Date: {date}
Location: {location}
Failed to fetch local events
" # 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 = """ #Date: {date}
Location: {location}
Failed to fetch local events
" 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 = """Date: {date}
Location: {location}
Failed to fetch local events
" # 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 = 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""" #}.png){kind=icon}
# Temperature: {temp_fahrenheit}°F
#Condition: {condition}
#Humidity: {humidity}%
#Failed to fetch local weather: {e}
" 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 = 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"""
Temperature: {temp_fahrenheit}°F
Condition: {condition}
Humidity: {humidity}%
Failed to fetch local weather: {e}
" 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 = """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}' # response = requests.get(url) # if response.status_code == 200: # results = response.json().get("news_results", []) # news_html = """ #{snippet}
#Failed to fetch local news
" 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}' response = requests.get(url) if response.status_code == 200: results = response.json().get("news_results", []) news_html = """{snippet}
Failed to fetch local news
" # 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 = 'd9MIrwLnvDeH7aZb61E9' # 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": 1.0, "similarity_boost": 0.0, "style": 0.60, # 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_1 = "Useing The top events like 'Summer Art Festival'and Date - 06/19/2024 ,Weather-Sunny Bright Day.Create Highly Visually Compelling High Resolution and High Quality Photographics Advatizement for 'Toyota'" 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 = "Create a vibrant poster of Nebraska with beautiful weather, featuring picturesque landscapes, clear skies, and the word 'Nebraska' prominently displayed." 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 # OAuth 2.0 endpoints for Google authorization_base_url = 'https://accounts.google.com/o/oauth2/auth' token_url = 'https://accounts.google.com/o/oauth2/token' scope = ['https://www.googleapis.com/auth/userinfo.profile', 'https://www.googleapis.com/auth/userinfo.email'] # Function to initiate the OAuth flow def login(): google = OAuth2Session(GOOGLE_CLIENT_ID, scope=scope, redirect_uri=REDIRECT_URI) authorization_url, state = google.authorization_url(authorization_base_url, access_type="offline", prompt="select_account") return f'Login with Google' # Function to handle the callback def oauth_callback(url): google = OAuth2Session(GOOGLE_CLIENT_ID, redirect_uri=REDIRECT_URI, scope=scope) google.fetch_token(token_url, client_secret=GOOGLE_CLIENT_SECRET, authorization_response=url) userinfo = google.get('https://www.googleapis.com/oauth2/v1/userinfo').json() return f"Logged in as: {userinfo['name']} ({userinfo['email']})" 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") gr.Markdown("Date: {date}
Location: {location}
Failed to fetch local events
" # 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 = 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""" #
# Temperature: {temp_fahrenheit}°F
#Condition: {condition}
#Humidity: {humidity}%
#Failed to fetch local weather: {e}
" # 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_news(): # api_key = os.environ['SERP_API'] # url = f'https://serpapi.com/search.json?engine=google_news&q=omaha headline&api_key={api_key}' # response = requests.get(url) # if response.status_code == 200: # results = response.json().get("news_results", []) # news_html = """ #{snippet}
#Failed to fetch local news
" # # 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 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 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 = 'd9MIrwLnvDeH7aZb61E9' # tts_url = f"https://api.elevenlabs.io/v1/text-to-speech/{VOICE_ID}/stream" # headers = { # "Accept": "application/json", # "xi-api-key": XI_API_KEY # } # data = { # "text": str(text), # "model_id": "eleven_multilingual_v2", # "voice_settings": { # "stability": 1.0, # "similarity_boost": 0.0, # "style": 0.60, # "use_speaker_boost": False # } # } # response = requests.post(tts_url, headers=headers, json=data, stream=True) # if response.ok: # with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as f: # for chunk in response.iter_content(chunk_size=1024): # 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 # import torch # from diffusers import StableDiffusion3Pipeline # 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_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 login_with_google(): # response = requests.get('http://localhost:5000/login') # return response.url # 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") # gr.Markdown("