Spaces:
Running
Running
| 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 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 | |
| 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 | |
| ) | |
| def search_internet(question): | |
| 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"] | |
| 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) | |
| string_temp = response.choices[0].text | |
| st.write(string_temp) | |
| st.write(snippets) | |
| # openai.api_key = "" | |
| openai.api_key = st.secrets["OPENAI_KEY"] #os.environ("OPENAI_KEY") #os.environ['OPENAI_KEY'] | |
| def openai_response(PROMPT): | |
| response = openai.Image.create( | |
| prompt=PROMPT, | |
| n=1, | |
| size="256x256", | |
| ) | |
| return response["data"][0]["url"] | |
| #page_bg_img = """ | |
| #<style> | |
| #[data-testid="stAppViewContainer"] { | |
| #background-color: #ffffff; | |
| #opacity: 0.8; | |
| #background-image: repeating-radial-gradient( circle at 0 0, transparent 0, #ffffff 40px ), repeating-linear-gradient( #55a6f655, #55a6f6 ); | |
| #} | |
| #</style> | |
| #""" | |
| #st.markdown(page_bg_img, unsafe_allow_html=True) | |
| st.title("Welcome to :red[_HyperChat_]!!🤖") | |
| st.title("How can I help?") | |
| Input_type = st.radio( | |
| "**Input type:**", | |
| ('TEXT', 'SPEECH') | |
| ) | |
| if Input_type == 'TEXT': | |
| #page_bg_img2 = """ | |
| #<style> | |
| #[data-testid="stAppViewContainer"] { | |
| #background-color: #e5e5f7; | |
| #opacity: 0.8; | |
| #background-size: 20px 20px; | |
| #background-image: repeating-linear-gradient(0deg, #32d947, #32d947 1px, #e5e5f7 1px, #e5e5f7); | |
| #} | |
| #</style> | |
| #""" | |
| #st.markdown(page_bg_img, unsafe_allow_html=True) | |
| st.write('**You are now in Text input mode**') | |
| 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 alexa and knowledge cutoff date is 2021-09, and it is 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 weather | |
| updates / stock updates / current news Etc 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 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: | |
| # 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=1, # 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) | |
| ) | |
| # Set up our warning to print to the console if the adult content classifier is tripped. | |
| # If adult content classifier is not tripped, save generated images. | |
| 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. | |
| except: | |
| st.write('image is being generated please wait...') | |
| def extract_image_description(input_string): | |
| return input_string.split('gen_draw("')[1].split('")')[0] | |
| prompt=extract_image_description(string_temp) | |
| # model_id = "CompVis/stable-diffusion-v1-4" | |
| model_id='runwayml/stable-diffusion-v1-5' | |
| device = "cuda" | |
| pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) | |
| pipe = pipe.to(device) | |
| # prompt = "a photo of an astronaut riding a horse on mars" | |
| image = pipe(prompt).images[0] | |
| image.save("astronaut_rides_horse.png") | |
| st.image(image) | |
| # image | |
| 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) | |
| elif ("don't" in string_temp or "internet" in string_temp ): | |
| st.write('searching internet ') | |
| search_internet(question) | |
| else: | |
| st.write(string_temp) | |
| elif Input_type == 'SPEECH': | |
| 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: | |
| st.write(result.get("GET_TEXT")) | |
| question = result.get("GET_TEXT") | |
| response = openai.Completion.create( | |
| model="text-davinci-003", | |
| prompt=f'''Your knowledge cutoff is 2021-09, and it is 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 weather | |
| updates / stock updates / current news Etc 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 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") | |
| \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): | |
| st.write('*image is being generated please wait..* ') | |
| def extract_image_description(input_string): | |
| return input_string.split('gen_draw("')[1].split('")')[0] | |
| prompt=extract_image_description(string_temp) | |
| # model_id = "CompVis/stable-diffusion-v1-4" | |
| model_id='runwayml/stable-diffusion-v1-5' | |
| device = "cuda" | |
| pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) | |
| pipe = pipe.to(device) | |
| # prompt = "a photo of an astronaut riding a horse on mars" | |
| image = pipe(prompt).images[0] | |
| image.save("astronaut_rides_horse.png") | |
| st.image(image) | |
| # image | |
| 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) | |
| elif ("don't" in string_temp or "internet" in string_temp ): | |
| st.write('*searching internet*') | |
| search_internet(question) | |
| else: | |
| st.write(string_temp) | |