Update app.py

app.py

@@ -24,48 +24,12 @@ from google.oauth2 import service_account
 from googleapiclient.discovery import build
 import wget
 import urllib.request
-import sqlite3
-import pandas as pd
-import pandasql as ps
-# import sounddevice as sd
-# import soundfile as sf
 
-def clean(value):
-    val = value.replace("'",'').replace("[",'').replace("]",'')
-    return val
 
 def save_uploadedfile(uploadedfile):
     with open(uploadedfile.name,"wb") as f:
         f.write(uploadedfile.getbuffer())
 
-def gpt3(texts):
-    # openai.api_key = os.environ["Secret"]
-    openai.api_key = st.secrets['OPENAI_KEY'] #'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'#'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' #'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'
-    response = openai.Completion.create(
-    engine="text-davinci-003",
-    prompt= texts,
-        temperature=temp,
-        max_tokens=750,
-        top_p=1,
-        frequency_penalty=0.0,          
-        presence_penalty=0.0,
-        stop = (";", "/*", "</code>"))
-    x = response.choices[0].text 
-    return x
-
-def warning(sqlOutput):
-    dl = []
-    lst = ['DELETE','DROP','TRUNCATE','MERGE','ALTER','UPDATE','INSERT']
-    op2 = " ".join(sqlOutput.split())
-    op3 = op2.split(' ')
-    op4 = list(map(lambda x: x.upper(), op3))
-    for i in op4:
-        if i in lst: 
-            dl.append(i)
-    for i in dl:
-        st.warning("This query will " + i + " the data ",icon="⚠️")
-
-
 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.
@@ -253,47 +217,6 @@ def g_sheet_log(myinput, output):
   ).execute()
 
 openai.api_key = st.secrets["OPENAI_KEY"]
-# duration = 5
-# fs = 44100
-# channels = 1
-# filename = "output.wav"
-
-# def record_audio():
-#     myrecording = sd.rec(int(duration * fs), samplerate=fs, channels=channels)
-#     sd.wait()
-#     sf.write(filename, myrecording, fs)
-#     return filename
-    # p = pyaudio.PyAudio()
-
-    # # Open the microphone stream
-    # stream = p.open(format=FORMAT,
-    #                 channels=CHANNELS,
-    #                 rate=RATE,
-    #                 input=True,
-    #                 frames_per_buffer=CHUNK)
-
-    # # Record the audio
-    # frames = []
-    # for i in range(0, int(RATE / CHUNK * RECORD_SECONDS)):
-    #     data = stream.read(CHUNK)
-    #     frames.append(data)
-
-    # # Close the microphone stream
-    # stream.stop_stream()
-    # stream.close()
-    # p.terminate()
-
-    # # Save the recorded audio to a WAV file
-    # wf = wave.open("output.mp3", "wb")
-    # wf.setnchannels(CHANNELS)
-    # wf.setsampwidth(p.get_sample_size(FORMAT))
-    # wf.setframerate(RATE)
-    # wf.writeframes(b"".join(frames))
-    # wf.close()
-
-    # # Return the path to the recorded audio file
-    # return "output.mp3"
-
 
 def openai_response(PROMPT):
     response = openai.Image.create(
@@ -306,392 +229,186 @@ def openai_response(PROMPT):
 st.title("Hi! :red[HyperBot] here!!🤖⭐️")
 st.title("Go on ask me anything!!")
 
-st.text('''
-⭐️ 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 📈, news📰, and more! Plus, you can also generate 🖌️ paintings, 
-drawings, abstract art 🎨, play music 🎵 or videos, create tweets 🐦 and posts 📝, and compose emails 📧 - 
-all with the help of HyperBot! 🤖 ✨
+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 📈, news📰, and more! Plus, you can also generate 🖌️ 
+paintings, drawings, abstract art 🎨, play music 🎵 or videos, 
+create tweets 🐦 and posts 📝, and compose emails 📧 - all with 
+the help of HyperBot! 🤖 ✨
 ''')
 
-option_ = ['Ask me anything!😊','Ask me anything (CSV file data)!📊']
-Usage = st.selectbox('Select an option:', option_)
-
-if Usage == 'Ask me anything (CSV file data)!📊':
-    st.text('''
-    You can use your own custom csv files to test this feature or
-    you can use the sample csv file which contains data about cars.
+st.text('''You can ask me: 
+1. All the things you ask ChatGPT.
+2. Generating 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':
+    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 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 weather 
+                      updates / stock updates / current 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 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
 
-    Example question:
-    - How many cars were manufactured each year between 2000 to 2008?
-    ''')
-    
-    option = ['Sample_Cars_csv','Upload_csv']
-    res = st.selectbox('Select from below options:',option)
-    if res == 'Upload_csv':
-        uploaded_file = st.file_uploader("Add dataset (csv) ",type=['csv'])
-        if uploaded_file is not None:
-            st.write("File Uploaded")
-            file_name=uploaded_file.name     
-            ext=file_name.split(".")[0]
-            st.write(ext)
-            df=pd.read_csv(uploaded_file)
-            save_uploadedfile(uploaded_file)
-            col= df.columns
+        if ("gen_draw" in string_temp):
             try:
-                columns = str((df.columns).tolist())
-                column = clean(columns)
-                st.write('Columns:' )
-                st.text(col)
-            except:
-                pass
-    
-        temp = st.slider('Temperature: ', 0.0, 1.0, 0.0)
-        
-        with st.form("Form Layout Upload_csv"):
-            userPrompt = st.text_area("Input Prompt",'Enter Natural Language Query')
-            submitButton = st.form_submit_button(label = 'Submit')
-            
-            if submitButton:
-                try:
-                    col_p ="Create SQL statement from instruction.  "+ext+" " " (" + column +")." +" Request:" + userPrompt  + "SQL statement:" 
-                    result = gpt3(col_p)
-                    sqlOutput = result #st.text_area('SQL Query', value=gpt3(col_p))
-                    warning(sqlOutput) 
-                    result_tab2=ps.sqldf(sqlOutput)
-                    st.write(result_tab2)
-                    with open("fewshot_matplot.txt", "r") as file:
-                        text_plot = file.read()
-    
-                    result_tab = result_tab2.reset_index(drop=True)
-                    result_tab_string = result_tab.to_string()
-                    gr_prompt = text_plot + userPrompt + result_tab_string + "Plot graph for: " 
-                    
-                    if len(gr_prompt) > 4097:
-                        st.write('OVERWHELMING DATA!!! You have given me more than 4097 tokens! ^_^')
-                        st.write('As of today, the NLP model text-davinci-003/gpt-3.5-turbo that I run on takes in inputs that have less than 4097 tokens. Kindly retry ^_^')
-                    
-                    elif len(result_tab2.columns) < 2:
-                        st.write("I need more data to conduct analysis and provide visualizations for you... ^_^") 
-    
-                    else:
-                        st.success("Plotting...")
-                        response_graph = openai.Completion.create(
-                                engine="text-davinci-003",
-                                prompt = gr_prompt,
-                                max_tokens=1024,
-                                n=1,
-                                stop=None,
-                                temperature=0.5,
-                            )
-                        
-                        if response_graph['choices'][0]['text'] != "":    
-                            print(response_graph['choices'][0]['text'])
-                            exec(response_graph['choices'][0]['text'])
-                        
-                        else:
-                            print('Retry! Graph could not be plotted *_*')
-    
-                except:    
-                    results = gpt3(userPrompt)
-                    st.success('loaded')
-                    
-    elif res == "Sample_Cars_csv":
-        df = pd.read_csv('cars.csv')
-        col= df.columns
-        try:
-            columns = str((df.columns).tolist())
-            column = clean(columns)
-            st.write('Columns:' )
-            st.text(col)
-        except:
-            pass
-    
-        temp = st.slider('Temperature: ', 0.0, 1.0, 0.0)
-
-        with st.form("Form Layout Custom_csv"):
-            userPrompt = st.text_area("Input Prompt",'Enter Natural Language Query')
-            submitButton = st.form_submit_button(label = 'Submit')
-            
-            if submitButton:
                 try:
-                    col_p ="Create SQL statement from instruction.  "+ext+" " " (" + column +")." +" Request:" + userPrompt  + "SQL statement:" 
-                    result = gpt3(col_p)
-                    sqlOutput = result #st.text_area('SQL Query', value=gpt3(col_p))
-                    warning(sqlOutput)
-                    result_tab2=ps.sqldf(sqlOutput)
-                    st.write(result_tab2)
-                    
-                    with open("fewshot_matplot.txt", "r") as file:
-                        text_plot = file.read()
-        
-                    result_tab = result_tab2.reset_index(drop=True)
-                    result_tab_string = result_tab.to_string()
-                    gr_prompt = text_plot + userPrompt + result_tab_string + "Plot graph for: " 
-                    
-                    if len(gr_prompt) > 4097:
-                        st.write('OVERWHELMING DATA!!! You have given me more than 4097 tokens! ^_^')
-                        st.write('As of today, the NLP model text-davinci-003 that I run on takes in inputs that have less than 4097 tokens. Kindly retry ^_^')
-                    
-                    elif len(result_tab2.columns) < 2:
-                        st.write("I need more data to conduct analysis and provide visualizations for you... ^_^") 
-        
-                    else:
-                        st.success("Plotting...")
-                        response_graph = openai.Completion.create(
-                                engine="text-davinci-003",
-                                prompt = gr_prompt,
-                                max_tokens=1024,
-                                n=1,
-                                stop=None,
-                                temperature=0.5,
-                            )
-                        
-                        if response_graph['choices'][0]['text'] != "":    
-                            print(response_graph['choices'][0]['text'])
-                            exec(response_graph['choices'][0]['text'])
-                        
-                        else:
-                            print('Retry! Graph could not be plotted *_*')
-                except:    
-                    results = gpt3(userPrompt)
-                    st.success('loaded')
-                
-
-elif Usage == 'Ask me anything!😊':
-    st.text('''You can ask me: 
-    1. All the things you ask ChatGPT.
-    2. Generating 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':
-        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 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 weather 
-                          updates / stock updates / current 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 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'
-                        g_sheet_log(mytext, rx)
-                    except:
-                        urllib.request.urlretrieve(openai_response(prompt),"img_ret.png")
-                        img = Image.open("img_ret.png")
-                        img.show()
-                        rx = 'Image returned'
-                        g_sheet_log(mytext, rx)
+                    wget.download(openai_response(prompt))
+                    img2 = Image.open(wget.download(openai_response(prompt)))
+                    img2.show()
+                    rx = 'Image returned'
+                    g_sheet_log(mytext, rx)
                 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=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.
-                                rx = 'Image returned'
-                                g_sheet_log(mytext, rx)
-                                
-                # 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)
-                ry = 'Youtube link and video returned'
-                g_sheet_log(mytext, ry)
-    
-            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)
-    
-            else:
-                st.write(string_temp)
-                g_sheet_log(mytext, string_temp)
-
-    elif Input_type == 'SPEECH':
-        option_speech = st.selectbox(
-        'Choose from below: (Options for Transcription)',
-        ('Use Microphone', 'OpenAI Whisper (Upload audio file)')
-        )
+                    urllib.request.urlretrieve(openai_response(prompt),"img_ret.png")
+                    img = Image.open("img_ret.png")
+                    img.show()
+                    rx = 'Image returned'
+                    g_sheet_log(mytext, rx)
+            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=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)
+                )
 
-        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}));
+                # 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.
+                            rx = 'Image returned'
+                            g_sheet_log(mytext, rx)
+                            
+            # 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)
+            ry = 'Youtube link and video returned'
+            g_sheet_log(mytext, ry)
+
+        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)
+
+        else:
+            st.write(string_temp)
+            g_sheet_log(mytext, string_temp)
+
+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;
                     }
                 }
-                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")
-                    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)
+                if ( value != "") {
+                    document.dispatchEvent(new CustomEvent("GET_TEXT", {detail: value}));
+                }
+            }
+            recognition.start();
+            """))
         
-                        # 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)
+        result = streamlit_bokeh_events(
+            stt_button,
+            events="GET_TEXT",
+            key="listen",
+            refresh_on_update=False,
+            override_height=75,
+            debounce_time=0)
         
-                    elif ("don't" in string_temp or "internet" in string_temp  ):
-                        st.write('*searching internet*')
-                        search_internet(question)
-                    else:
-                        st.write(string_temp)
-
-        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
+        if result:
+            if "GET_TEXT" in result:
+                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  
@@ -707,7 +424,6 @@ elif Usage == 'Ask me anything!😊':
                 frequency_penalty=0,
                 presence_penalty=0
                 )
-                
                 string_temp=response.choices[0].text
     
                 if ("gen_draw" in string_temp):
@@ -747,77 +463,70 @@ elif Usage == 'Ask me anything!😊':
                     search_internet(question)
                 else:
                     st.write(string_temp)
+
+    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 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
+            )
             
-        # except:
-        #     pass
-            # st.text("Record your audio, **max length - 5 seconds**")
-            # if st.button("Record"):
-            #     st.write("Recording...")
-            #     audio_file = record_audio()
-            #     st.write("Recording complete.")
-            #     file = open(audio_file, "rb")
-            
-            #     # Play the recorded audio
-            #     st.audio(audio_file)
+            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 
             
-            #     transcription = openai.Audio.transcribe("whisper-1", file)
-            #     result = transcription["text"]
-            #     st.write(f"Fetched from audio - {result}")
-                
-            #     question = result
-            #     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)
+            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)
 else:
     pass