Update app.py

app.py

@@ -882,17 +882,6 @@
 
 
 
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 import os
 import re
 import time
@@ -903,7 +892,6 @@ import gradio as gr
 import tempfile
 import torch
 import sqlite3
-import hashlib
 from datetime import datetime
 import numpy as np
 from gtts import gTTS
@@ -911,7 +899,8 @@ from googlemaps import Client as GoogleMapsClient
 from diffusers import StableDiffusion3Pipeline
 import concurrent.futures
 from PIL import Image
-
+from hashlib import sha256
+from transformers import pipeline, AutoModelForSpeechSeq2Seq, AutoProcessor
 from langchain_openai import OpenAIEmbeddings, ChatOpenAI
 from langchain_pinecone import PineconeVectorStore
 from langchain.prompts import PromptTemplate
@@ -920,61 +909,63 @@ from langchain.chains.conversation.memory import ConversationBufferWindowMemory
 from langchain.agents import Tool, initialize_agent
 from huggingface_hub import login
 
-# Create a new SQLite database (or connect to an existing one)
-conn = sqlite3.connect('users.db')
-cursor = conn.cursor()
+# Initialize database
+def init_db():
+    conn = sqlite3.connect("user_data.db")
+    cursor = conn.cursor()
+    cursor.execute('''
+        CREATE TABLE IF NOT EXISTS users (
+            id INTEGER PRIMARY KEY,
+            username TEXT UNIQUE NOT NULL,
+            password TEXT NOT NULL,
+            created_at TEXT NOT NULL
+        )
+    ''')
+    conn.commit()
+    conn.close()
 
-# Create a table for storing user credentials
-cursor.execute('''
-CREATE TABLE IF NOT EXISTS users (
-    id INTEGER PRIMARY KEY AUTOINCREMENT,
-    username TEXT NOT NULL UNIQUE,
-    password TEXT NOT NULL
-)
-''')
-conn.commit()
-conn.close()
+init_db()
 
-# Helper functions for user authentication
 def hash_password(password):
-    return hashlib.sha256(password.encode()).hexdigest()
+    return sha256(password.encode()).hexdigest()
+
+def verify_password(stored_password, provided_password):
+    return stored_password == sha256(provided_password.encode()).hexdigest()
 
-def register_user(username, password):
-    conn = sqlite3.connect('users.db')
+def create_account(username, password):
+    conn = sqlite3.connect("user_data.db")
     cursor = conn.cursor()
     try:
-        cursor.execute('INSERT INTO users (username, password) VALUES (?, ?)', (username, hash_password(password)))
+        cursor.execute('''
+            INSERT INTO users (username, password, created_at) VALUES (?, ?, ?)
+        ''', (username, hash_password(password), datetime.now().strftime("%Y-%m-%d %H:%M:%S")))
         conn.commit()
-        return True, "User registered successfully!"
+        conn.close()
+        return "Account created successfully!"
     except sqlite3.IntegrityError:
-        return False, "Username already exists!"
-    finally:
         conn.close()
+        return "Username already exists!"
 
-def authenticate_user(username, password):
-    conn = sqlite3.connect('users.db')
+def login_user(username, password):
+    conn = sqlite3.connect("user_data.db")
     cursor = conn.cursor()
-    cursor.execute('SELECT password FROM users WHERE username = ?', (username,))
-    record = cursor.fetchone()
+    cursor.execute('''
+        SELECT password FROM users WHERE username = ?
+    ''', (username,))
+    stored_password = cursor.fetchone()
     conn.close()
-    if record and record[0] == hash_password(password):
-        return True, "Login successful!"
+    if stored_password and verify_password(stored_password[0], password):
+        return True
     else:
-        return False, "Invalid username or password!"
+        return False
 
 # 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)
 
@@ -1003,13 +994,128 @@ 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()
 
+template1 = """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 20th june 2024, 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 = """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 20th june 2024, 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)
+
+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
+
+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
+
+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)
+    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] = ""
+    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)
+            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_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", [])
@@ -1054,18 +1160,14 @@ def fetch_local_weather():
         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>
@@ -1132,138 +1234,6 @@ def get_weather_icon(condition):
     }
     return condition_map.get(condition, "c04d")
 
-# Define prompt templates
-template1 = """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 20th june 2024, 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 = """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 20th june 2024, 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=omaha headline&api_key={api_key}'
@@ -1338,44 +1308,29 @@ def fetch_local_news():
     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):
@@ -1386,7 +1341,7 @@ def update_map_with_response(history):
     return generate_map(addresses)
 
 def clear_textbox():
-    return "" 
+    return ""
 
 def show_map_if_details(history, choice):
     if choice in ["Details", "Conversational"]:
@@ -1396,7 +1351,7 @@ def show_map_if_details(history, choice):
 
 def generate_audio_elevenlabs(text):
     XI_API_KEY = os.environ['ELEVENLABS_API']
-    VOICE_ID = 'd9MIrwLnvDeH7aZb61E9'  # Replace with your voice ID
+    VOICE_ID = 'd9MIrwLnvDeH7aZb61E9'
     tts_url = f"https://api.elevenlabs.io/v1/text-to-speech/{VOICE_ID}/stream"
     headers = {
         "Accept": "application/json",
@@ -1408,7 +1363,7 @@ def generate_audio_elevenlabs(text):
         "voice_settings": {
             "stability": 1.0,
             "similarity_boost": 0.0,
-            "style": 0.60,  # Adjust style for more romantic tone
+            "style": 0.60,
             "use_speaker_boost": False
         }
     }
@@ -1424,7 +1379,6 @@ def generate_audio_elevenlabs(text):
         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")
 
@@ -1437,9 +1391,8 @@ def generate_image(prompt):
     ).images[0]
     return image
 
-# Hardcoded prompt for image generation
-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_1="Give a high quality photograph of a great looking red 2026 toyota coupe against a skyline setting in th 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():
@@ -1448,61 +1401,39 @@ def update_images():
     image_3 = generate_image(hardcoded_prompt_3)
     return image_1, image_2, image_3
 
-def show_login_screen():
-    return gr.update(visible=True), gr.update(visible=False), gr.update(visible=False)
-
-def show_main_screen():
-    return gr.update(visible=False), gr.update(visible=True), gr.update(visible=True)
+authenticated = False
 
-def login(username, password):
-    success, message = authenticate_user(username, password)
-    if success:
-        return show_main_screen()
+def check_login(username, password):
+    global authenticated
+    if login_user(username, password):
+        authenticated = True
+        return "Login successful! Redirecting...", gr.update(visible=False), gr.update(visible=True)
     else:
-        return gr.update(visible=True, value=message), gr.update(visible=False), gr.update(visible=False)
+        return "Invalid username or password.", gr.update(visible=True), gr.update(visible=False)
 
-def create_account(username, password):
-    success, message = register_user(username, password)
-    if success:
-        return gr.update(visible=True, value=message), gr.update(visible(False), gr.update(visible=False))
-    else:
-        return gr.update(visible(True, value=message), gr.update(visible=False), gr.update(visible(False)))
-
-with gr.Blocks(theme='Pijush2023/scikit-learn-pijush') as demo:
-    state = gr.State()
-    
-    # Login screen
-    with gr.Row(visible=True) as login_screen:
+with gr.Blocks() as demo:
+    with gr.Row(visible=not authenticated) as login_row:
         with gr.Column():
-            gr.Markdown("<h1>Login</h1>")
-            login_username = gr.Textbox(label="Username")
-            login_password = gr.Textbox(label="Password", type="password")
+            gr.Markdown("### Login")
+            username = gr.Textbox(label="Username")
+            password = gr.Textbox(label="Password", type="password")
             login_button = gr.Button("Login")
-            login_message = gr.Textbox(visible=False)
-            login_button.click(login, [login_username, login_password], [login_message, login_screen, state])
-            create_account_button = gr.Button("Create Account")
-            create_account_button.click(lambda: (gr.update(visible=False), gr.update(visible=True)), None, [login_screen, create_account_screen])
-    
-    # Create account screen
-    with gr.Row(visible=False) as create_account_screen:
+            login_output = gr.Markdown()
+            login_button.click(fn=check_login, inputs=[username, password], outputs=[login_output, login_row, demo])
+
         with gr.Column():
-            gr.Markdown("<h1>Create Account</h1>")
-            create_username = gr.Textbox(label="Username")
-            create_password = gr.Textbox(label="Password", type="password")
+            gr.Markdown("### Create Account")
+            username_create = gr.Textbox(label="Username")
+            password_create = gr.Textbox(label="Password", type="password")
             create_button = gr.Button("Create Account")
-            create_message = gr.Textbox(visible=False)
-            create_button.click(create_account, [create_username, create_password], [create_message, create_account_screen, state])
-            back_to_login_button = gr.Button("Back to Login")
-            back_to_login_button.click(lambda: (gr.update(visible=True), gr.update(visible(False))), None, [login_screen, create_account_screen])
-    
-    # Main application screen (initially hidden)
-    with gr.Row(visible=False) as main_screen:
+            create_output = gr.Markdown()
+            create_button.click(fn=create_account, inputs=[username_create, password_create], outputs=create_output)
+
+    with gr.Row(visible=authenticated) as main_interface:
         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")
-            
             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 !!!")
             chat_msg = chat_input.submit(add_message, [chatbot, chat_input], [chatbot, chat_input])
@@ -1511,26 +1442,21 @@ with gr.Blocks(theme='Pijush2023/scikit-learn-pijush') as demo:
             chatbot.like(print_like_dislike, None, None)
             clear_button = gr.Button("Clear")
             clear_button.click(fn=clear_textbox, inputs=None, outputs=chat_input)
-           
-            
             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")
-
             gr.Markdown("<h1 style='color: red;'>Map</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():
             weather_output = gr.HTML(value=fetch_local_weather())
             news_output = gr.HTML(value=fetch_local_news())
-            news_output = gr.HTML(value=fetch_local_events())
-            
+            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])
 
@@ -1538,3 +1464,12 @@ demo.queue()
 demo.launch(share=True)
 
 
+
+
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+
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+