Update app.py

app.py

@@ -253,16 +253,16 @@ 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
+# 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
@@ -326,6 +326,7 @@ all with the help of HyperBot! 🤖 ✨
 
 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
@@ -372,13 +373,13 @@ if Usage == 'Questions based on custom CSV data':
                 st.success('loaded')
         with col4:
             try:
-                sqlOutput = st.text_area('SQL Query', value=gpt3(col_p))
+                sqlOutput = gpt3(col_p) #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()
+                    text_plot = file.read()
 
                 result_tab = result_tab2.reset_index(drop=True)
                 result_tab_string = result_tab.to_string()
@@ -428,57 +429,58 @@ if Usage == 'Questions based on custom CSV data':
         
         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:
+            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 = gpt3(col_p) #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)
                     
-                    if response_graph['choices'][0]['text'] != "":    
-                        print(response_graph['choices'][0]['text'])
-                        exec(response_graph['choices'][0]['text'])
+                    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:
-                        print('Retry! Graph could not be plotted *_*')
-
-            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 *_*')
+    
+                except:
+                    pass
         
 
 elif Usage == 'Random Questions':
@@ -614,93 +616,161 @@ elif Usage == 'Random Questions':
                 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;
+        option_speech = st.selectbox(
+        'Choose from below: (Options for Transcription)',
+        ('Use Microphone', 'OpenAI Whisper (Upload audio file)')
+        )
+
+        if option_speech == 'Use Microphone':
+            stt_button = Button(label="Speak", width=100)
+            stt_button.js_on_event("button_click", CustomJS(code="""
+                var recognition = new webkitSpeechRecognition();
+                recognition.continuous = true;
+                recognition.interimResults = true;
+             
+                recognition.onresult = function (e) {
+                    var value = "";
+                    for (var i = e.resultIndex; i < e.results.length; ++i) {
+                        if (e.results[i].isFinal) {
+                            value += e.results[i][0].transcript;
+                        }
+                    }
+                    if ( value != "") {
+                        document.dispatchEvent(new CustomEvent("GET_TEXT", {detail: value}));
                     }
                 }
-                if ( value != "") {
-                    document.dispatchEvent(new CustomEvent("GET_TEXT", {detail: value}));
-                }
-            }
-            recognition.start();
-            """))
+                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"
         
-        result = streamlit_bokeh_events(
-            stt_button,
-            events="GET_TEXT",
-            key="listen",
-            refresh_on_update=False,
-            override_height=75,
-            debounce_time=0)
+                        pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
+                        pipe = pipe.to(device)
         
-        if result:
-            if "GET_TEXT" in result:
-                question = result.get("GET_TEXT")
+                        # 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 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", 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
-                )
-                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)
-
-                
+                    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)
             
         # except:
         #     pass