr
import os
import logging
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_openai import ChatOpenAI
from langchain_community.graphs import Neo4jGraph
from typing import List, Tuple
from pydantic import BaseModel, Field
from langchain_core.messages import AIMessage, HumanMessage
from langchain_core.runnables import (
    RunnableBranch,
    RunnableLambda,
    RunnablePassthrough,
    RunnableParallel,
)
from langchain_core.prompts.prompt import PromptTemplate
import requests
import tempfile
from langchain.memory import ConversationBufferWindowMemory
import time



conversational_memory = ConversationBufferWindowMemory(
        memory_key='chat_history',
        k=10,
        return_messages=True
    )

# Setup Neo4j
graph = Neo4jGraph(
    url="neo4j+s://6457770f.databases.neo4j.io",
    username="neo4j",
    password="Z10duoPkKCtENuOukw3eIlvl0xJWKtrVSr-_hGX1LQ4"
)

# Define entity extraction and retrieval functions
class Entities(BaseModel):
    names: List[str] = Field(
        ..., description="All the person, organization, or business entities that appear in the text"
    )

entity_prompt = ChatPromptTemplate.from_messages([
    ("system", "You are extracting organization and person entities from the text."),
    ("human", "Use the given format to extract information from the following input: {question}"),
])

chat_model = ChatOpenAI(temperature=0, model_name="gpt-4o", api_key=os.environ['OPENAI_API_KEY'])
entity_chain = entity_prompt | chat_model.with_structured_output(Entities)

def remove_lucene_chars(input: str) -> str:
    return input.translate(str.maketrans({
        "\\": r"\\", "+": r"\+", "-": r"\-", "&": r"\&", "|": r"\|", "!": r"\!",
        "(": r"\(", ")": r"\)", "{": r"\{", "}": r"\}", "[": r"\[", "]": r"\]",
        "^": r"\^", "~": r"\~", "*": r"\*", "?": r"\?", ":": r"\:", '"': r'\"',
        ";": r"\;", " ": r"\ "
    }))

def generate_full_text_query(input: str) -> str:
    full_text_query = ""
    words = [el for el in remove_lucene_chars(input).split() if el]
    for word in words[:-1]:
        full_text_query += f" {word}~2 AND"
    full_text_query += f" {words[-1]}~2"
    return full_text_query.strip()

def structured_retriever(question: str) -> str:
    result = ""
    entities = entity_chain.invoke({"question": question})
    for entity in entities.names:
        response = graph.query(
            """CALL db.index.fulltext.queryNodes('entity', $query, {limit:2})
            YIELD node,score
            CALL {
              WITH node
              MATCH (node)-[r:!MENTIONS]->(neighbor)
              RETURN node.id + ' - ' + type(r) + ' -> ' + neighbor.id AS output
              UNION ALL
              WITH node
              MATCH (node)<-[r:!MENTIONS]-(neighbor)
              RETURN neighbor.id + ' - ' + type(r) + ' -> ' +  node.id AS output
            }
            RETURN output LIMIT 50
            """,
            {"query": generate_full_text_query(entity)},
        )
        result += "\n".join([el['output'] for el in response])
    return result

def retriever_neo4j(question: str):
    structured_data = structured_retriever(question)
    logging.debug(f"Structured data: {structured_data}")
    return structured_data

# Setup for condensing the follow-up questions
_template = """Given the following conversation and a follow-up question, rephrase the follow-up question to be a standalone question,
in its original language.
Chat History:
{chat_history}
Follow Up Input: {question}
Standalone question:"""

CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)

def _format_chat_history(chat_history: list[tuple[str, str]]) -> list:
    buffer = []
    for human, ai in chat_history:
        buffer.append(HumanMessage(content=human))
        buffer.append(AIMessage(content=ai))
    return buffer

_search_query = RunnableBranch(
    (
        RunnableLambda(lambda x: bool(x.get("chat_history"))).with_config(
            run_name="HasChatHistoryCheck"
        ),
        RunnablePassthrough.assign(
            chat_history=lambda x: _format_chat_history(x["chat_history"])
        )
        | CONDENSE_QUESTION_PROMPT
        | ChatOpenAI(temperature=0, api_key=os.environ['OPENAI_API_KEY'])
        | StrOutputParser(),
    ),
    RunnableLambda(lambda x: x["question"]),
)

# Define the QA prompt template
#template = """As an expert concierge known for being helpful and a renowned guide for Birmingham, Alabama, 
#I assist visitors in discovering the best that the city has to offer. I also assist the visitors about various sports and activities.
#I am well-equipped to provide valuable insights and recommendations. 
#I draw upon my extensive knowledge of the area, including perennial events and historical context.
#In light of this, how can I assist you today? Feel free to ask any questions or seek recommendations for your day in Birmingham. If there's anything specific you'd like to know or experience, 
#please share, and I'll be glad to help. Remember, keep the response precise short, crisp, and accurate response and don't greet.
#{context}
#Question: {question}
#Helpful Answer:"""


template = """I am a guide for Birmingham, Alabama. I can provide recommendations and insights about the city, including events and activities.
Ask your question directly, and I'll provide a precise and quick,short and crisp response in a conversational way without any Greet.
{context}
Question: {question}
Answer:"""


qa_prompt = ChatPromptTemplate.from_template(template)

# Define the chain for Neo4j-based retrieval and response generation
chain_neo4j = (
    RunnableParallel(
        {
            "context": _search_query | retriever_neo4j,
            "question": RunnablePassthrough(),
        }
    )
    | qa_prompt
    | chat_model
    | StrOutputParser()
)

# Define the function to get the response
def get_response(question):
    try:
        return chain_neo4j.invoke({"question": question})
    except Exception as e:
        return f"Error: {str(e)}"

# Define the function to clear input and output
def clear_fields():
    return [],"",None

# Function to generate audio with Eleven Labs TTS
def generate_audio_elevenlabs(text):
    XI_API_KEY = os.environ['ELEVENLABS_API']
    VOICE_ID = 'ehbJzYLQFpwbJmGkqbnW'
    tts_url = f"https://api.elevenlabs.io/v1/text-to-speech/{VOICE_ID}/stream"
    headers = {
        "Accept": "application/json",
        "xi-api-key": XI_API_KEY
    }
    data = {
        "text": str(text),
        "model_id": "eleven_multilingual_v2",
        "voice_settings": {
            "stability": 1.0,
            "similarity_boost": 0.0,
            "style": 0.60,
            "use_speaker_boost": False
        }
    }
    response = requests.post(tts_url, headers=headers, json=data, stream=True)
    if response.ok:
        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as f:
            for chunk in response.iter_content(chunk_size=1024):
                if chunk:
                    f.write(chunk)
            audio_path = f.name
        logging.debug(f"Audio saved to {audio_path}")
        return audio_path  # Return audio path for automatic playback
    else:
        logging.error(f"Error generating audio: {response.text}")
        return None


# Define function to generate a streaming response
def chat_with_bot(messages, user_message):
    # Add user message to the chat history
    messages.append((user_message, ""))
    response = get_response(user_message)
    
    # Simulate streaming response by iterating over each character in the response
    for character in response:
        messages[-1] = (user_message, messages[-1][1] + character)
        yield messages  # Stream each character
        time.sleep(0.05)  # Adjust delay as needed for real-time effect

    yield messages  # Final yield to ensure full response is displayed


# Function to generate audio with Eleven Labs TTS from the last bot response
def generate_audio_from_last_response(history):
    # Get the most recent bot response from the chat history
    if history and len(history) > 0:
        recent_response = history[-1][1]  # The second item in the tuple is the bot response text
        if recent_response:
            return generate_audio_elevenlabs(recent_response)
    return None



# Create the Gradio Blocks interface
with gr.Blocks() as demo:
    chatbot = gr.Chatbot([], elem_id="RADAR", bubble_full_width=False)
    with gr.Row():
        with gr.Column():
            #response_output = gr.Textbox(
                #label="Response",
                #placeholder="The response will appear here...",
                #interactive=False,
                #lines=10,  # Sets the number of visible lines
                #max_lines=20  # Allows for a larger display area if needed
            #)
            question_input = gr.Textbox(label="Ask a Question", placeholder="Type your question here...")
        
        with gr.Column():
            audio_output = gr.Audio(label="Audio", type="filepath", interactive=False)
    
    with gr.Row():
        with gr.Column():
            get_response_btn = gr.Button("Get Response")
        with gr.Column():
            generate_audio_btn = gr.Button("Generate Audio")
        with gr.Column():
            clean_btn = gr.Button("Clean")
    
    # Define interactions
    #get_response_btn.click(fn=get_response, inputs=question_input, outputs=response_output)
    #generate_audio_btn.click(fn=generate_audio_elevenlabs, inputs=response_output, outputs=audio_output)
    #clean_btn.click(fn=clear_fields, inputs=[], outputs=[question_input, response_output])

    # Define interactions
    get_response_btn.click(fn=chat_with_bot, inputs=[chatbot, question_input], outputs=chatbot)
    generate_audio_btn.click(fn=generate_audio_from_last_response, inputs=chatbot, outputs=audio_output)
    clean_btn.click(fn=clear_fields, inputs=[], outputs=[chatbot, question_input, audio_output])

# Launch the Gradio interface
demo.launch(show_error=True)