Upload 2 files

app.py

@@ -0,0 +1,302 @@
+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 IPython.display import Audio, display
+from google.colab import output
+from IPython.display import Javascript
+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
+
+os.environ['STABILITY_HOST'] = 'grpc.stability.ai:443'
+os.environ['STABILITY_KEY'] = 'sk-Ndzkpi6OYwM5fQgEJAVwRPbFZSMNyFk0GoZw1EvNtqVExGdi'
+stability_api = client.StabilityInference(
+    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": "e77d5416608a110ea2babd7b2e33ede48b0c4159ade5cfd5cebbc7483c513ff3"
+    }
+
+    params = {
+        "q": question,
+        "location": "Bengaluru, Karnataka, India",
+        "hl": "hi",
+        "gl": "in",
+        "google_domain": "google.co.in",
+        "api_key": "e77d5416608a110ea2babd7b2e33ede48b0c4159ade5cfd5cebbc7483c513ff3"
+    }
+
+    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 = "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"
+
+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)

requirements.txt

@@ -0,0 +1,12 @@
+stability-sdk
+pytube
+openai
+google-search-results
+accelerate
+streamlit
+wget
+streamlit-bokeh-events
+streamlit-player
+diffusers
+transformers
+scipy