Update app.py

app.py

@@ -890,7 +890,7 @@ from datetime import datetime
 import numpy as np
 from gtts import gTTS
 from googlemaps import Client as GoogleMapsClient
-from diffusers import StableDiffusionPipeline
+from diffusers import StableDiffusion3Pipeline
 import concurrent.futures
 from PIL import Image
 
@@ -903,16 +903,19 @@ from langchain.agents import Tool, initialize_agent
 from huggingface_hub import login
 
 import sqlite3
-import hashlib
+from passlib.hash import bcrypt
 
 # 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
@@ -931,7 +934,7 @@ 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-4')
+                        temperature=0, model='gpt-4o')
 
 conversational_memory = ConversationBufferWindowMemory(
     memory_key='chat_history',
@@ -943,6 +946,7 @@ 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():
@@ -1071,6 +1075,140 @@ def get_weather_icon(condition):
     }
     return condition_map.get(condition, "c04d")
 
+current_time_and_date = get_current_time_and_date()
+
+# 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'], '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}'
@@ -1154,6 +1292,7 @@ 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)
 
@@ -1162,8 +1301,6 @@ pipe_asr = pipeline("automatic-speech-recognition", model=model, tokenizer=proce
 
 base_audio_drive = "/data/audio"
 
-import numpy as np
-
 def transcribe_function(stream, new_chunk):
     try:
         sr, y = new_chunk[0], new_chunk[1]
@@ -1183,7 +1320,6 @@ def transcribe_function(stream, new_chunk):
     full_text = result.get("text", "")
     
     return stream, full_text, result
-    
 
 def update_map_with_response(history):
     if not history:
@@ -1199,7 +1335,7 @@ 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), ""
+        return gr.update(visible(False), "")
 
 def generate_audio_elevenlabs(text):
     XI_API_KEY = os.environ['ELEVENLABS_API']
@@ -1245,8 +1381,8 @@ def generate_image(prompt):
     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 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():
@@ -1255,197 +1391,56 @@ def update_images():
     image_3 = generate_image(hardcoded_prompt_3)
     return image_1, image_2, image_3
 
-# Initialize database and create user table if not exists
-def init_db():
-    conn = sqlite3.connect('user_data.db')
-    c = conn.cursor()
-    c.execute('''CREATE TABLE IF NOT EXISTS users
-                 (username TEXT PRIMARY KEY, password TEXT)''')
+# Database setup
+def create_database():
+    conn = sqlite3.connect('user_credentials.db')
+    cursor = conn.cursor()
+    cursor.execute('''
+        CREATE TABLE IF NOT EXISTS users (
+            id INTEGER PRIMARY KEY,
+            username TEXT UNIQUE,
+            password TEXT
+        )
+    ''')
     conn.commit()
     conn.close()
 
-init_db()
-
-def hash_password(password):
-    return hashlib.sha256(password.encode()).hexdigest()
-
 def signup_user(username, password):
-    conn = sqlite3.connect('user_data.db')
-    c = conn.cursor()
+    conn = sqlite3.connect('user_credentials.db')
+    cursor = conn.cursor()
+    hashed_password = bcrypt.hash(password)
     try:
-        c.execute("INSERT INTO users (username, password) VALUES (?, ?)", (username, hash_password(password)))
+        cursor.execute('INSERT INTO users (username, password) VALUES (?, ?)', (username, hashed_password))
         conn.commit()
-        return True
+        return "Signup successful!"
     except sqlite3.IntegrityError:
-        return False
+        return "Username already exists!"
     finally:
         conn.close()
 
 def login_user(username, password):
-    conn = sqlite3.connect('user_data.db')
-    c = conn.cursor()
-    c.execute("SELECT * FROM users WHERE username=? AND password=?", (username, hash_password(password)))
-    user = c.fetchone()
+    conn = sqlite3.connect('user_credentials.db')
+    cursor = conn.cursor()
+    cursor.execute('SELECT password FROM users WHERE username = ?', (username,))
+    result = cursor.fetchone()
     conn.close()
-    return user is not None
-
-def handle_signup(username, password):
-    if '@mangoes.ai' not in username:
-        return "Username must include '@mangoes.ai'."
-    if signup_user(username, password):
-        return "Signup successful! You can now log in."
-    else:
-        return "Signup failed! Username may already be taken."
-
-def handle_login(username, password):
-    if login_user(username, password):
+    if result and bcrypt.verify(password, result[0]):
         return "Login successful!"
     else:
-        return "Login failed! Please check your credentials."
-
-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)
-
-    # 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
+        return "Invalid username or password."
 
-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:"""
+# Initialize database
+create_database()
 
-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:"""
+def signup(username, password, password_confirmation):
+    if password != password_confirmation:
+        return "Passwords do not match."
+    return signup_user(username, password)
 
-QA_CHAIN_PROMPT_1 = PromptTemplate(input_variables=["context", "question"], template=template1)
-QA_CHAIN_PROMPT_2 = PromptTemplate(input_variables=["context", "question"], template=template2)
+def login(username, password):
+    return login_user(username, password)
 
 with gr.Blocks(theme='Pijush2023/scikit-learn-pijush') as demo:
-    with gr.Tab("Login"):
-        login_username = gr.Textbox(label="Username")
-        login_password = gr.Password(label="Password")
-        login_button = gr.Button("Login")
-        login_output = gr.Textbox(label="Login Status", interactive=False)
-        login_button.click(handle_login, inputs=[login_username, login_password], outputs=login_output)
-    
-    with gr.Tab("Signup"):
-        signup_username = gr.Textbox(label="Username")
-        signup_password = gr.Password(label="Password")
-        signup_button = gr.Button("Signup")
-        signup_output = gr.Textbox(label="Signup Status", interactive=False)
-        signup_button.click(handle_signup, inputs=[signup_username, signup_password], outputs=signup_output)
-    
     with gr.Row():
         with gr.Column():
             state = gr.State()
@@ -1462,6 +1457,7 @@ with gr.Blocks(theme='Pijush2023/scikit-learn-pijush') as demo:
             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")
 
@@ -1472,20 +1468,36 @@ with gr.Blocks(theme='Pijush2023/scikit-learn-pijush') as demo:
         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())
-        
+            news_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])
+    
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("<h2>Signup</h2>")
+            signup_username = gr.Textbox(placeholder="Username")
+            signup_password = gr.Textbox(placeholder="Password", type="password")
+            signup_password_confirmation = gr.Textbox(placeholder="Confirm Password", type="password")
+            signup_button = gr.Button("Signup")
+            signup_message = gr.Textbox(interactive=False)
+            
+            signup_button.click(fn=signup, inputs=[signup_username, signup_password, signup_password_confirmation], outputs=[signup_message])
+        
+        with gr.Column():
+            gr.Markdown("<h2>Login</h2>")
+            login_username = gr.Textbox(placeholder="Username")
+            login_password = gr.Textbox(placeholder="Password", type="password")
+            login_button = gr.Button("Login")
+            login_message = gr.Textbox(interactive=False)
+            
+            login_button.click(fn=login, inputs=[login_username, login_password], outputs=[login_message])
 
 demo.queue()
 demo.launch(share=True)
-
-
-
-
-