app.py

import os
import re
import time
import requests
import logging
import folium
import gradio as gr
import tempfile
import torch
from datetime import datetime
import numpy as np
from gtts import gTTS
from googlemaps import Client as GoogleMapsClient
from diffusers import StableDiffusion3Pipeline
import concurrent.futures

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 langchain.agents import Tool, initialize_agent
from huggingface_hub import login

# Check if the token is already set in the environment variables
hf_token = os.getenv("HF_TOKEN")

if hf_token is None:
    # If the token is not set, prompt for it (this should be done securely)
    print("Please set your Hugging Face token in the environment variables.")
else:
    # Login using the token
    login(token=hf_token)

# Your application logic goes here
print("Logged in successfully to Hugging Face Hub!")



# Set up logging
logging.basicConfig(level=logging.DEBUG)

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

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

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

# Initialize ChatOpenAI model
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
)

def get_current_time_and_date():
    now = datetime.now()
    return now.strftime("%Y-%m-%d %H:%M:%S")

# Example usage
current_time_and_date = get_current_time_and_date()

def fetch_local_events():
    api_key = os.environ['SERP_API']
    url = f'https://serpapi.com/search.json?engine=google_events&q=Events+in+Omaha&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: #4CAF50; background-color: #f8f8f8; padding: 10px; border-radius: 10px;">Local Events</h2>
        <style>
            .event-item {
                font-family: 'Verdana', sans-serif;
                color: #333;
                background-color: #f0f8ff;
                margin-bottom: 15px;
                padding: 10px;
                border: 1px solid #ddd;
                border-radius: 5px;
                transition: box-shadow 0.3s ease, background-color 0.3s ease;
                font-weight: bold;
            }
            .event-item:hover {
                box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
                background-color: #e6f7ff;
            }
            .event-item a {
                color: #1E90FF;
                text-decoration: none;
                font-weight: bold;
            }
            .event-item a:hover {
                text-decoration: underline;
            }
            .event-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('event-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('event-preview');
                previewBox.style.display = 'none';
            }
        </script>
        <div id="event-preview" class="event-preview"></div>
        """
        for index, event in enumerate(events_results):
            title = event.get("title", "No title")
            date = event.get("date", "No date")
            location = event.get("address", "No location")
            link = event.get("link", "#")
            events_html += f"""
            <div class="event-item" onmouseover="showPreview(event, 'Date: {date}<br>Location: {location}')" onmouseout="hidePreview()">
                <a href='{link}' target='_blank'>{index + 1}. {title}</a>
                <p>Date: {date}<br>Location: {location}</p>
            </div>
            """
        return events_html
    else:
        return "<p>Failed to fetch local events</p>"

def fetch_local_weather():
    try:
        api_key = os.environ['WEATHER_API']
        url = f'https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/omaha?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 = (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: #4CAF50; 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: 1px solid #ddd;
                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 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")

# Update prompt templates to include fetched details

current_time_and_date = get_current_time_and_date()



# Define prompt templates
template1 = f"""You are an expert concierge who is helpful and a renowned guide for Omaha, Nebraska. Based on weather being a sunny bright day and the today's date is {current_time_and_date}, use the following pieces of context, 
memory, and message history, along with your knowledge of perennial events in Omaha, Nebraska, to answer the question at the end. If you don't know the answer, just say "Homie, I need to get more data for this," and don't try to make up an answer. 
Use fifteen sentences maximum. Keep the answer as detailed as possible. Always include the address, time, date, and
event type and description. Always say "It was my pleasure!" at the end of the answer.
{{context}}
Question: {{question}}
Helpful Answer:"""

template2 = f"""You are an expert concierge who is helpful and a renowned guide for Omaha, Nebraska. Based on today's weather being a sunny bright day and today's date is {current_time_and_date}, take the location or address but don't show the location or address on the output prompts. Use the following pieces of context, 
memory, and message history, along with your knowledge of perennial events in Omaha, Nebraska, to answer the question at the end. If you don't know the answer, just say "Homie, I need to get more data for this," and don't try to make up an answer. 
Keep the answer short and sweet and crisp. Always say "It was my pleasure!" at the end of the answer.
{{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)


# Define the retrieval QA chain
def build_qa_chain(prompt_template):
    qa_chain = RetrievalQA.from_chain_type(
        llm=chat_model,
        chain_type="stuff",
        retriever=retriever,
        chain_type_kwargs={"prompt": prompt_template}
    )
    tools = [
        Tool(
            name='Knowledge Base',
            func=qa_chain,
            description='Use this tool when answering general knowledge queries to get more information about the topic'
        )
    ]
    return qa_chain, tools

# Define the agent initializer
def initialize_agent_with_prompt(prompt_template):
    qa_chain, tools = build_qa_chain(prompt_template)
    agent = initialize_agent(
        agent='chat-conversational-react-description',
        tools=tools,
        llm=chat_model,
        verbose=False,
        max_iteration=5,
        early_stopping_method='generate',
        memory=conversational_memory
    )
    return agent

# Define the function to generate answers
def generate_answer(message, choice):
    logging.debug(f"generate_answer called with prompt_choice: {choice}")
    
    if choice == "Details":
        agent = initialize_agent_with_prompt(QA_CHAIN_PROMPT_1)
    elif choice == "Conversational":
        agent = initialize_agent_with_prompt(QA_CHAIN_PROMPT_2)
    else:
        logging.error(f"Invalid prompt_choice: {choice}. Defaulting to 'Conversational'")
        agent = initialize_agent_with_prompt(QA_CHAIN_PROMPT_2)
    response = agent(message)

    # Extract addresses for mapping regardless of the choice
    addresses = extract_addresses(response['output'])
    return response['output'], addresses



def bot(history, choice):
    if not history:
        return history
    response, addresses = generate_answer(history[-1][0], choice)
    history[-1][1] = ""
    
    # Generate audio for the entire response in a separate thread
    with concurrent.futures.ThreadPoolExecutor() as executor:
        audio_future = executor.submit(generate_audio_elevenlabs, response)
        
        for character in response:
            history[-1][1] += character
            time.sleep(0.05)  # Adjust the speed of text appearance
            yield history, None
        
        audio_path = audio_future.result()
        yield history, audio_path

def add_message(history, message):
    history.append((message, None))
    return history, gr.Textbox(value="", interactive=True, placeholder="Enter message or upload file...", 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].*,\sOmaha,\sNE\s\d{5})',
        r'(\d{4}\s.*,\sOmaha,\sNE\s\d{5})',
        r'([A-Z].*,\sNE\s\d{5})',
        r'([A-Z].*,.*\sSt,\sOmaha,\sNE\s\d{5})',
        r'([A-Z].*,.*\sStreets,\sOmaha,\sNE\s\d{5})',
        r'(\d{2}.*\sStreets)',
        r'([A-Z].*\s\d{2},\sOmaha,\sNE\s\d{5})'
    ]
    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=[41.2565, -95.9345], 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=ohama 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: #4CAF50; background-color: #f8f8f8; padding: 10px; border-radius: 10px;">Omaha Today Headlines</h2>
        <style>
            .news-item {
                font-family: 'Verdana', sans-serif;
                color: #333;
                background-color: #f0f8ff;
                margin-bottom: 15px;
                padding: 10px;
                border: 1px solid #ddd;
                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>"


# Voice Control
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,
                                                  #low_cpu_mem_usage=True,
                                                  use_safetensors=True).to(device)
processor = AutoProcessor.from_pretrained(model_id)

# Optimized ASR pipeline
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"

import numpy as np

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 = 'SHZHI20rSPDR3iE8SvZ0'  # Replace with your voice ID
    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": 0.7,
            "similarity_boost": 0.5,
            "style": 0.50,  # Adjust style for more romantic tone
            "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):
                f.write(chunk)
            temp_audio_path = f.name
        logging.debug(f"Audio saved to {temp_audio_path}")
        return temp_audio_path
    else:
        logging.error(f"Error generating audio: {response.text}")
        return None

# Stable Diffusion setup
pipe = StableDiffusion3Pipeline.from_pretrained("stabilityai/stable-diffusion-3-medium-diffusers", torch_dtype=torch.float16)
pipe = pipe.to("cuda")

def generate_image(prompt):
    image = pipe(
        prompt,
        negative_prompt="",
        num_inference_steps=28,
        guidance_scale=3.0,
    ).images[0]
    return image

# Hardcoded prompt for image generation
hardcoded_prompt = "Useing The top events like 'Summer Art Festival' and current time - 4:07 PM ,Date - 06/17/2024 ,Weather-Sunny Bright Day.Create Highly Visually Compelling High Resolution  and High Quality Photographics Advatizement for 'Toyota'"




with gr.Blocks(theme='rawrsor1/Everforest') as demo:
    with gr.Row():
        chatbot = gr.Chatbot([], elem_id="chatbot", bubble_full_width=False)
        with gr.Column():
            weather_output = gr.HTML(value=fetch_local_weather())
        
        with gr.Column():
            news_output = gr.HTML(value=fetch_local_news())

    def setup_ui():
        state = gr.State()
        with gr.Row():
            with gr.Column():
              gr.Markdown("<h1>Choose the prompt</h1>", elem_id="prompt-markdown")
              choice = gr.Radio(label="Choose a prompt", choices=["Details", "Conversational"], value="Details")
            with gr.Column():  # Larger scale for the right column
                gr.Markdown("<h1>Enter the query / Voice Output</h1>", elem_id="query-markdown")

                chat_input = gr.Textbox(show_copy_button=True, interactive=True, show_label=False, label="Transcription")
                chat_msg = chat_input.submit(add_message, [chatbot, chat_input], [chatbot, chat_input])
                bot_msg = chat_msg.then(bot, [chatbot, choice], [chatbot, gr.Audio(interactive=False, autoplay=True)])
                bot_msg.then(lambda: gr.Textbox(value="", interactive=True, placeholder="Enter message or upload file...", show_label=False), None, [chat_input])
                chatbot.like(print_like_dislike, None, None)
                clear_button = gr.Button("Clear")
                clear_button.click(fn=clear_textbox, inputs=None, outputs=chat_input)
        
            with gr.Column():  # Smaller scale for the left column
                gr.Markdown("<h1>Stream your Voice</h1>", elem_id="voice-markdown")

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

        with gr.Row():
            with gr.Column():
                gr.Markdown("<h1>Locate the Events</h1>", elem_id="location-markdown")
                location_output = gr.HTML()
                bot_msg.then(show_map_if_details, [chatbot, choice], [location_output, location_output])
        
        
            # with gr.Column():
            #     gr.Markdown("<h1>Listen to the audio</h1>", elem_id="audio-markdown")
            #     audio_output = gr.Audio()
            #     bot_msg_audio = bot_msg.then(lambda history: generate_audio_elevenlabs(history[-1][1]), chatbot, audio_output)
        with gr.Row():   
            
            with gr.Column():
                gr.Markdown("<h1>Local Events</h1>", elem_id="events-markdown")
                news_output = gr.HTML(value=fetch_local_events())
                     
            with gr.Column():
                gr.Markdown("<h1>Generated Image</h1>", elem_id="image-markdown")
                image_output = gr.Image(value=generate_image(hardcoded_prompt))

    setup_ui()    

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