Update app.py

app.py

@@ -172,381 +172,291 @@ def openai_response(PROMPT):
 #}
 #</style>
 #"""
-
-#st.markdown(page_bg_img, unsafe_allow_html=True)
-st.title("Ask :red[Mukesh] anything!!🤖")
-st.title("Puchne mai kya jaata hai??")
-
-option_ = ['Random Questions','Questions based on custom CSV data']
-Usage = st.selectbox('Select an option:', option_)
-if Usage == 'Questions based on custom CSV 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.
-
-    Example question:
-    - How many cars were manufactured each year between 2000 to 2008?
-    ''')
+col1, col2 = st.columns(2)
+with col1:
+    st.image('https://ibb.co/rGSj8pB')    
+with col2:
+    #st.markdown(page_bg_img, unsafe_allow_html=True)
+    st.title("Ask :red[Mukesh] anything!!🤖")
+    st.title("Puchne mai kya jaata hai??")
     
-    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
-            try:
-                columns = str((df.columns).tolist())
-                column = clean(columns)
-                st.write('Columns:' )
-                st.text(col)
-            except:
-                pass
+    option_ = ['Random Questions','Questions based on custom CSV data']
+    Usage = st.selectbox('Select an option:', option_)
+    if Usage == 'Questions based on custom CSV 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.
     
-        temp = st.slider('Temperature: ', 0.0, 1.0, 0.0)
+        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
+                try:
+                    columns = str((df.columns).tolist())
+                    column = clean(columns)
+                    st.write('Columns:' )
+                    st.text(col)
+                except:
+                    pass
         
-        with st.form(key='columns_in_form2'):
-            col3, col4 = st.columns(2)
-        with col3:
-            userPrompt = st.text_area("Input Prompt",'Enter Natural Language Query')
-            submitButton = st.form_submit_button(label = 'Submit')
-            if submitButton:
+            temp = st.slider('Temperature: ', 0.0, 1.0, 0.0)
+            
+            
+            with st.form(key='columns_in_form2'):
+                col3, col4 = st.columns(2)
+            with col3:
+                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)
+                    except:    
+                        results = gpt3(userPrompt)
+                    st.success('loaded')
+            with col4:
                 try:
-                    col_p ="Create SQL statement from instruction.  "+ext+" " " (" + column +")." +" Request:" + userPrompt  + "SQL statement:" 
-                    result = gpt3(col_p)
-                except:    
-                    results = gpt3(userPrompt)
-                st.success('loaded')
-        with col4:
-            try:
-                sqlOutput = st.text_area('SQL Query', value=gpt3(col_p))
-                warning(sqlOutput) 
-                cars=pd.read_csv('cars.csv')
-                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,
-                        )
+                    sqlOutput = st.text_area('SQL Query', value=gpt3(col_p))
+                    warning(sqlOutput) 
+                    cars=pd.read_csv('cars.csv')
+                    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 response_graph['choices'][0]['text'] != "":    
-                        print(response_graph['choices'][0]['text'])
-                        exec(response_graph['choices'][0]['text'])
+                    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:
-                        print('Retry! Graph could not be plotted *_*')
-
-            except:
-                pass
-
-    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
+                        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 *_*')
     
-        temp = st.slider('Temperature: ', 0.0, 1.0, 0.0)
-        
-        
-        with st.form(key='columns_in_form2'):
-            col3, col4 = st.columns(2)
-        with col3:
-            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)
-                except:    
-                    results = gpt3(userPrompt)
-                st.success('loaded')
-        with col4:
+                except:
+                    pass
+    
+        elif res == "Sample_Cars_csv":
+            df = pd.read_csv('cars.csv')
+            col= df.columns
             try:
-                sqlOutput = st.text_area('SQL Query', value=gpt3(col_p))
-                warning(sqlOutput) 
-                cars=pd.read_csv('cars.csv')
-                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 *_*')
-
+                columns = str((df.columns).tolist())
+                column = clean(columns)
+                st.write('Columns:' )
+                st.text(col)
             except:
                 pass
         
-
-elif Usage == 'Random Questions':
-    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':
-        #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:
+            temp = st.slider('Temperature: ', 0.0, 1.0, 0.0)
+            
+            
+            with st.form(key='columns_in_form2'):
+                col3, col4 = st.columns(2)
+            with col3:
+                userPrompt = st.text_area("Input Prompt",'Enter Natural Language Query')
+                submitButton = st.form_submit_button(label = 'Submit')
+                if submitButton:
                     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)
-                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)
+                        col_p ="Create SQL statement from instruction.  "+ext+" " " (" + column +")." +" Request:" + userPrompt  + "SQL statement:" 
+                        result = gpt3(col_p)
+                    except:    
+                        results = gpt3(userPrompt)
+                    st.success('loaded')
+            with col4:
+                try:
+                    sqlOutput = st.text_area('SQL Query', value=gpt3(col_p))
+                    warning(sqlOutput) 
+                    cars=pd.read_csv('cars.csv')
+                    result_tab2=ps.sqldf(sqlOutput)
+                    st.write(result_tab2)
+                    with open("fewshot_matplot.txt", "r") as file:
+                                text_plot = file.read()
     
-                #     # prompt = "a photo of an astronaut riding a horse on mars"
-                #     image = pipe(prompt).images[0]  
+                    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 ^_^')
                     
-                #     image.save("astronaut_rides_horse.png")
-                #     st.image(image)
-                #     # image
+                    elif len(result_tab2.columns) < 2:
+                        st.write("I need more data to conduct analysis and provide visualizations for you... ^_^") 
     
-            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)
+                    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 *_*')
     
-            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, rz)
+                except:
+                    pass
+            
     
-            else:
-                st.write(string_temp)
-                g_sheet_log(mytext, string_temp)
+    elif Usage == 'Random Questions':
+        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.''')
         
-    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();
-            """))
+        Input_type = st.radio(
+            "**Input type:**",
+            ('TEXT', 'SPEECH')
+            )
         
-        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")
+        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 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
+                  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):
-                    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 
-                
+                    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)
+                    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(question)
+                    s = Search(mytext)
                     search_res = s.results
                     first_vid = search_res[0]
                     print(first_vid)
@@ -557,9 +467,102 @@ elif Usage == 'Random Questions':
                     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*')
+                    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, rz)
+        
                 else:
                     st.write(string_temp)
+                    g_sheet_log(mytext, 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)