app.py

import gradio as gr
import requests
import os
import time
import re
import logging
import tempfile
import folium
import concurrent.futures
import torch
from PIL import Image
from datetime import datetime
from transformers import pipeline, AutoModelForSpeechSeq2Seq, AutoProcessor
from googlemaps import Client as GoogleMapsClient
from gtts import gTTS
from diffusers import StableDiffusionPipeline
from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain_pinecone import PineconeVectorStore
from langchain.prompts import PromptTemplate
from langchain.chains import RetrievalQA
from langchain.chains.conversation.memory import ConversationBufferWindowMemory
from huggingface_hub import login
from transformers.models.speecht5.number_normalizer import EnglishNumberNormalizer
from parler_tts import ParlerTTSForConditionalGeneration
from transformers import AutoTokenizer, AutoFeatureExtractor, set_seed
from scipy.io.wavfile import write as write_wav
from pydub import AudioSegment
from string import punctuation
import librosa
from pathlib import Path
import torchaudio
import numpy as np
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline


# Neo4j imports
from langchain.chains import GraphCypherQAChain
from langchain_community.graphs import Neo4jGraph
from langchain_community.document_loaders import HuggingFaceDatasetLoader
from langchain_text_splitters import CharacterTextSplitter
from langchain_experimental.graph_transformers import LLMGraphTransformer
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_core.messages import AIMessage, HumanMessage
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnableBranch, RunnableLambda, RunnableParallel, RunnablePassthrough
from serpapi.google_search import GoogleSearch

#Parler TTS v1 Modules 

import os
import re
import tempfile
import soundfile as sf
from string import punctuation
from pydub import AudioSegment
from transformers import AutoTokenizer, AutoFeatureExtractor



#API AutoDate Fix Up
def get_current_date1():
    return datetime.now().strftime("%Y-%m-%d")

# Usage
current_date1 = get_current_date1()



# Set environment variables for CUDA
os.environ['PYTORCH_USE_CUDA_DSA'] = '1'
os.environ['CUDA_LAUNCH_BLOCKING'] = '1'


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)


embeddings = OpenAIEmbeddings(api_key=os.environ['OPENAI_API_KEY'])


#Initialization

# Initialize the models
def initialize_phi_model():
    model = AutoModelForCausalLM.from_pretrained(
        "microsoft/Phi-3.5-mini-instruct", 
        device_map="cuda", 
        torch_dtype="auto", 
        trust_remote_code=True, 
    )
    tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3.5-mini-instruct")
    return pipeline("text-generation", model=model, tokenizer=tokenizer)

def initialize_gpt_model():
    return ChatOpenAI(api_key=os.environ['OPENAI_API_KEY'], temperature=0, model='gpt-4o')

# Initialize both models
phi_pipe = initialize_phi_model()
gpt_model = initialize_gpt_model()


# Existing embeddings and vector store for GPT-4o
gpt_embeddings = OpenAIEmbeddings(api_key=os.environ['OPENAI_API_KEY'])
gpt_vectorstore = PineconeVectorStore(index_name="radarfinaldata08192024", embedding=gpt_embeddings)
gpt_retriever = gpt_vectorstore.as_retriever(search_kwargs={'k': 5})

# New vector store setup for Phi-3.5
phi_embeddings = OpenAIEmbeddings(api_key=os.environ['OPENAI_API_KEY'])
phi_vectorstore = PineconeVectorStore(index_name="phivector08252024", embedding=phi_embeddings)
phi_retriever = phi_vectorstore.as_retriever(search_kwargs={'k': 5})



# Pinecone setup
from pinecone import Pinecone
pc = Pinecone(api_key=os.environ['PINECONE_API_KEY'])

index_name = "radarfinaldata08192024"
vectorstore = PineconeVectorStore(index_name=index_name, embedding=embeddings)
retriever = vectorstore.as_retriever(search_kwargs={'k': 5})

chat_model = ChatOpenAI(api_key=os.environ['OPENAI_API_KEY'], temperature=0, model='gpt-4o')

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

# Prompt templates
def get_current_date():
    return datetime.now().strftime("%B %d, %Y")

current_date = get_current_date()

template1 = f"""As an expert concierge in Birmingham, Alabama, known for being a helpful and renowned guide, I am here to assist you on this sunny bright day of {current_date}. Given the current weather conditions and date, I have access to a plethora of information regarding events, places,sports and activities in Birmingham that can enhance your experience.
If you have any questions or need recommendations, feel free to ask. I have a wealth of knowledge of perennial events in Birmingham and can provide detailed information to ensure you make the most of your time here. Remember, I am here to assist you in any way possible.
Now, let me guide you through some of the exciting events happening today in Birmingham, Alabama:
Address: >>, Birmingham, AL
Time: >>__
Date: >>__
Description: >>__
Address: >>, Birmingham, AL
Time: >>__
Date: >>__
Description: >>__
Address: >>, Birmingham, AL
Time: >>__
Date: >>__
Description: >>__
Address: >>, Birmingham, AL
Time: >>__
Date: >>__
Description: >>__
Address: >>, Birmingham, AL
Time: >>__
Date: >>__
Description: >>__
If you have any specific preferences or questions about these events or any other inquiries, please feel free to ask. Remember, I am here to ensure you have a memorable and enjoyable experience in Birmingham, AL.
It was my pleasure!
{{context}}
Question: {{question}}
Helpful Answer:"""

template2 = f"""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. Given today's sunny and bright weather on {current_date}, I am well-equipped to provide valuable insights and recommendations without revealing the locations. 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 question concise for a quick and accurate response.
"It was my pleasure!"
{{context}}
Question: {{question}}
Helpful Answer:"""

QA_CHAIN_PROMPT_1 = PromptTemplate(input_variables=["context", "question"], template=template1)
QA_CHAIN_PROMPT_2 = PromptTemplate(input_variables=["context", "question"], template=template2)

# Neo4j setup
graph = Neo4jGraph(url="neo4j+s://6457770f.databases.neo4j.io",
                    username="neo4j",
                    password="Z10duoPkKCtENuOukw3eIlvl0xJWKtrVSr-_hGX1LQ4"
                    )
# Avoid pushing the graph documents to Neo4j every time
# Only push the documents once and comment the code below after the initial push
# dataset_name = "Pijush2023/birmindata07312024"
# page_content_column = 'events_description'
# loader = HuggingFaceDatasetLoader(dataset_name, page_content_column)
# data = loader.load()

# text_splitter = CharacterTextSplitter(chunk_size=100, chunk_overlap=50)
# documents = text_splitter.split_documents(data)

# llm_transformer = LLMGraphTransformer(llm=chat_model)
# graph_documents = llm_transformer.convert_to_graph_documents(documents)
# graph.add_graph_documents(graph_documents, baseEntityLabel=True, include_source=True)

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}"),
])

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

_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"]),
)

# template = """Answer the question based only on the following context:
# {context}
# Question: {question}
# Use natural language and be concise.
# Answer:"""

template = f"""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. Given today's sunny and bright weather on {current_date}, I am well-equipped to provide valuable insights and recommendations without revealing specific locations. 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 question concise for a quick,short ,crisp and accurate response.
"It was my pleasure!"
{{context}}
Question: {{question}}
Helpful Answer:"""

qa_prompt = ChatPromptTemplate.from_template(template)

chain_neo4j = (
    RunnableParallel(
        {
            "context": _search_query | retriever_neo4j,
            "question": RunnablePassthrough(),
        }
    )
    | qa_prompt
    | chat_model
    | StrOutputParser()
)





# def generate_answer(message, choice, retrieval_mode):
#     logging.debug(f"generate_answer called with choice: {choice} and retrieval_mode: {retrieval_mode}")

#     # Check if the question is about hotels
#     if "hotel" in message.lower() or "hotels" in message.lower() and "birmingham" in message.lower():
#         response = fetch_google_hotels()
#         return response, extract_addresses(response)
    
#     # Check if the question is about restaurants
#     if "restaurant" in message.lower() or "restaurants" in message.lower() and "birmingham" in message.lower():
#         response = fetch_yelp_restaurants()
#         return response, extract_addresses(response)
#     # Check if the question is about flights
#     if "flight" in message.lower() or "flights" in message.lower() and "birmingham" in message.lower():
#         response = fetch_google_flights()
#         return response, extract_addresses(response)

#     prompt_template = QA_CHAIN_PROMPT_1 if choice == "Details" else QA_CHAIN_PROMPT_2

#     if retrieval_mode == "VDB":
#         qa_chain = RetrievalQA.from_chain_type(
#             llm=chat_model,
#             chain_type="stuff",
#             retriever=retriever,
#             chain_type_kwargs={"prompt": prompt_template}
#         )
#         response = qa_chain({"query": message})
#         logging.debug(f"Vector response: {response}")
#         return response['result'], extract_addresses(response['result'])
#     elif retrieval_mode == "KGF":
#         response = chain_neo4j.invoke({"question": message})
#         logging.debug(f"Knowledge-Graph response: {response}")
#         return response, extract_addresses(response)
#     else:
#         return "Invalid retrieval mode selected.", []



def generate_answer(message, choice, retrieval_mode, selected_model):
    logging.debug(f"generate_answer called with choice: {choice} and retrieval_mode: {retrieval_mode}")

    try:
        # Handle hotel-related queries
        if "hotel" in message.lower() or "hotels" in message.lower() and "birmingham" in message.lower():
            response = fetch_google_hotels()
            return response, extract_addresses(response)
        
        # Handle restaurant-related queries
        if "restaurant" in message.lower() or "restaurants" in message.lower() and "birmingham" in message.lower():
            response = fetch_yelp_restaurants()
            return response, extract_addresses(response)

        # Handle flight-related queries
        if "flight" in message.lower() or "flights" in message.lower() and "birmingham" in message.lower():
            response = fetch_google_flights()
            return response, extract_addresses(response)

        if retrieval_mode == "VDB":
            if selected_model == chat_model:
                # Use GPT-4o with its vector store and template
                retriever = gpt_retriever
                prompt_template = QA_CHAIN_PROMPT_1 if choice == "Details" else QA_CHAIN_PROMPT_2
                context = retriever.get_relevant_documents(message)
                prompt = prompt_template.format(context=context, question=message)

                qa_chain = RetrievalQA.from_chain_type(
                    llm=chat_model,
                    chain_type="stuff",
                    retriever=retriever,
                    chain_type_kwargs={"prompt": prompt_template}
                )
                response = qa_chain({"query": message})
                return response['result'], extract_addresses(response['result'])

            elif selected_model == phi_pipe:
                # Use Phi-3.5 with its vector store and a simplified prompt
                retriever = phi_retriever
                context = retriever.get_relevant_documents(message)
                prompt = f"""
                Here is the information based on the documents provided:
                {context}

                {message}
                """

                logging.debug(f"Phi-3.5 Prompt: {prompt}")

                response = selected_model(prompt, **{
                    "max_new_tokens": 300,
                    "return_full_text": False,
                    "temperature": 0.5,
                    "do_sample": False,
                })

                if response:
                    generated_text = response[0]['generated_text']
                    logging.debug(f"Phi-3.5 Response: {generated_text}")
                    return generated_text, extract_addresses(generated_text)
                else:
                    logging.error("Phi-3.5 did not return any response.")
                    return "No response generated.", []

        elif retrieval_mode == "KGF":
            response = chain_neo4j.invoke({"question": message})
            return response, extract_addresses(response)
        else:
            return "Invalid retrieval mode selected.", []

    except Exception as e:
        logging.error(f"Error in generate_answer: {e}")
        return "Sorry, I encountered an error while processing your request.", []


# def bot(history, choice, tts_choice, retrieval_mode):
#     if not history:
#         return history
    
#     response, addresses = generate_answer(history[-1][0], choice, retrieval_mode)
#     history[-1][1] = ""

#     with concurrent.futures.ThreadPoolExecutor() as executor:
#         if tts_choice == "Alpha":
#             audio_future = executor.submit(generate_audio_elevenlabs, response)
#         elif tts_choice == "Beta":
#             audio_future = executor.submit(generate_audio_parler_tts, response)
#         elif tts_choice == "Gamma":
#             audio_future = executor.submit(generate_audio_mars5, response)

#         for character in response:
#             history[-1][1] += character
#             time.sleep(0.05)
#             yield history, None

#         audio_path = audio_future.result()
#         yield history, audio_path

#     history.append([response, None])  # Ensure the response is added in the correct format



def bot(history, choice, tts_choice, retrieval_mode, model_choice):
    if not history:
        return history
    
    # Select the model
    selected_model = chat_model if model_choice == "GPT-4o" else phi_pipe
    
    response, addresses = generate_answer(history[-1][0], choice, retrieval_mode, selected_model)
    history[-1][1] = ""

    with concurrent.futures.ThreadPoolExecutor() as executor:
        if tts_choice == "Alpha":
            audio_future = executor.submit(generate_audio_elevenlabs, response)
        elif tts_choice == "Beta":
            audio_future = executor.submit(generate_audio_parler_tts, response)
        # elif tts_choice == "Gamma":
        #     audio_future = executor.submit(generate_audio_mars5, response)

        for character in response:
            history[-1][1] += character
            time.sleep(0.05)
            yield history, None

        audio_path = audio_future.result()
        yield history, audio_path

    history.append([response, None])





def add_message(history, message):
    history.append((message, None))
    return history, gr.Textbox(value="", interactive=True, show_label=False)

def print_like_dislike(x: gr.LikeData):
    print(x.index, x.value, x.liked)

def extract_addresses(response):
    if not isinstance(response, str):
        response = str(response)
    address_patterns = [
        r'([A-Z].*,\sBirmingham,\sAL\s\d{5})',
        r'(\d{4}\s.*,\sBirmingham,\sAL\s\d{5})',
        r'([A-Z].*,\sAL\s\d{5})',
        r'([A-Z].*,.*\sSt,\sBirmingham,\sAL\s\d{5})',
        r'([A-Z].*,.*\sStreets,\sBirmingham,\sAL\s\d{5})',
        r'(\d{2}.*\sStreets)',
        r'([A-Z].*\s\d{2},\sBirmingham,\sAL\s\d{5})',
        r'([a-zA-Z]\s Birmingham)',
        r'([a-zA-Z].*,\sBirmingham,\sAL)',
        r'(.*),(Birmingham, AL,USA)$'
        r'(^Birmingham,AL$)',
        r'((.*)(Stadium|Field),.*,\sAL$)',
        r'((.*)(Stadium|Field),.*,\sFL$)',
        r'((.*)(Stadium|Field),.*,\sMS$)',
        r'((.*)(Stadium|Field),.*,\sAR$)',
        r'((.*)(Stadium|Field),.*,\sKY$)',
        r'((.*)(Stadium|Field),.*,\sTN$)',
        r'((.*)(Stadium|Field),.*,\sLA$)',
        r'((.*)(Stadium|Field),.*,\sFL$)'
        
    ]
    addresses = []
    for pattern in address_patterns:
        addresses.extend(re.findall(pattern, response))
    return addresses

all_addresses = []

def generate_map(location_names):
    global all_addresses
    all_addresses.extend(location_names)
    
    api_key = os.environ['GOOGLEMAPS_API_KEY']
    gmaps = GoogleMapsClient(key=api_key)
    
    m = folium.Map(location=[33.5175, -86.809444], zoom_start=12)
    
    for location_name in all_addresses:
        geocode_result = gmaps.geocode(location_name)
        if geocode_result:
            location = geocode_result[0]['geometry']['location']
            folium.Marker(
                [location['lat'], location['lng']],
                tooltip=f"{geocode_result[0]['formatted_address']}"
            ).add_to(m)
    
    map_html = m._repr_html_()
    return map_html


def fetch_local_news():
    api_key = os.environ['SERP_API']
    url = f'https://serpapi.com/search.json?engine=google_news&q=birmingham headline&api_key={api_key}'
    response = requests.get(url)
    if response.status_code == 200:
        results = response.json().get("news_results", [])
        news_html = """
        <h2 style="font-family: 'Georgia', serif; color: #ff0000; background-color: #f8f8f8; padding: 10px; border-radius: 10px;">Birmingham Today</h2>
        <style>
            .news-item {
                font-family: 'Verdana', sans-serif;
                color: #333;
                background-color: #f0f8ff;
                margin-bottom: 15px;
                padding: 10px;
                border-radius: 5px;
                transition: box-shadow 0.3s ease, background-color 0.3s ease;
                font-weight: bold;
            }
            .news-item:hover {
                box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
                background-color: #e6f7ff;
            }
            .news-item a {
                color: #1E90FF;
                text-decoration: none;
                font-weight: bold;
            }
            .news-item a:hover {
                text-decoration: underline;
            }
            .news-preview {
                position: absolute;
                display: none;
                border: 1px solid #ccc;
                border-radius: 5px;
                box-shadow: 0 2px 4px rgba(0, 0, 0, 0.2);
                background-color: white;
                z-index: 1000;
                max-width: 300px;
                padding: 10px;
                font-family: 'Verdana', sans-serif;
                color: #333;
            }
        </style>
        <script>
            function showPreview(event, previewContent) {
                var previewBox = document.getElementById('news-preview');
                previewBox.innerHTML = previewContent;
                previewBox.style.left = event.pageX + 'px';
                previewBox.style.top = event.pageY + 'px';
                previewBox.style.display = 'block';
            }
            function hidePreview() {
                var previewBox = document.getElementById('news-preview');
                previewBox.style.display = 'none';
            }
        </script>
        <div id="news-preview" class="news-preview"></div>
        """
        for index, result in enumerate(results[:7]):
            title = result.get("title", "No title")
            link = result.get("link", "#")
            snippet = result.get("snippet", "")
            news_html += f"""
            <div class="news-item" onmouseover="showPreview(event, '{snippet}')" onmouseout="hidePreview()">
                <a href='{link}' target='_blank'>{index + 1}. {title}</a>
                <p>{snippet}</p>
            </div>
            """
        return news_html
    else:
        return "<p>Failed to fetch local news</p>"

import numpy as np
import torch
from transformers import pipeline, AutoModelForSpeechSeq2Seq, AutoProcessor

model_id = 'openai/whisper-large-v3'
device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch_dtype).to(device)
processor = AutoProcessor.from_pretrained(model_id)

pipe_asr = pipeline("automatic-speech-recognition", model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, max_new_tokens=128, chunk_length_s=15, batch_size=16, torch_dtype=torch_dtype, device=device, return_timestamps=True)

base_audio_drive = "/data/audio"

#Normal Code with sample rate is 44100 Hz

def transcribe_function(stream, new_chunk):
    try:
        sr, y = new_chunk[0], new_chunk[1]
    except TypeError:
        print(f"Error chunk structure: {type(new_chunk)}, content: {new_chunk}")
        return stream, "", None

    y = y.astype(np.float32) / np.max(np.abs(y))

    if stream is not None:
        stream = np.concatenate([stream, y])
    else:
        stream = y

    result = pipe_asr({"array": stream, "sampling_rate": sr}, return_timestamps=False)

    full_text = result.get("text","")
    
    return stream, full_text, result







def update_map_with_response(history):
    if not history:
        return ""
    response = history[-1][1]
    addresses = extract_addresses(response)
    return generate_map(addresses)

def clear_textbox():
    return "" 

def show_map_if_details(history, choice):
    if choice in ["Details", "Conversational"]:
        return gr.update(visible=True), update_map_with_response(history)
    else:
        return gr.update(visible(False), "")








def generate_audio_elevenlabs(text):
    XI_API_KEY = os.environ['ELEVENLABS_API']
    VOICE_ID = 'WX6XNe3QcmzYHt4DymI9'
    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:
        audio_segments = []
        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as f:
            for chunk in response.iter_content(chunk_size=1024):
                if chunk:
                    f.write(chunk)
                    audio_segments.append(chunk)
            temp_audio_path = f.name

        # Combine all audio chunks into a single file
        combined_audio = AudioSegment.from_file(temp_audio_path, format="mp3")
        combined_audio_path = os.path.join(tempfile.gettempdir(), "elevenlabs_combined_audio.mp3")
        combined_audio.export(combined_audio_path, format="mp3")

        logging.debug(f"Audio saved to {combined_audio_path}")
        return combined_audio_path
    else:
        logging.error(f"Error generating audio: {response.text}")
        return None


repo_id = "parler-tts/parler-tts-mini-v1"

parler_model = ParlerTTSForConditionalGeneration.from_pretrained(repo_id).to(device)
parler_tokenizer = AutoTokenizer.from_pretrained(repo_id)
parler_feature_extractor = AutoFeatureExtractor.from_pretrained(repo_id)

SAMPLE_RATE = parler_feature_extractor.sampling_rate

def preprocess(text):
    number_normalizer = EnglishNumberNormalizer()
    text = number_normalizer(text).strip()
    if text[-1] not in punctuation:
        text = f"{text}."

    abbreviations_pattern = r'\b[A-Z][A-Z\.]+\b'

    def separate_abb(chunk):
        chunk = chunk.replace(".", "")
        return " ".join(chunk)

    abbreviations = re.findall(abbreviations_pattern, text)
    for abv in abbreviations:
        if abv in text:
            text = text.replace(abv, separate_abb(abv))
    return text

def chunk_text(text, max_length=250):
    words = text.split()
    chunks = []
    current_chunk = []
    current_length = 0

    for word in words:
        if current_length + len(word) + 1 <= max_length:
            current_chunk.append(word)
            current_length += len(word) + 1
        else:
            chunks.append(' '.join(current_chunk))
            current_chunk = [word]
            current_length = len(word) + 1

    if current_chunk:
        chunks.append(' '.join(current_chunk))

    return chunks

def generate_audio_parler_tts(text):
    description = "A female speaker delivers a slightly expressive and animated speech with a moderate speed and pitch. The recording is of very high quality, with the speaker's voice sounding clear and very close up."
    chunks = chunk_text(preprocess(text))
    audio_segments = []

    for chunk in chunks:
        input_ids = parler_tokenizer(description, return_tensors="pt").input_ids.to(device)
        prompt_input_ids = parler_tokenizer(chunk, return_tensors="pt").input_ids.to(device)

        generation = parler_model.generate(input_ids=input_ids, prompt_input_ids=prompt_input_ids)
        audio_arr = generation.cpu().numpy().squeeze()

        temp_audio_path = os.path.join(tempfile.gettempdir(), f"parler_tts_audio_{len(audio_segments)}.wav")
        sf.write(temp_audio_path, audio_arr, parler_model.config.sampling_rate)
        audio_segments.append(AudioSegment.from_wav(temp_audio_path))

    combined_audio = sum(audio_segments)
    combined_audio_path = os.path.join(tempfile.gettempdir(), "parler_tts_combined_audio.wav")
    combined_audio.export(combined_audio_path, format="wav")

    logging.debug(f"Audio saved to {combined_audio_path}")
    return combined_audio_path








# # Load the MARS5 model
# mars5, config_class = torch.hub.load('Camb-ai/mars5-tts', 'mars5_english', trust_repo=True)

# def generate_audio_mars5(text):
#     description = "Thomas speaks with emphasis and excitement at a moderate pace with high quality."
#     kwargs_dict = {
#         'temperature': 0.2,
#         'top_k': -1,
#         'top_p': 0.2,
#         'typical_p': 1.0,
#         'freq_penalty': 2.6,
#         'presence_penalty': 0.4,
#         'rep_penalty_window': 100,
#         'max_prompt_phones': 360,
#         'deep_clone': True,
#         'nar_guidance_w': 3
#     }

#     chunks = chunk_text(preprocess(text))
#     audio_segments = []

#     for chunk in chunks:
#         wav = torch.zeros(1, mars5.sr)  # Use a placeholder silent audio for the reference
#         cfg = config_class(**{k: kwargs_dict[k] for k in kwargs_dict if k in config_class.__dataclass_fields__})
#         ar_codes, wav_out = mars5.tts(chunk, wav, "", cfg=cfg)
        
#         temp_audio_path = os.path.join(tempfile.gettempdir(), f"mars5_audio_{len(audio_segments)}.wav")
#         torchaudio.save(temp_audio_path, wav_out.unsqueeze(0), mars5.sr)
#         audio_segments.append(AudioSegment.from_wav(temp_audio_path))

#     combined_audio = sum(audio_segments)
#     combined_audio_path = os.path.join(tempfile.gettempdir(), "mars5_combined_audio.wav")
#     combined_audio.export(combined_audio_path, format="wav")

#     logging.debug(f"Audio saved to {combined_audio_path}")
#     return combined_audio_path

# pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2", torch_dtype=torch.float16)
# pipe.to(device)

# def generate_image(prompt):
#     with torch.cuda.amp.autocast():
#         image = pipe(
#             prompt,
#             num_inference_steps=28,
#             guidance_scale=3.0,
#         ).images[0]
#     return image

# hardcoded_prompt_1 = "Give a high quality photograph of a great looking red 2026 Toyota coupe against a skyline setting in the night, michael mann style in omaha enticing the consumer to buy this product"
# hardcoded_prompt_2 = "A vibrant and dynamic football game scene in the style of Peter Paul Rubens, showcasing the intense match between Alabama and Nebraska. The players are depicted with the dramatic, muscular physiques and expressive faces typical of Rubens' style. The Alabama team is wearing their iconic crimson and white uniforms, while the Nebraska team is in their classic red and white attire. The scene is filled with action, with players in mid-motion, tackling, running, and catching the ball. The background features a grand stadium filled with cheering fans, banners, and the natural landscape in the distance. The colors are rich and vibrant, with a strong use of light and shadow to create depth and drama. The overall atmosphere captures the intensity and excitement of the game, infused with the grandeur and dynamism characteristic of Rubens' work."
# hardcoded_prompt_3 = "Create a high-energy scene of a DJ performing on a large stage with vibrant lights, colorful lasers, a lively dancing crowd, and various electronic equipment in the background."

# def update_images():
#     image_1 = generate_image(hardcoded_prompt_1)
#     image_2 = generate_image(hardcoded_prompt_2)
#     image_3 = generate_image(hardcoded_prompt_3)
#     return image_1, image_2, image_3




def fetch_local_events():
    api_key = os.environ['SERP_API']
    url = f'https://serpapi.com/search.json?engine=google_events&q=Events+in+Birmingham&hl=en&gl=us&api_key={api_key}'
    response = requests.get(url)
    if response.status_code == 200:
        events_results = response.json().get("events_results", [])
        events_html = """
        <h2 style="font-family: 'Georgia', serif; color: #ff0000; background-color: #f8f8f8; padding: 10px; border-radius: 10px;">Local Events</h2>
        <style>
            table {
                font-family: 'Verdana', sans-serif;
                color: #333;
                border-collapse: collapse;
                width: 100%;
            }
            th, td {
                border: 1px solid #fff !important;
                padding: 8px;
            }
            th {
                background-color: #f2f2f2;
                color: #333;
                text-align: left;
            }
            tr:hover {
                background-color: #f5f5f5;
            }
            .event-link {
                color: #1E90FF;
                text-decoration: none;
            }
            .event-link:hover {
                text-decoration: underline;
            }
        </style>
        <table>
            <tr>
                <th>Title</th>
                <th>Date and Time</th>
                <th>Location</th>
            </tr>
        """
        for event in events_results:
            title = event.get("title", "No title")
            date_info = event.get("date", {})
            date = f"{date_info.get('start_date', '')} {date_info.get('when', '')}".replace("{", "").replace("}", "")
            location = event.get("address", "No location")
            if isinstance(location, list):
                location = " ".join(location)
            location = location.replace("[", "").replace("]", "")
            link = event.get("link", "#")
            events_html += f"""
            <tr>
                <td><a class='event-link' href='{link}' target='_blank'>{title}</a></td>
                <td>{date}</td>
                <td>{location}</td>
            </tr>
            """
        events_html += "</table>"
        return events_html
    else:
        return "<p>Failed to fetch local events</p>"

def get_weather_icon(condition):
    condition_map = {
        "Clear": "c01d",
        "Partly Cloudy": "c02d",
        "Cloudy": "c03d",
        "Overcast": "c04d",
        "Mist": "a01d",
        "Patchy rain possible": "r01d",
        "Light rain": "r02d",
        "Moderate rain": "r03d",
        "Heavy rain": "r04d",
        "Snow": "s01d",
        "Thunderstorm": "t01d",
        "Fog": "a05d",
    }
    return condition_map.get(condition, "c04d")

def fetch_local_weather():
    try:
        api_key = os.environ['WEATHER_API']
        url = f'https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/birmingham?unitGroup=metric&include=events%2Calerts%2Chours%2Cdays%2Ccurrent&key={api_key}'
        response = requests.get(url)
        response.raise_for_status()
        jsonData = response.json()
        
        current_conditions = jsonData.get("currentConditions", {})
        temp_celsius = current_conditions.get("temp", "N/A")
        
        if temp_celsius != "N/A":
            temp_fahrenheit = int((temp_celsius * 9/5) + 32)
        else:
            temp_fahrenheit = "N/A"
            
        condition = current_conditions.get("conditions", "N/A")
        humidity = current_conditions.get("humidity", "N/A")

        weather_html = f"""
        <div class="weather-theme">
            <h2 style="font-family: 'Georgia', serif; color: #ff0000; background-color: #f8f8f8; padding: 10px; border-radius: 10px;">Local Weather</h2>
            <div class="weather-content">
                <div class="weather-icon">
                    <img src="https://www.weatherbit.io/static/img/icons/{get_weather_icon(condition)}.png" alt="{condition}" style="width: 100px; height: 100px;">
                </div>
                <div class="weather-details">
                    <p style="font-family: 'Verdana', sans-serif; color: #333; font-size: 1.2em;">Temperature: {temp_fahrenheit}°F</p>
                    <p style="font-family: 'Verdana', sans-serif; color: #333; font-size: 1.2em;">Condition: {condition}</p>
                    <p style="font-family: 'Verdana', sans-serif; color: #333; font-size: 1.2em;">Humidity: {humidity}%</p>
                </div>
            </div>
        </div>
        <style>
            .weather-theme {{
                animation: backgroundAnimation 10s infinite alternate;
                border-radius: 10px;
                padding: 10px;
                margin-bottom: 15px;
                background: linear-gradient(45deg, #ffcc33, #ff6666, #ffcc33, #ff6666);
                background-size: 400% 400%;
                box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
                transition: box-shadow 0.3s ease, background-color 0.3s ease;
            }}
            .weather-theme:hover {{
                box-shadow: 0 8px 16px rgba(0, 0, 0, 0.2);
                background-position: 100% 100%;
            }}
            @keyframes backgroundAnimation {{
                0% {{ background-position: 0% 50%; }}
                100% {{ background-position: 100% 50%; }}
            }}
            .weather-content {{
                display: flex;
                align-items: center;
            }}
            .weather-icon {{
                flex: 1;
            }}
            .weather-details {{
                flex 3;
            }}
        </style>
        """
        return weather_html
    except requests.exceptions.RequestException as e:
        return f"<p>Failed to fetch local weather: {e}</p>"


def handle_retrieval_mode_change(choice):
    if choice == "KGF":
        return gr.update(interactive=False), gr.update(interactive=False)
    else:
        return gr.update(interactive=True), gr.update(interactive=True)






def format_restaurant_hotel_info(name, link, location, phone, rating, reviews, snippet):
    return f"""
    {name}

    - Link: {link} 
    - Location: {location}
    - Contact No: {phone} 
    - Rating: {rating} stars ({reviews} reviews) 
    - Snippet: {snippet}
    """

def fetch_yelp_restaurants():
    # Introductory prompt for restaurants
    intro_prompt = "Here are some of the top-rated restaurants in Birmingham, Alabama. I hope these suggestions help you find the perfect place to enjoy your meal:"
    
    params = {
        "engine": "yelp",
        "find_desc": "Restaurant",
        "find_loc": "Birmingham, AL, USA",
        "api_key": os.getenv("SERP_API")
    }

    search = GoogleSearch(params)
    results = search.get_dict()
    organic_results = results.get("organic_results", [])

    response_text = f"{intro_prompt}\n"
    
    for result in organic_results[:5]:  # Limiting to top 5 restaurants
        name = result.get("title", "No name")
        rating = result.get("rating", "No rating")
        reviews = result.get("reviews", "No reviews")
        phone = result.get("phone", "Not Available")
        snippet = result.get("snippet", "Not Available")
        location = f"{name}, Birmingham, AL,USA"
        link = result.get("link", "#")

        response_text += format_restaurant_hotel_info(name, link, location, phone, rating, reviews, snippet)
        
    
    return response_text






def format_hotel_info(name, link, location, rate_per_night, total_rate, description, check_in_time, check_out_time, amenities):
    return f"""
    {name}

    - Link: {link} 
    - Location: {location}
    - Rate per Night: {rate_per_night} (Before taxes/fees: {total_rate})
    - Check-in Time: {check_in_time}
    - Check-out Time: {check_out_time}
    - Amenities: {amenities}
    - Description: {description}
    """

def fetch_google_hotels(query="Birmingham Hotel", check_in=current_date1, check_out="2024-08-20", adults=2):
    # Introductory prompt for hotels
    intro_prompt = "Here are some of the best hotels in Birmingham, Alabama, for your stay. Each of these options offers a unique experience, whether you're looking for luxury, comfort, or convenience:"
    
    params = {
        "engine": "google_hotels",
        "q": query,
        "check_in_date": check_in,
        "check_out_date": check_out,
        "adults": str(adults),
        "currency": "USD",
        "gl": "us",
        "hl": "en",
        "api_key": os.getenv("SERP_API")
    }
    
    search = GoogleSearch(params)
    results = search.get_dict()
    hotel_results = results.get("properties", [])
    
    hotel_info = f"{intro_prompt}\n"
    for hotel in hotel_results[:5]:  # Limiting to top 5 hotels
        name = hotel.get('name', 'No name')
        description = hotel.get('description', 'No description')
        link = hotel.get('link', '#')
        check_in_time = hotel.get('check_in_time', 'N/A')
        check_out_time = hotel.get('check_out_time', 'N/A')
        rate_per_night = hotel.get('rate_per_night', {}).get('lowest', 'N/A')
        before_taxes_fees = hotel.get('rate_per_night', {}).get('before_taxes_fees', 'N/A')
        total_rate = hotel.get('total_rate', {}).get('lowest', 'N/A')
        amenities = ", ".join(hotel.get('amenities', [])) if hotel.get('amenities') else "Not Available"
        
        location = f"{name}, Birmingham, AL,USA"

        hotel_info += format_hotel_info(
            name, 
            link, 
            location, 
            rate_per_night, 
            total_rate, 
            description, 
            check_in_time, 
            check_out_time,
            amenities
        )
       
    
    return hotel_info




def format_flight_info(flight_number, departure_airport, departure_time, arrival_airport, arrival_time, duration, airplane):
    return f"""
    Flight {flight_number}

    - Departure: {departure_airport} at {departure_time}
    - Arrival: {arrival_airport} at {arrival_time}
    - Duration: {duration} minutes
    - Airplane: {airplane}
    """

def fetch_google_flights(departure_id="JFK", arrival_id="BHM", outbound_date=current_date1, return_date="2024-08-20"):
    # Introductory prompt for flights
    intro_prompt = "Here are some available flights from JFK to Birmingham, Alabama. These options provide a range of times and durations to fit your travel needs:"
    
    params = {
        "engine": "google_flights",
        "departure_id": departure_id,
        "arrival_id": arrival_id,
        "outbound_date": outbound_date,
        "return_date": return_date,
        "currency": "USD",
        "hl": "en",
        "api_key": os.getenv("SERP_API")
    }
    
    search = GoogleSearch(params)
    results = search.get_dict()
    
    # Extract flight details from the results
    best_flights = results.get('best_flights', [])
    flight_info = f"{intro_prompt}\n"
    
    # Process each flight in the best_flights list
    for i, flight in enumerate(best_flights, start=1):
        for segment in flight.get('flights', []):
            departure_airport = segment.get('departure_airport', {}).get('name', 'Unknown Departure Airport')
            departure_time = segment.get('departure_airport', {}).get('time', 'Unknown Time')
            arrival_airport = segment.get('arrival_airport', {}).get('name', 'Unknown Arrival Airport')
            arrival_time = segment.get('arrival_airport', {}).get('time', 'Unknown Time')
            duration = segment.get('duration', 'Unknown Duration')
            airplane = segment.get('airplane', 'Unknown Airplane')
            
            # Format the flight segment details
            flight_info += format_flight_info(
                flight_number=i, 
                departure_airport=departure_airport, 
                departure_time=departure_time, 
                arrival_airport=arrival_airport, 
                arrival_time=arrival_time, 
                duration=duration, 
                airplane=airplane
            )
            
    
    return flight_info











# with gr.Blocks(theme='Pijush2023/scikit-learn-pijush') as demo:
#     with gr.Row():
#         with gr.Column():
#             state = gr.State()

#             chatbot = gr.Chatbot([], elem_id="RADAR:Channel 94.1", bubble_full_width=False)
#             choice = gr.Radio(label="Select Style", choices=["Details", "Conversational"], value="Conversational")
#             retrieval_mode = gr.Radio(label="Retrieval Mode", choices=["VDB", "KGF"], value="VDB")

#             gr.Markdown("<h1 style='color: red;'>Talk to RADAR</h1>", elem_id="voice-markdown")

#             chat_input = gr.Textbox(show_copy_button=True, interactive=True, show_label=False, label="ASK Radar !!!", placeholder="Hey Radar...!!")
#             tts_choice = gr.Radio(label="Select TTS System", choices=["Alpha", "Beta", "Gamma"], value="Alpha")
#             retriever_button = gr.Button("Retriever")

#             clear_button = gr.Button("Clear")
#             clear_button.click(lambda:[None,None] ,outputs=[chat_input, state])

#             gr.Markdown("<h1 style='color: red;'>Radar Map</h1>", elem_id="Map-Radar")
#             location_output = gr.HTML()
            
#             # Define a single audio component
#             audio_output = gr.Audio(interactive=False, autoplay=True)

#             def stop_audio():
#                 audio_output.stop()
#                 return None

#             # Define the sequence of actions for the "Retriever" button
#             retriever_sequence = (
#                 retriever_button.click(fn=stop_audio, inputs=[], outputs=[audio_output],api_name="Ask_Retriever")
#                 .then(fn=add_message, inputs=[chatbot, chat_input], outputs=[chatbot, chat_input],api_name="voice_query")
#                 .then(fn=bot, inputs=[chatbot, choice, tts_choice, retrieval_mode], outputs=[chatbot, audio_output],api_name="generate_voice_response" )
#                 .then(fn=show_map_if_details, inputs=[chatbot, choice], outputs=[location_output, location_output], api_name="map_finder")
#                 .then(fn=clear_textbox, inputs=[], outputs=[chat_input])
#             )

#             # Link the "Enter" key (submit event) to the same sequence of actions
#             chat_input.submit(fn=stop_audio, inputs=[], outputs=[audio_output])
#             chat_input.submit(fn=add_message, inputs=[chatbot, chat_input], outputs=[chatbot, chat_input],api_name="voice_query").then(
#                 fn=bot, inputs=[chatbot, choice, tts_choice, retrieval_mode], outputs=[chatbot, audio_output], api_name="generate_voice_response"
#             ).then(
#                 fn=show_map_if_details, inputs=[chatbot, choice], outputs=[location_output, location_output], api_name="map_finder"
#             ).then(
#                 fn=clear_textbox, inputs=[], outputs=[chat_input]
#             )

#             audio_input = gr.Audio(sources=["microphone"], streaming=True, type='numpy', every=0.1)
#             audio_input.stream(transcribe_function, inputs=[state, audio_input], outputs=[state, chat_input], api_name="voice_query_to_text")

#             # Handle retrieval mode change
#             retrieval_mode.change(fn=handle_retrieval_mode_change, inputs=retrieval_mode, outputs=[choice, choice])

#         with gr.Column():
#             weather_output = gr.HTML(value=fetch_local_weather())
#             news_output = gr.HTML(value=fetch_local_news())
#             events_output = gr.HTML(value=fetch_local_events())
#             # restaurant_output=gr.HTML(value=fetch_yelp_restaurants())
        
          
        

#         with gr.Column():
#             image_output_1 = gr.Image(value=generate_image(hardcoded_prompt_1), width=400, height=400)
#             image_output_2 = gr.Image(value=generate_image(hardcoded_prompt_2), width=400, height=400)
#             image_output_3 = gr.Image(value=generate_image(hardcoded_prompt_3), width=400, height=400)

#             refresh_button = gr.Button("Refresh Images")
#             refresh_button.click(fn=update_images, inputs=None, outputs=[image_output_1, image_output_2, image_output_3], api_name="update_image")
    

# demo.queue()
# demo.launch(share=True)

with gr.Blocks(theme='Pijush2023/scikit-learn-pijush') as demo:
    with gr.Row():
        with gr.Column():
            state = gr.State()

            chatbot = gr.Chatbot([], elem_id="RADAR:Channel 94.1", bubble_full_width=False)
            choice = gr.Radio(label="Select Style", choices=["Details", "Conversational"], value="Conversational")
            retrieval_mode = gr.Radio(label="Retrieval Mode", choices=["VDB", "KGF"], value="VDB")
            model_choice = gr.Dropdown(label="Choose Model", choices=["GPT-4o", "Phi-3.5"], value="GPT-4o")

            gr.Markdown("<h1 style='color: red;'>Talk to RADAR</h1>", elem_id="voice-markdown")

            chat_input = gr.Textbox(show_copy_button=True, interactive=True, show_label=False, label="ASK Radar !!!", placeholder="Hey Radar...!!")
            tts_choice = gr.Radio(label="Select TTS System", choices=["Alpha", "Beta"], value="Alpha")
            retriever_button = gr.Button("Retriever")

            clear_button = gr.Button("Clear")
            clear_button.click(lambda:[None,None], outputs=[chat_input, state])

            gr.Markdown("<h1 style='color: red;'>Radar Map</h1>", elem_id="Map-Radar")
            location_output = gr.HTML()
            audio_output = gr.Audio(interactive=False, autoplay=True)

            def stop_audio():
                audio_output.stop()
                return None

            retriever_sequence = (
                retriever_button.click(fn=stop_audio, inputs=[], outputs=[audio_output], api_name="Ask_Retriever")
                .then(fn=add_message, inputs=[chatbot, chat_input], outputs=[chatbot, chat_input], api_name="voice_query")
                .then(fn=bot, inputs=[chatbot, choice, tts_choice, retrieval_mode, model_choice], outputs=[chatbot, audio_output], api_name="generate_voice_response")
                .then(fn=show_map_if_details, inputs=[chatbot, choice], outputs=[location_output, location_output], api_name="map_finder")
                .then(fn=clear_textbox, inputs=[], outputs=[chat_input])
            )

            chat_input.submit(fn=stop_audio, inputs=[], outputs=[audio_output])
            chat_input.submit(fn=add_message, inputs=[chatbot, chat_input], outputs=[chatbot, chat_input], api_name="voice_query").then(
                fn=bot, inputs=[chatbot, choice, tts_choice, retrieval_mode, model_choice], outputs=[chatbot, audio_output], api_name="generate_voice_response"
            ).then(
                fn=show_map_if_details, inputs=[chatbot, choice], outputs=[location_output, location_output], api_name="map_finder"
            ).then(
                fn=clear_textbox, inputs=[], outputs=[chat_input]
            )

            audio_input = gr.Audio(sources=["microphone"], streaming=True, type='numpy', every=0.1)
            audio_input.stream(transcribe_function, inputs=[state, audio_input], outputs=[state, chat_input], api_name="voice_query_to_text")

            retrieval_mode.change(fn=handle_retrieval_mode_change, inputs=retrieval_mode, outputs=[choice, choice])

        with gr.Column():
            weather_output = gr.HTML(value=fetch_local_weather())
            news_output = gr.HTML(value=fetch_local_news())
            events_output = gr.HTML(value=fetch_local_events())

        # with gr.Column():
        #     image_output_1 = gr.Image(value=generate_image(hardcoded_prompt_1), width=400, height=400)
        #     image_output_2 = gr.Image(value=generate_image(hardcoded_prompt_2), width=400, height=400)
        #     image_output_3 = gr.Image(value=generate_image(hardcoded_prompt_3), width=400, height=400)

        #     refresh_button = gr.Button("Refresh Images")
        #     refresh_button.click(fn=update_images, inputs=None, outputs=[image_output_1, image_output_2, image_output_3], api_name="update_image")

demo.queue()
demo.launch(share=True)