import os import openai import wget import streamlit as st from PIL import Image from serpapi import GoogleSearch import torch from diffusers import StableDiffusionPipeline from bokeh.models.widgets import Button from bokeh.models.widgets.buttons import Button from bokeh.models import CustomJS from streamlit_bokeh_events import streamlit_bokeh_events import base64 from streamlit_player import st_player from pytube import YouTube from pytube import Search import io import warnings from PIL import Image from stability_sdk import client import stability_sdk.interfaces.gooseai.generation.generation_pb2 as generation import datetime from google.oauth2 import service_account from googleapiclient.discovery import build import wget import urllib.request import csv def save_uploadedfile(uploadedfile): with open(uploadedfile.name,"wb") as f: f.write(uploadedfile.getbuffer()) stability_api = client.StabilityInference( key=st.secrets["STABILITY_KEY"], #os.environ("STABILITY_KEY"), # key=os.environ['STABILITY_KEY'], # API Key reference. verbose=True, # Print debug messages. engine="stable-diffusion-v1-5", # Set the engine to use for generation. # Available engines: stable-diffusion-v1 stable-diffusion-v1-5 stable-diffusion-512-v2-0 stable-diffusion-768-v2-0 # stable-diffusion-512-v2-1 stable-diffusion-768-v2-1 stable-inpainting-v1-0 stable-inpainting-512-v2-0 ) header = ["sl. no.", "Input Prompt", "Output", "Date_time"] def csv_logs(mytext, result, date_time): with open("logs.csv", "r") as file: sl_no = sum(1 for _ in csv.reader(file)) with open("logs.csv", "a", newline="") as file: writer = csv.writer(file) writer.writerow([sl_no, mytext, result, date_time]) def search_internet(question): try: params = { "q": question, "location": "Bengaluru, Karnataka, India", "hl": "hi", "gl": "in", "google_domain": "google.co.in", # "api_key": "" "api_key": st.secrets["GOOGLE_API"] #os.environ("GOOGLE_API") #os.environ['GOOGLE_API'] } params = { "q": question, "location": "Bengaluru, Karnataka, India", "hl": "hi", "gl": "in", "google_domain": "google.co.in", # "api_key": "" "api_key": st.secrets["GOOGLE_API"] #os.environ("GOOGLE_API") #os.environ['GOOGLE_API'] } search = GoogleSearch(params) results = search.get_dict() organic_results = results["organic_results"] st.text("Key 0 used") snippets = "" counter = 1 for item in organic_results: snippets += str(counter) + ". " + item.get("snippet", "") + '\n' + item['about_this_result']['source']['source_info_link'] + '\n' counter += 1 # snippets response = openai.Completion.create( model="text-davinci-003", prompt=f'''following are snippets from google search with these as knowledge base only answer questions and print reference link as well followed by answer. \n\n {snippets}\n\n question-{question}\n\nAnswer-''', temperature=0.49, max_tokens=256, top_p=1, frequency_penalty=0, presence_penalty=0) now = datetime.datetime.now() date_time = now.strftime("%Y-%m-%d %H:%M:%S") string_temp = response.choices[0].text csv_logs(question, string_temp, date_time) st.write(string_temp) st.write(snippets) except: try: params = { "q": question, "location": "Bengaluru, Karnataka, India", "hl": "hi", "gl": "in", "google_domain": "google.co.in", # "api_key": "" "api_key": st.secrets["GOOGLE_API1"] #os.environ("GOOGLE_API") #os.environ['GOOGLE_API'] } params = { "q": question, "location": "Bengaluru, Karnataka, India", "hl": "hi", "gl": "in", "google_domain": "google.co.in", # "api_key": "" "api_key": st.secrets["GOOGLE_API1"] #os.environ("GOOGLE_API") #os.environ['GOOGLE_API'] } search = GoogleSearch(params) results = search.get_dict() organic_results = results["organic_results"] st.text("Key 1 used") snippets = "" counter = 1 for item in organic_results: snippets += str(counter) + ". " + item.get("snippet", "") + '\n' + item['about_this_result']['source']['source_info_link'] + '\n' counter += 1 # snippets response = openai.Completion.create( model="text-davinci-003", prompt=f'''following are snippets from google search with these as knowledge base only answer questions and print reference link as well followed by answer. \n\n {snippets}\n\n question-{question}\n\nAnswer-''', temperature=0.49, max_tokens=256, top_p=1, frequency_penalty=0, presence_penalty=0) now = datetime.datetime.now() date_time = now.strftime("%Y-%m-%d %H:%M:%S") string_temp = response.choices[0].text csv_logs(question, string_temp, date_time) st.write(string_temp) st.write(snippets) except: try: params = { "q": question, "location": "Bengaluru, Karnataka, India", "hl": "hi", "gl": "in", "google_domain": "google.co.in", # "api_key": "" "api_key": st.secrets["GOOGLE_API2"] #os.environ("GOOGLE_API") #os.environ['GOOGLE_API'] } params = { "q": question, "location": "Bengaluru, Karnataka, India", "hl": "hi", "gl": "in", "google_domain": "google.co.in", # "api_key": "" "api_key": st.secrets["GOOGLE_API2"] #os.environ("GOOGLE_API") #os.environ['GOOGLE_API'] } search = GoogleSearch(params) results = search.get_dict() organic_results = results["organic_results"] st.text("Key 2 used") snippets = "" counter = 1 for item in organic_results: snippets += str(counter) + ". " + item.get("snippet", "") + '\n' + item['about_this_result']['source']['source_info_link'] + '\n' counter += 1 # snippets response = openai.Completion.create( model="text-davinci-003", prompt=f'''following are snippets from google search with these as knowledge base only answer questions and print reference link as well followed by answer. \n\n {snippets}\n\n question-{question}\n\nAnswer-''', temperature=0.49, max_tokens=256, top_p=1, frequency_penalty=0, presence_penalty=0) now = datetime.datetime.now() date_time = now.strftime("%Y-%m-%d %H:%M:%S") string_temp = response.choices[0].text csv_logs(question, string_temp, date_time) st.write(string_temp) st.write(snippets) except: try: params = { "q": question, "location": "Bengaluru, Karnataka, India", "hl": "hi", "gl": "in", "google_domain": "google.co.in", # "api_key": "" "api_key": st.secrets["GOOGLE_API3"] #os.environ("GOOGLE_API") #os.environ['GOOGLE_API'] } params = { "q": question, "location": "Bengaluru, Karnataka, India", "hl": "hi", "gl": "in", "google_domain": "google.co.in", # "api_key": "" "api_key": st.secrets["GOOGLE_API3"] #os.environ("GOOGLE_API") #os.environ['GOOGLE_API'] } search = GoogleSearch(params) results = search.get_dict() organic_results = results["organic_results"] st.text("Key 3 used") snippets = "" counter = 1 for item in organic_results: snippets += str(counter) + ". " + item.get("snippet", "") + '\n' + item['about_this_result']['source']['source_info_link'] + '\n' counter += 1 # snippets response = openai.Completion.create( model="text-davinci-003", prompt=f'''following are snippets from google search with these as knowledge base only answer questions and print reference link as well followed by answer. \n\n {snippets}\n\n question-{question}\n\nAnswer-''', temperature=0.49, max_tokens=256, top_p=1, frequency_penalty=0, presence_penalty=0) now = datetime.datetime.now() date_time = now.strftime("%Y-%m-%d %H:%M:%S") string_temp = response.choices[0].text csv_logs(question, string_temp, date_time) st.write(string_temp) st.write(snippets) except: try: params = { "q": question, "location": "Bengaluru, Karnataka, India", "hl": "hi", "gl": "in", "google_domain": "google.co.in", # "api_key": "" "api_key": st.secrets["GOOGLE_API4"] #os.environ("GOOGLE_API") #os.environ['GOOGLE_API'] } params = { "q": question, "location": "Bengaluru, Karnataka, India", "hl": "hi", "gl": "in", "google_domain": "google.co.in", # "api_key": "" "api_key": st.secrets["GOOGLE_API4"] #os.environ("GOOGLE_API") #os.environ['GOOGLE_API'] } search = GoogleSearch(params) results = search.get_dict() organic_results = results["organic_results"] st.text("Key 4 used") snippets = "" counter = 1 for item in organic_results: snippets += str(counter) + ". " + item.get("snippet", "") + '\n' + item['about_this_result']['source']['source_info_link'] + '\n' counter += 1 # snippets response = openai.Completion.create( model="text-davinci-003", prompt=f'''following are snippets from google search with these as knowledge base only answer questions and print reference link as well followed by answer. \n\n {snippets}\n\n question-{question}\n\nAnswer-''', temperature=0.49, max_tokens=256, top_p=1, frequency_penalty=0, presence_penalty=0) now = datetime.datetime.now() date_time = now.strftime("%Y-%m-%d %H:%M:%S") string_temp = response.choices[0].text csv_logs(question, string_temp, date_time) st.write(string_temp) st.write(snippets) except: try: params = { "q": question, "location": "Bengaluru, Karnataka, India", "hl": "hi", "gl": "in", "google_domain": "google.co.in", # "api_key": "" "api_key": st.secrets["GOOGLE_API5"] #os.environ("GOOGLE_API") #os.environ['GOOGLE_API'] } params = { "q": question, "location": "Bengaluru, Karnataka, India", "hl": "hi", "gl": "in", "google_domain": "google.co.in", # "api_key": "" "api_key": st.secrets["GOOGLE_API5"] #os.environ("GOOGLE_API") #os.environ['GOOGLE_API'] } search = GoogleSearch(params) results = search.get_dict() organic_results = results["organic_results"] st.text("Key 5 used") snippets = "" counter = 1 for item in organic_results: snippets += str(counter) + ". " + item.get("snippet", "") + '\n' + item['about_this_result']['source']['source_info_link'] + '\n' counter += 1 # snippets response = openai.Completion.create( model="text-davinci-003", prompt=f'''following are snippets from google search with these as knowledge base only answer questions and print reference link as well followed by answer. \n\n {snippets}\n\n question-{question}\n\nAnswer-''', temperature=0.49, max_tokens=256, top_p=1, frequency_penalty=0, presence_penalty=0) now = datetime.datetime.now() date_time = now.strftime("%Y-%m-%d %H:%M:%S") string_temp = response.choices[0].text csv_logs(question, string_temp, date_time) st.write(string_temp) st.write(snippets) except: try: params = { "q": question, "location": "Bengaluru, Karnataka, India", "hl": "hi", "gl": "in", "google_domain": "google.co.in", # "api_key": "" "api_key": st.secrets["GOOGLE_API6"] #os.environ("GOOGLE_API") #os.environ['GOOGLE_API'] } params = { "q": question, "location": "Bengaluru, Karnataka, India", "hl": "hi", "gl": "in", "google_domain": "google.co.in", # "api_key": "" "api_key": st.secrets["GOOGLE_API6"] #os.environ("GOOGLE_API") #os.environ['GOOGLE_API'] } search = GoogleSearch(params) results = search.get_dict() organic_results = results["organic_results"] st.text("Key 6 used") snippets = "" counter = 1 for item in organic_results: snippets += str(counter) + ". " + item.get("snippet", "") + '\n' + item['about_this_result']['source']['source_info_link'] + '\n' counter += 1 # snippets response = openai.Completion.create( model="text-davinci-003", prompt=f'''following are snippets from google search with these as knowledge base only answer questions and print reference link as well followed by answer. \n\n {snippets}\n\n question-{question}\n\nAnswer-''', temperature=0.49, max_tokens=256, top_p=1, frequency_penalty=0, presence_penalty=0) now = datetime.datetime.now() date_time = now.strftime("%Y-%m-%d %H:%M:%S") string_temp = response.choices[0].text csv_logs(question, string_temp, date_time) st.write(string_temp) st.write(snippets) except: try: params = { "q": question, "location": "Bengaluru, Karnataka, India", "hl": "hi", "gl": "in", "google_domain": "google.co.in", # "api_key": "" "api_key": st.secrets["GOOGLE_API7"] #os.environ("GOOGLE_API") #os.environ['GOOGLE_API'] } params = { "q": question, "location": "Bengaluru, Karnataka, India", "hl": "hi", "gl": "in", "google_domain": "google.co.in", # "api_key": "" "api_key": st.secrets["GOOGLE_API7"] #os.environ("GOOGLE_API") #os.environ['GOOGLE_API'] } search = GoogleSearch(params) results = search.get_dict() organic_results = results["organic_results"] st.text("Key 7 used") snippets = "" counter = 1 for item in organic_results: snippets += str(counter) + ". " + item.get("snippet", "") + '\n' + item['about_this_result']['source']['source_info_link'] + '\n' counter += 1 # snippets response = openai.Completion.create( model="text-davinci-003", prompt=f'''following are snippets from google search with these as knowledge base only answer questions and print reference link as well followed by answer. \n\n {snippets}\n\n question-{question}\n\nAnswer-''', temperature=0.49, max_tokens=256, top_p=1, frequency_penalty=0, presence_penalty=0) now = datetime.datetime.now() date_time = now.strftime("%Y-%m-%d %H:%M:%S") string_temp = response.choices[0].text csv_logs(question, string_temp, date_time) st.write(string_temp) st.write(snippets) except: try: params = { "q": question, "location": "Bengaluru, Karnataka, India", "hl": "hi", "gl": "in", "google_domain": "google.co.in", # "api_key": "" "api_key": st.secrets["GOOGLE_API8"] #os.environ("GOOGLE_API") #os.environ['GOOGLE_API'] } params = { "q": question, "location": "Bengaluru, Karnataka, India", "hl": "hi", "gl": "in", "google_domain": "google.co.in", # "api_key": "" "api_key": st.secrets["GOOGLE_API8"] #os.environ("GOOGLE_API") #os.environ['GOOGLE_API'] } search = GoogleSearch(params) results = search.get_dict() organic_results = results["organic_results"] st.text("Key 8 used") snippets = "" counter = 1 for item in organic_results: snippets += str(counter) + ". " + item.get("snippet", "") + '\n' + item['about_this_result']['source']['source_info_link'] + '\n' counter += 1 # snippets response = openai.Completion.create( model="text-davinci-003", prompt=f'''following are snippets from google search with these as knowledge base only answer questions and print reference link as well followed by answer. \n\n {snippets}\n\n question-{question}\n\nAnswer-''', temperature=0.49, max_tokens=256, top_p=1, frequency_penalty=0, presence_penalty=0) now = datetime.datetime.now() date_time = now.strftime("%Y-%m-%d %H:%M:%S") string_temp = response.choices[0].text csv_logs(question, string_temp, date_time) st.write(string_temp) st.write(snippets) except: try: params = { "q": question, "location": "Bengaluru, Karnataka, India", "hl": "hi", "gl": "in", "google_domain": "google.co.in", # "api_key": "" "api_key": st.secrets["GOOGLE_API9"] #os.environ("GOOGLE_API") #os.environ['GOOGLE_API'] } params = { "q": question, "location": "Bengaluru, Karnataka, India", "hl": "hi", "gl": "in", "google_domain": "google.co.in", # "api_key": "" "api_key": st.secrets["GOOGLE_API9"] #os.environ("GOOGLE_API") #os.environ['GOOGLE_API'] } search = GoogleSearch(params) results = search.get_dict() organic_results = results["organic_results"] st.text("Key 9 used") snippets = "" counter = 1 for item in organic_results: snippets += str(counter) + ". " + item.get("snippet", "") + '\n' + item['about_this_result']['source']['source_info_link'] + '\n' counter += 1 # snippets response = openai.Completion.create( model="text-davinci-003", prompt=f'''following are snippets from google search with these as knowledge base only answer questions and print reference link as well followed by answer. \n\n {snippets}\n\n question-{question}\n\nAnswer-''', temperature=0.49, max_tokens=256, top_p=1, frequency_penalty=0, presence_penalty=0) now = datetime.datetime.now() date_time = now.strftime("%Y-%m-%d %H:%M:%S") string_temp = response.choices[0].text csv_logs(question, string_temp, date_time) st.write(string_temp) st.write(snippets) except: try: params = { "q": question, "location": "Bengaluru, Karnataka, India", "hl": "hi", "gl": "in", "google_domain": "google.co.in", # "api_key": "" "api_key": st.secrets["GOOGLE_API10"] #os.environ("GOOGLE_API") #os.environ['GOOGLE_API'] } params = { "q": question, "location": "Bengaluru, Karnataka, India", "hl": "hi", "gl": "in", "google_domain": "google.co.in", # "api_key": "" "api_key": st.secrets["GOOGLE_API10"] #os.environ("GOOGLE_API") #os.environ['GOOGLE_API'] } search = GoogleSearch(params) results = search.get_dict() organic_results = results["organic_results"] st.text("Key 10 used") snippets = "" counter = 1 for item in organic_results: snippets += str(counter) + ". " + item.get("snippet", "") + '\n' + item['about_this_result']['source']['source_info_link'] + '\n' counter += 1 # snippets response = openai.Completion.create( model="text-davinci-003", prompt=f'''following are snippets from google search with these as knowledge base only answer questions and print reference link as well followed by answer. \n\n {snippets}\n\n question-{question}\n\nAnswer-''', temperature=0.49, max_tokens=256, top_p=1, frequency_penalty=0, presence_penalty=0) now = datetime.datetime.now() date_time = now.strftime("%Y-%m-%d %H:%M:%S") string_temp = response.choices[0].text csv_logs(question, string_temp, date_time) st.write(string_temp) st.write(snippets) except: pass openai.api_key = st.secrets["OPENAI_KEY"] #os.environ("OPENAI_KEY") #os.environ['OPENAI_KEY'] # date_time = str(datetime.now()) openai.api_key = st.secrets["OPENAI_KEY"] def openai_response(PROMPT): response = openai.Image.create( prompt=PROMPT, n=1, size="256x256", ) return response["data"][0]["url"] st.title("Hi! :red[HyperBot] here!!πŸ€–β­οΈ") st.title("Go on ask me anything!!") st.write(''' ⭐️ *HyperBot is your virtual assistant powered by Whisper / chatgpt / internet / Dall-E / OpenAI embeddings - the perfect companion for you. With HyperBot, you can ask anything you ask internet everyday . Get answers to questions about the weather, stocks πŸ“ˆ, newsπŸ“°, and more! Plus, you can also generate πŸ–ŒοΈ paintings, drawings, abstract art 🎨, play music 🎡 or videos, create tweets 🐦 and posts πŸ“, and compose emails πŸ“§ - all with the help of HyperBot!* πŸ€– ✨ ''') st.text('''You can ask me: 1. All the things you ask ChatGPT. 2. To generate paintings, drawings, abstract art. 3. Music or Videos 4. Weather 5. Stocks 6. Current Affairs and News. 7. Create or compose tweets or Linkedin posts or email.''') Input_type = st.radio( "**Input type:**", ('TEXT', 'SPEECH') ) if Input_type == 'TEXT': mytext = st.text_input('**Go on! Ask me anything:**') if st.button("SUBMIT"): question=mytext response = openai.Completion.create( model="text-davinci-003", prompt=f'''Your name is HyperBot and knowledge cutoff date is 2021-09, and you are not aware of any events after that time. if the Answer to following questions is not from your knowledge base or in case of queries like date, time, weather updates / stock updates / current affairs / news or people which requires you to have internet connection then print i don't have access to internet to answer your question, if question is related to image or painting or drawing or diagram generation then print ipython type output function gen_draw("detailed prompt of image to be generated") if the question is related to playing a song or video or music of a singer then print ipython type output function vid_tube("relevent search query") if the question is related to operating home appliances then print ipython type output function home_app(" action(ON/Off),appliance(TV,Geaser,Fridge,Lights,fans,AC)") . if question is realted to sending mail or sms then print ipython type output function messenger_app(" message of us ,messenger(email,sms)") \nQuestion-{question} \nAnswer -''', temperature=0.49, max_tokens=256, top_p=1, frequency_penalty=0, presence_penalty=0 ) string_temp=response.choices[0].text if ("gen_draw" in string_temp): try: try: wget.download(openai_response(prompt)) img2 = Image.open(wget.download(openai_response(prompt))) img2.show() rx = 'Image returned' now = datetime.datetime.now() date_time = now.strftime("%Y-%m-%d %H:%M:%S") csv_logs(mytext, rx, date_time) except: urllib.request.urlretrieve(openai_response(prompt),"img_ret.png") img = Image.open("img_ret.png") img.show() rx = 'Image returned' now = datetime.datetime.now() date_time = now.strftime("%Y-%m-%d %H:%M:%S") csv_logs(mytext, rx, date_time) except: # Set up our initial generation parameters. answers = stability_api.generate( prompt = mytext, seed=992446758, # If a seed is provided, the resulting generated image will be deterministic. # What this means is that as long as all generation parameters remain the same, you can always recall the same image simply by generating it again. # Note: This isn't quite the case for Clip Guided generations, which we'll tackle in a future example notebook. steps=30, # Amount of inference steps performed on image generation. Defaults to 30. cfg_scale=8.0, # Influences how strongly your generation is guided to match your prompt. # Setting this value higher increases the strength in which it tries to match your prompt. # Defaults to 7.0 if not specified. width=512, # Generation width, defaults to 512 if not included. height=512, # Generation height, defaults to 512 if not included. samples=4, # Number of images to generate, defaults to 1 if not included. sampler=generation.SAMPLER_K_DPMPP_2M # Choose which sampler we want to denoise our generation with. # Defaults to k_dpmpp_2m if not specified. Clip Guidance only supports ancestral samplers. # (Available Samplers: ddim, plms, k_euler, k_euler_ancestral, k_heun, k_dpm_2, k_dpm_2_ancestral, k_dpmpp_2s_ancestral, k_lms, k_dpmpp_2m) ) for resp in answers: for artifact in resp.artifacts: if artifact.finish_reason == generation.FILTER: warnings.warn( "Your request activated the API's safety filters and could not be processed." "Please modify the prompt and try again.") st.warning("Issue with image generation") if artifact.type == generation.ARTIFACT_IMAGE: img = Image.open(io.BytesIO(artifact.binary)) st.image(img) img.save(str(artifact.seed)+ ".png") # Save our generated images with their seed number as the filename. rx = 'Image returned' # g_sheet_log(mytext, rx) now = datetime.datetime.now() date_time = now.strftime("%Y-%m-%d %H:%M:%S") csv_logs(mytext, rx, date_time) elif ("vid_tube" in string_temp): s = Search(mytext) search_res = s.results first_vid = search_res[0] print(first_vid) string = str(first_vid) video_id = string[string.index('=') + 1:-1] # print(video_id) YoutubeURL = "https://www.youtube.com/watch?v=" OurURL = YoutubeURL + video_id st.write(OurURL) st_player(OurURL) now = datetime.datetime.now() date_time = now.strftime("%Y-%m-%d %H:%M:%S") ry = 'Youtube link and video returned' # g_sheet_log(mytext, ry) csv_logs(mytext, ry, date_time) elif ("don't" in string_temp or "internet" in string_temp): st.write('searching internet ') search_internet(question) # rz = 'Internet result returned' # g_sheet_log(mytext, string_temp) # csv_logs(mytext, rz, date_time) else: st.write(string_temp) # g_sheet_log(mytext, string_temp) now = datetime.datetime.now() date_time = now.strftime("%Y-%m-%d %H:%M:%S") csv_logs(mytext, string_temp, date_time) elif Input_type == 'SPEECH': option_speech = st.selectbox( 'Choose from below: (Options for Transcription)', ('Use Microphone', 'OpenAI Whisper (Upload audio file)') ) if option_speech == 'Use Microphone': stt_button = Button(label="Speak", width=100) stt_button.js_on_event("button_click", CustomJS(code=""" var recognition = new webkitSpeechRecognition(); recognition.continuous = true; recognition.interimResults = true; recognition.onresult = function (e) { var value = ""; for (var i = e.resultIndex; i < e.results.length; ++i) { if (e.results[i].isFinal) { value += e.results[i][0].transcript; } } if ( value != "") { document.dispatchEvent(new CustomEvent("GET_TEXT", {detail: value})); } } recognition.start(); """)) result = streamlit_bokeh_events( stt_button, events="GET_TEXT", key="listen", refresh_on_update=False, override_height=75, debounce_time=0) if result: if "GET_TEXT" in result: question = result.get("GET_TEXT") st.text(question) response = openai.Completion.create( model="text-davinci-003", prompt=f'''Your name is HyperBot and knowledge cutoff date is 2021-09, and you are not aware of any events after that time. if the Answer to following questions is not from your knowledge base or in case of queries like date, time, weather updates / stock updates / current affairs / news or people which requires you to have internet connection then print i don't have access to internet to answer your question, if question is related to image or painting or drawing or diagram generation then print ipython type output function gen_draw("detailed prompt of image to be generated") if the question is related to playing a song or video or music of a singer then print ipython type output function vid_tube("relevent search query") if the question is related to operating home appliances then print ipython type output function home_app(" action(ON/Off),appliance(TV,Geaser,Fridge,Lights,fans,AC)") . if question is realted to sending mail or sms then print ipython type output function messenger_app(" message of us ,messenger(email,sms)") \nQuestion-{question} \nAnswer -''', temperature=0.49, max_tokens=256, top_p=1, frequency_penalty=0, presence_penalty=0 ) string_temp=response.choices[0].text if ("gen_draw" in string_temp): try: try: wget.download(openai_response(prompt)) img2 = Image.open(wget.download(openai_response(prompt))) img2.show() rx = 'Image returned' now = datetime.datetime.now() date_time = now.strftime("%Y-%m-%d %H:%M:%S") csv_logs(question, rx, date_time) except: urllib.request.urlretrieve(openai_response(prompt),"img_ret.png") img = Image.open("img_ret.png") img.show() rx = 'Image returned' now = datetime.datetime.now() date_time = now.strftime("%Y-%m-%d %H:%M:%S") csv_logs(question, rx, date_time) except: # Set up our initial generation parameters. answers = stability_api.generate( prompt = mytext, seed=992446758, # If a seed is provided, the resulting generated image will be deterministic. # What this means is that as long as all generation parameters remain the same, you can always recall the same image simply by generating it again. # Note: This isn't quite the case for Clip Guided generations, which we'll tackle in a future example notebook. steps=30, # Amount of inference steps performed on image generation. Defaults to 30. cfg_scale=8.0, # Influences how strongly your generation is guided to match your prompt. # Setting this value higher increases the strength in which it tries to match your prompt. # Defaults to 7.0 if not specified. width=512, # Generation width, defaults to 512 if not included. height=512, # Generation height, defaults to 512 if not included. samples=4, # Number of images to generate, defaults to 1 if not included. sampler=generation.SAMPLER_K_DPMPP_2M # Choose which sampler we want to denoise our generation with. # Defaults to k_dpmpp_2m if not specified. Clip Guidance only supports ancestral samplers. # (Available Samplers: ddim, plms, k_euler, k_euler_ancestral, k_heun, k_dpm_2, k_dpm_2_ancestral, k_dpmpp_2s_ancestral, k_lms, k_dpmpp_2m) ) for resp in answers: for artifact in resp.artifacts: if artifact.finish_reason == generation.FILTER: warnings.warn( "Your request activated the API's safety filters and could not be processed." "Please modify the prompt and try again.") if artifact.type == generation.ARTIFACT_IMAGE: img = Image.open(io.BytesIO(artifact.binary)) st.image(img) img.save(str(artifact.seed)+ ".png") # Save our generated images with their seed number as the filename. rx = 'Image returned' # g_sheet_log(mytext, rx) csv_logs(question, rx, date_time) elif ("vid_tube" in string_temp): s = Search(question) search_res = s.results first_vid = search_res[0] print(first_vid) string = str(first_vid) video_id = string[string.index('=') + 1:-1] # print(video_id) YoutubeURL = "https://www.youtube.com/watch?v=" OurURL = YoutubeURL + video_id st.write(OurURL) st_player(OurURL) now = datetime.datetime.now() date_time = now.strftime("%Y-%m-%d %H:%M:%S") ry = 'Youtube link and video returned' # g_sheet_log(mytext, ry) csv_logs(question, ry, date_time) elif ("don't" in string_temp or "internet" in string_temp ): st.write('*searching internet*') search_internet(question) else: st.write(string_temp) now = datetime.datetime.now() date_time = now.strftime("%Y-%m-%d %H:%M:%S") csv_logs(question, string_temp, date_time) elif option_speech == 'OpenAI Whisper (Upload audio file)': audio_file = st.file_uploader("Upload Audio file",type=['wav', 'mp3']) if audio_file is not None: # file = open(audio_file, "rb") st.audio(audio_file) transcription = openai.Audio.transcribe("whisper-1", audio_file) st.write(transcription["text"]) result = transcription["text"] question = result response = openai.Completion.create( model="text-davinci-003", prompt=f'''Your name is HyperBot and knowledge cutoff date is 2021-09, and you are not aware of any events after that time. if the Answer to following questions is not from your knowledge base or in case of queries like date, time, weather updates / stock updates / current affairs / news or people which requires you to have internet connection then print i don't have access to internet to answer your question, if question is related to image or painting or drawing or diagram generation then print ipython type output function gen_draw("detailed prompt of image to be generated") if the question is related to playing a song or video or music of a singer then print ipython type output function vid_tube("relevent search query") if the question is related to operating home appliances then print ipython type output function home_app(" action(ON/Off),appliance(TV,Geaser,Fridge,Lights,fans,AC)") . if question is realted to sending mail or sms then print ipython type output function messenger_app(" message of us ,messenger(email,sms)") \nQuestion-{question} \nAnswer -''', temperature=0.49, max_tokens=256, top_p=1, frequency_penalty=0, presence_penalty=0 ) string_temp=response.choices[0].text if ("gen_draw" in string_temp): try: try: wget.download(openai_response(prompt)) img2 = Image.open(wget.download(openai_response(prompt))) img2.show() rx = 'Image returned' now = datetime.datetime.now() date_time = now.strftime("%Y-%m-%d %H:%M:%S") csv_logs(question, rx, date_time) except: urllib.request.urlretrieve(openai_response(prompt),"img_ret.png") img = Image.open("img_ret.png") img.show() rx = 'Image returned' now = datetime.datetime.now() date_time = now.strftime("%Y-%m-%d %H:%M:%S") csv_logs(question, rx, date_time) except: # Set up our initial generation parameters. answers = stability_api.generate( prompt = mytext, seed=992446758, # If a seed is provided, the resulting generated image will be deterministic. # What this means is that as long as all generation parameters remain the same, you can always recall the same image simply by generating it again. # Note: This isn't quite the case for Clip Guided generations, which we'll tackle in a future example notebook. steps=30, # Amount of inference steps performed on image generation. Defaults to 30. cfg_scale=8.0, # Influences how strongly your generation is guided to match your prompt. # Setting this value higher increases the strength in which it tries to match your prompt. # Defaults to 7.0 if not specified. width=512, # Generation width, defaults to 512 if not included. height=512, # Generation height, defaults to 512 if not included. samples=4, # Number of images to generate, defaults to 1 if not included. sampler=generation.SAMPLER_K_DPMPP_2M # Choose which sampler we want to denoise our generation with. # Defaults to k_dpmpp_2m if not specified. Clip Guidance only supports ancestral samplers. # (Available Samplers: ddim, plms, k_euler, k_euler_ancestral, k_heun, k_dpm_2, k_dpm_2_ancestral, k_dpmpp_2s_ancestral, k_lms, k_dpmpp_2m) ) for resp in answers: for artifact in resp.artifacts: if artifact.finish_reason == generation.FILTER: warnings.warn( "Your request activated the API's safety filters and could not be processed." "Please modify the prompt and try again.") if artifact.type == generation.ARTIFACT_IMAGE: img = Image.open(io.BytesIO(artifact.binary)) st.image(img) img.save(str(artifact.seed)+ ".png") # Save our generated images with their seed number as the filename. rx = 'Image returned' # g_sheet_log(mytext, rx) csv_logs(question, rx, date_time) elif ("vid_tube" in string_temp): s = Search(question) search_res = s.results first_vid = search_res[0] print(first_vid) string = str(first_vid) video_id = string[string.index('=') + 1:-1] # print(video_id) YoutubeURL = "https://www.youtube.com/watch?v=" OurURL = YoutubeURL + video_id st.write(OurURL) st_player(OurURL) now = datetime.datetime.now() date_time = now.strftime("%Y-%m-%d %H:%M:%S") ry = 'Youtube link and video returned' # g_sheet_log(mytext, ry) csv_logs(question, ry, date_time) elif ("don't" in string_temp or "internet" in string_temp ): st.write('*searching internet*') search_internet(question) else: st.write(string_temp) now = datetime.datetime.now() date_time = now.strftime("%Y-%m-%d %H:%M:%S") csv_logs(question, string_temp, date_time) else: pass