App updated

app.py

@@ -1,7 +1,9 @@
 # Main driver code of chatbot
+from cgitb import text
 import streamlit as st
 from streamlit_option_menu import option_menu
 from src.components.avatarsys import AvatarSystem
+from src.exception.exception import customexception
 from src.utils.accessory import play_speech, listen, save_output, load_output
 import speech_recognition as sr
 
@@ -41,10 +43,33 @@ def response(input_text):
         
     return ans, response_sentiment
 
+# Voice to text conversion
+r = sr.Recognizer()
+
+def record_voice():
+    try:
+        with sr.Microphone() as source:
+            st.write("Speak: ") # print("Say something!")
+            st.write("Please wait, response under process...")
+            audio = r.listen(source)
+            # r.adjust_for_ambient_noise(source, duration=0.2)
+            text = r.recognize_google(audio)
+            user_input = text + '?'
+
+            return user_input
+    
+    except sr.RequestError as e:
+        st.write("Could not request results: {0}".format(e))
+
+    except sr.UnknownValueError:
+        st.write("Unknown error occurred.")
+                
+
+
 if mode == "Text" and st.session_state.HF_TOKEN == '':    
     if 'chathist' not in st.session_state:
         st.session_state.chathist = dict()
-    
+
     # Form requires unique key
     with st.form(key=f'Chat form', clear_on_submit=True):
         user_input = st.text_input("You: ", value="", placeholder="Ask anything or Type 'Exit'")
@@ -64,7 +89,44 @@ if mode == "Text" and st.session_state.HF_TOKEN == '':
         # Chat history 
         st.session_state.chathist = chat_history(user_input, ans, senti)
     
-    # Chat history display
+    # # Chat history display
+    # if st.button("View chat")
+    # st.markdown("### Chat History: ")
+    # with st.container(border=True):
+    #     for key in st.session_state.chathist.keys():
+    #         user_col1, user_col2, user_col3 = st.columns(3, vertical_alignment="center")
+    #         user = user_col3.container(border=True)
+    #         user.write(key)
+    #         bot_col1, bot_col2, bot_col3 = st.columns([4, 1, 1], vertical_alignment='center')
+    #         bot = bot_col1.container(border=True)
+    #         bot.write(st.session_state.chathist[key][0])
+                            
+elif mode == "Voice" and st.session_state.HF_TOKEN == '':
+    if 'chathist' not in st.session_state:
+        st.session_state.chathist = dict()
+    
+    if st.button("speak"):
+        user_input = record_voice()
+
+        if user_input != "":
+
+            # Exiting the chat
+            if 'exit' in user_input:
+                st.write(salutation)
+                st.stop()
+
+            # Getting response and sentiment of response 
+            ans, senti = response(user_input)
+
+            st.write(f"You: {user_input}")
+            st.write(f"Bot: {ans}")
+            play_speech(ans)
+           
+            # Chat history 
+            st.session_state.chathist = chat_history(user_input, ans, senti)
+        
+# Chat history display
+if st.button("View Chat history"):
     st.markdown("### Chat History: ")
     with st.container(border=True):
         for key in st.session_state.chathist.keys():
@@ -74,32 +136,6 @@ if mode == "Text" and st.session_state.HF_TOKEN == '':
             bot_col1, bot_col2, bot_col3 = st.columns([4, 1, 1], vertical_alignment='center')
             bot = bot_col1.container(border=True)
             bot.write(st.session_state.chathist[key][0])
-                            
-elif mode == "Voice" and st.session_state.HF_TOKEN != '':
-    # Voice to text conversion
-    r = sr.Recognizer()
-    while 1:
-        with sr.Microphone() as source:
-            st.write("Speak: ") # print("Say something!")
-            st.write("Please wait, response under process...")
-            audio = r.listen(source)
-            r.adjust_for_ambient_noise(source, duration=0.2)
-            text = r.recognize_google(audio)
-            user_input = text + '?'
-            if text == '':
-                user_input='exit?'
-                st.write("Start again please. Failed to recognise the voice.")
-                
-            # Exiting the chat
-            if 'exit' in user_input:
-                st.write(salutation) # print("Pleasure meeting you. Have a nice day!")
-                play_speech(salutation)
-                st.stop()
-                break
-        
-            #Getting response and sentiment of response 
-            response(user_input)
-
 
 # if mode == "Doc-Bot" and st.session_state.HF_TOKEN != '':
 #     st.write("Doc-Bot implementation")

artifacts/Docmate.ipynb

@@ -825,59 +825,527 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "c:\\Users\\umesh\\anaconda3\\envs\\DeepLearning\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n",
-      "c:\\Users\\umesh\\anaconda3\\envs\\DeepLearning\\lib\\site-packages\\torchvision\\io\\image.py:13: UserWarning: Failed to load image Python extension: '[WinError 127] The specified procedure could not be found'If you don't plan on using image functionality from `torchvision.io`, you can ignore this warning. Otherwise, there might be something wrong with your environment. Did you have `libjpeg` or `libpng` installed before building `torchvision` from source?\n",
-      "  warn(\n",
-      "c:\\Users\\umesh\\anaconda3\\envs\\DeepLearning\\lib\\site-packages\\torchvision\\datapoints\\__init__.py:12: UserWarning: The torchvision.datapoints and torchvision.transforms.v2 namespaces are still Beta. While we do not expect major breaking changes, some APIs may still change according to user feedback. Please submit any feedback you may have in this issue: https://github.com/pytorch/vision/issues/6753, and you can also check out https://github.com/pytorch/vision/issues/7319 to learn more about the APIs that we suspect might involve future changes. You can silence this warning by calling torchvision.disable_beta_transforms_warning().\n",
-      "  warnings.warn(_BETA_TRANSFORMS_WARNING)\n",
-      "c:\\Users\\umesh\\anaconda3\\envs\\DeepLearning\\lib\\site-packages\\torchvision\\transforms\\v2\\__init__.py:54: UserWarning: The torchvision.datapoints and torchvision.transforms.v2 namespaces are still Beta. While we do not expect major breaking changes, some APIs may still change according to user feedback. Please submit any feedback you may have in this issue: https://github.com/pytorch/vision/issues/6753, and you can also check out https://github.com/pytorch/vision/issues/7319 to learn more about the APIs that we suspect might involve future changes. You can silence this warning by calling torchvision.disable_beta_transforms_warning().\n",
-      "  warnings.warn(_BETA_TRANSFORMS_WARNING)\n"
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+      "   ---------------------------------------- 2.0/2.0 MB 109.4 kB/s eta 0:00:00\n",
+      "Installing collected packages: streamlit-bokeh-events\n",
+      "Successfully installed streamlit-bokeh-events-0.1.2\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "pip install streamlit-bokeh-events"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
     {
-     "ename": "ImportError",
-     "evalue": "Loading an AWQ quantized model requires auto-awq library (`pip install autoawq`)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[1], line 4\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Use a pipeline as a high-level helper\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtransformers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pipeline\n\u001b[1;32m----> 4\u001b[0m pipe \u001b[38;5;241m=\u001b[39m \u001b[43mpipeline\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtext-generation\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mTheBloke/meditron-7B-chat-AWQ\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\umesh\\anaconda3\\envs\\DeepLearning\\lib\\site-packages\\transformers\\pipelines\\__init__.py:926\u001b[0m, in \u001b[0;36mpipeline\u001b[1;34m(task, model, config, tokenizer, feature_extractor, image_processor, processor, framework, revision, use_fast, token, device, device_map, torch_dtype, trust_remote_code, model_kwargs, pipeline_class, **kwargs)\u001b[0m\n\u001b[0;32m    924\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(model, \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m framework \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    925\u001b[0m     model_classes \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtf\u001b[39m\u001b[38;5;124m\"\u001b[39m: targeted_task[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtf\u001b[39m\u001b[38;5;124m\"\u001b[39m], \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpt\u001b[39m\u001b[38;5;124m\"\u001b[39m: targeted_task[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpt\u001b[39m\u001b[38;5;124m\"\u001b[39m]}\n\u001b[1;32m--> 926\u001b[0m     framework, model \u001b[38;5;241m=\u001b[39m infer_framework_load_model(\n\u001b[0;32m    927\u001b[0m         model,\n\u001b[0;32m    928\u001b[0m         model_classes\u001b[38;5;241m=\u001b[39mmodel_classes,\n\u001b[0;32m    929\u001b[0m         config\u001b[38;5;241m=\u001b[39mconfig,\n\u001b[0;32m    930\u001b[0m         framework\u001b[38;5;241m=\u001b[39mframework,\n\u001b[0;32m    931\u001b[0m         task\u001b[38;5;241m=\u001b[39mtask,\n\u001b[0;32m    932\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mhub_kwargs,\n\u001b[0;32m    933\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mmodel_kwargs,\n\u001b[0;32m    934\u001b[0m     )\n\u001b[0;32m    936\u001b[0m model_config \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mconfig\n\u001b[0;32m    937\u001b[0m hub_kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_commit_hash\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39m_commit_hash\n",
-      "File \u001b[1;32mc:\\Users\\umesh\\anaconda3\\envs\\DeepLearning\\lib\\site-packages\\transformers\\pipelines\\base.py:289\u001b[0m, in \u001b[0;36minfer_framework_load_model\u001b[1;34m(model, config, model_classes, task, framework, **model_kwargs)\u001b[0m\n\u001b[0;32m    283\u001b[0m     logger\u001b[38;5;241m.\u001b[39mwarning(\n\u001b[0;32m    284\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mModel might be a PyTorch model (ending with `.bin`) but PyTorch is not available. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    285\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTrying to load the model with Tensorflow.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    286\u001b[0m     )\n\u001b[0;32m    288\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 289\u001b[0m     model \u001b[38;5;241m=\u001b[39m model_class\u001b[38;5;241m.\u001b[39mfrom_pretrained(model, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    290\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(model, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124meval\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m    291\u001b[0m         model \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39meval()\n",
-      "File \u001b[1;32mc:\\Users\\umesh\\anaconda3\\envs\\DeepLearning\\lib\\site-packages\\transformers\\models\\auto\\auto_factory.py:564\u001b[0m, in \u001b[0;36m_BaseAutoModelClass.from_pretrained\u001b[1;34m(cls, pretrained_model_name_or_path, *model_args, **kwargs)\u001b[0m\n\u001b[0;32m    562\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(config) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_model_mapping\u001b[38;5;241m.\u001b[39mkeys():\n\u001b[0;32m    563\u001b[0m     model_class \u001b[38;5;241m=\u001b[39m _get_model_class(config, \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_model_mapping)\n\u001b[1;32m--> 564\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m model_class\u001b[38;5;241m.\u001b[39mfrom_pretrained(\n\u001b[0;32m    565\u001b[0m         pretrained_model_name_or_path, \u001b[38;5;241m*\u001b[39mmodel_args, config\u001b[38;5;241m=\u001b[39mconfig, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mhub_kwargs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[0;32m    566\u001b[0m     )\n\u001b[0;32m    567\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    568\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnrecognized configuration class \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconfig\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m for this kind of AutoModel: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    569\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mModel type should be one of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(c\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mfor\u001b[39;00m\u001b[38;5;250m \u001b[39mc\u001b[38;5;250m \u001b[39m\u001b[38;5;129;01min\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_model_mapping\u001b[38;5;241m.\u001b[39mkeys())\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    570\u001b[0m )\n",
-      "File \u001b[1;32mc:\\Users\\umesh\\anaconda3\\envs\\DeepLearning\\lib\\site-packages\\transformers\\modeling_utils.py:3656\u001b[0m, in \u001b[0;36mPreTrainedModel.from_pretrained\u001b[1;34m(cls, pretrained_model_name_or_path, config, cache_dir, ignore_mismatched_sizes, force_download, local_files_only, token, revision, use_safetensors, weights_only, *model_args, **kwargs)\u001b[0m\n\u001b[0;32m   3653\u001b[0m     hf_quantizer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   3655\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m hf_quantizer \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 3656\u001b[0m     \u001b[43mhf_quantizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalidate_environment\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   3657\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtorch_dtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtorch_dtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfrom_tf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfrom_tf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfrom_flax\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfrom_flax\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdevice_map\u001b[49m\n\u001b[0;32m   3658\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   3659\u001b[0m     torch_dtype \u001b[38;5;241m=\u001b[39m hf_quantizer\u001b[38;5;241m.\u001b[39mupdate_torch_dtype(torch_dtype)\n\u001b[0;32m   3660\u001b[0m     device_map \u001b[38;5;241m=\u001b[39m hf_quantizer\u001b[38;5;241m.\u001b[39mupdate_device_map(device_map)\n",
-      "File \u001b[1;32mc:\\Users\\umesh\\anaconda3\\envs\\DeepLearning\\lib\\site-packages\\transformers\\quantizers\\quantizer_awq.py:50\u001b[0m, in \u001b[0;36mAwqQuantizer.validate_environment\u001b[1;34m(self, device_map, **kwargs)\u001b[0m\n\u001b[0;32m     48\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mvalidate_environment\u001b[39m(\u001b[38;5;28mself\u001b[39m, device_map, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m     49\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_auto_awq_available():\n\u001b[1;32m---> 50\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLoading an AWQ quantized model requires auto-awq library (`pip install autoawq`)\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     52\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_accelerate_available():\n\u001b[0;32m     53\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLoading an AWQ quantized model requires accelerate (`pip install accelerate`)\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "\u001b[1;31mImportError\u001b[0m: Loading an AWQ quantized model requires auto-awq library (`pip install autoawq`)"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-10-30 10:52:39.204 WARNING streamlit.runtime.scriptrunner_utils.script_run_context: Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
+      "2024-10-30 10:52:39.276 WARNING streamlit.runtime.scriptrunner_utils.script_run_context: Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
+      "2024-10-30 10:52:39.277 WARNING streamlit.runtime.scriptrunner_utils.script_run_context: Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
+      "2024-10-30 10:52:39.278 WARNING streamlit.runtime.scriptrunner_utils.script_run_context: Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n",
+      "2024-10-30 10:52:39.371 \n",
+      "  \u001b[33m\u001b[1mWarning:\u001b[0m to view this Streamlit app on a browser, run it with the following\n",
+      "  command:\n",
+      "\n",
+      "    streamlit run c:\\Users\\umesh\\anaconda3\\envs\\DeepLearning\\lib\\site-packages\\ipykernel_launcher.py [ARGUMENTS]\n",
+      "2024-10-30 10:52:39.372 Thread 'MainThread': missing ScriptRunContext! This warning can be ignored when running in bare mode.\n"
      ]
     }
    ],
    "source": [
-    "# Use a pipeline as a high-level helper\n",
-    "from transformers import pipeline\n",
+    "import streamlit as st\n",
+    "from bokeh.models.widgets import Button\n",
+    "from bokeh.models import CustomJS\n",
+    "from streamlit_bokeh_events import streamlit_bokeh_events\n",
+    "\n",
+    "stt_button = Button(label=\"Speak\", width=100)\n",
+    "\n",
+    "stt_button.js_on_event(\"button_click\", CustomJS(code=\"\"\"\n",
+    "    var recognition = new webkitSpeechRecognition();\n",
+    "    recognition.continuous = true;\n",
+    "    recognition.interimResults = true;\n",
+    " \n",
+    "    recognition.onresult = function (e) {\n",
+    "        var value = \"\";\n",
+    "        for (var i = e.resultIndex; i < e.results.length; ++i) {\n",
+    "            if (e.results[i].isFinal) {\n",
+    "                value += e.results[i][0].transcript;\n",
+    "            }\n",
+    "        }\n",
+    "        if ( value != \"\") {\n",
+    "            document.dispatchEvent(new CustomEvent(\"GET_TEXT\", {detail: value}));\n",
+    "        }\n",
+    "    }\n",
+    "    recognition.start();\n",
+    "    \"\"\"))\n",
     "\n",
-    "pipe = pipeline(\"text-generation\", model=\"TheBloke/meditron-7B-chat-AWQ\")"
+    "result = streamlit_bokeh_events(\n",
+    "    stt_button,\n",
+    "    events=\"GET_TEXT\",\n",
+    "    key=\"listen\",\n",
+    "    refresh_on_update=False,\n",
+    "    override_height=75,\n",
+    "    debounce_time=0)\n",
+    "\n",
+    "if result:\n",
+    "    if \"GET_TEXT\" in result:\n",
+    "        st.write(result.get(\"GET_TEXT\"))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "^C\n",
+      "Requirement already satisfied: pyzmq in c:\\users\\umesh\\anaconda3\\envs\\deeplearning\\lib\\site-packages (23.2.0)\n",
       "Note: you may need to restart the kernel to use updated packages.\n"
      ]
     }
    ],
    "source": [
-    "pip install autoawq"
+    "pip install pyzmq"
    ]
   }
  ],

artifacts/response.txt

@@ -1,3 +1,3 @@
 
 
-I'm doing well, thank you! How about you
+Pythagoras was an ancient Greek philosopher and mathematician who is best known for the Pythagorean theorem, which states that in a right-angled triangle, the square of the length of the hypotenuse (the side opposite the right angle) is equal to the sum of the squares of the lengths of the other two sides. He is also credited with the discovery of the five regular solids, and he founded the Pythagorean Brotherhood, a religious and philosophical community that had a significant influence on Western thought. Pythagoras lived in the 6th century BC and is considered one of the most important figures in the history of mathematics and philosophy.

logs/10_30_2024_09_32_06.log

@@ -0,0 +1,97 @@
+[2024-10-30 09:32:08,069] 28 root - INFO - LLM model for text generation created.
+[2024-10-30 09:32:08,070] 29 root - INFO - LLM model for medical text generation created.
+[2024-10-30 09:32:17,268] 23 root - INFO - Avatar system initiated.
+[2024-10-30 09:38:40,395] 28 root - INFO - LLM model for text generation created.
+[2024-10-30 09:38:40,399] 30 root - INFO - LLM model for medical text generation created.
+[2024-10-30 09:38:42,252] 23 root - INFO - Avatar system initiated.
+[2024-10-30 09:39:23,130] 33 root - INFO - Text response generated.
+[2024-10-30 09:42:06,980] 28 root - INFO - LLM model for text generation created.
+[2024-10-30 09:42:06,984] 30 root - INFO - LLM model for medical text generation created.
+[2024-10-30 09:42:09,191] 23 root - INFO - Avatar system initiated.
+[2024-10-30 09:42:24,758] 33 root - INFO - Text response generated.
+[2024-10-30 09:45:30,730] 33 root - INFO - Text response generated.
+[2024-10-30 09:51:32,564] 28 root - INFO - LLM model for text generation created.
+[2024-10-30 09:51:32,568] 30 root - INFO - LLM model for medical text generation created.
+[2024-10-30 09:51:44,217] 23 root - INFO - Avatar system initiated.
+[2024-10-30 09:52:10,172] 33 root - INFO - Text response generated.
+[2024-10-30 09:56:50,731] 31 root - INFO - Text response generated.
+[2024-10-30 09:57:01,301] 32 root - INFO - Text response generated.
+[2024-10-30 09:57:02,340] 30 root - INFO - Sentiment of response generated.
+[2024-10-30 09:57:02,344] 36 root - INFO - Response sentiment received.
+[2024-10-30 09:57:10,189] 19 root - INFO - Response text convertion to audio done.
+[2024-10-30 09:57:10,189] 40 root - INFO - Generated response saved as audio mp3 format.
+[2024-10-30 09:57:10,189] 67 root - INFO - Generated response stored in response.txt file in artifacts folder.
+[2024-10-30 09:57:10,190] 73 root - INFO - Stored text loaded.
+[2024-10-30 10:11:10,193] 28 root - INFO - LLM model for text generation created.
+[2024-10-30 10:11:10,197] 30 root - INFO - LLM model for medical text generation created.
+[2024-10-30 10:11:50,869] 23 root - INFO - Avatar system initiated.
+[2024-10-30 10:12:42,676] 33 root - INFO - Text response generated.
+[2024-10-30 10:13:10,566] 29 root - INFO - Docbot Text response generated.
+[2024-10-30 10:13:11,035] 30 root - INFO - Sentiment of response generated.
+[2024-10-30 10:13:11,035] 36 root - INFO - Response sentiment received.
+[2024-10-30 10:15:41,599] 19 root - INFO - Response text convertion to audio done.
+[2024-10-30 10:15:41,599] 40 root - INFO - Generated response saved as audio mp3 format.
+[2024-10-30 10:15:41,606] 67 root - INFO - Generated response stored in response.txt file in artifacts folder.
+[2024-10-30 10:15:41,606] 73 root - INFO - Stored text loaded.
+[2024-10-30 10:22:00,106] 28 root - INFO - LLM model for text generation created.
+[2024-10-30 10:22:00,106] 30 root - INFO - LLM model for medical text generation created.
+[2024-10-30 10:22:02,829] 22 root - INFO - Avatar system initiated.
+[2024-10-30 10:22:33,338] 28 root - INFO - LLM model for text generation created.
+[2024-10-30 10:22:33,338] 30 root - INFO - LLM model for medical text generation created.
+[2024-10-30 10:22:36,576] 22 root - INFO - Avatar system initiated.
+[2024-10-30 10:22:58,937] 28 root - INFO - LLM model for text generation created.
+[2024-10-30 10:22:58,937] 30 root - INFO - LLM model for medical text generation created.
+[2024-10-30 10:23:02,005] 22 root - INFO - Avatar system initiated.
+[2024-10-30 10:24:18,130] 28 root - INFO - LLM model for text generation created.
+[2024-10-30 10:24:18,130] 30 root - INFO - LLM model for medical text generation created.
+[2024-10-30 10:24:20,276] 22 root - INFO - Avatar system initiated.
+[2024-10-30 10:24:33,787] 28 root - INFO - LLM model for text generation created.
+[2024-10-30 10:24:33,788] 30 root - INFO - LLM model for medical text generation created.
+[2024-10-30 10:24:37,352] 22 root - INFO - Avatar system initiated.
+[2024-10-30 10:25:15,562] 28 root - INFO - LLM model for text generation created.
+[2024-10-30 10:25:15,566] 30 root - INFO - LLM model for medical text generation created.
+[2024-10-30 10:25:18,220] 22 root - INFO - Avatar system initiated.
+[2024-10-30 10:25:51,124] 28 root - INFO - LLM model for text generation created.
+[2024-10-30 10:25:51,124] 30 root - INFO - LLM model for medical text generation created.
+[2024-10-30 10:25:53,372] 22 root - INFO - Avatar system initiated.
+[2024-10-30 10:27:31,296] 28 root - INFO - LLM model for text generation created.
+[2024-10-30 10:27:31,297] 30 root - INFO - LLM model for medical text generation created.
+[2024-10-30 10:27:33,655] 22 root - INFO - Avatar system initiated.
+[2024-10-30 10:27:41,868] 31 root - INFO - Text response generated.
+[2024-10-30 10:27:43,255] 28 root - INFO - Text response generated.
+[2024-10-30 10:27:43,536] 30 root - INFO - Sentiment of response generated.
+[2024-10-30 10:27:43,553] 32 root - INFO - Response sentiment received.
+[2024-10-30 10:27:44,542] 19 root - INFO - Response text convertion to audio done.
+[2024-10-30 10:27:44,542] 36 root - INFO - Generated response saved as audio mp3 format.
+[2024-10-30 10:27:44,542] 67 root - INFO - Generated response stored in response.txt file in artifacts folder.
+[2024-10-30 10:27:44,550] 73 root - INFO - Stored text loaded.
+[2024-10-30 10:31:37,573] 28 root - INFO - LLM model for text generation created.
+[2024-10-30 10:31:37,574] 30 root - INFO - LLM model for medical text generation created.
+[2024-10-30 10:31:40,587] 22 root - INFO - Avatar system initiated.
+[2024-10-30 10:31:45,825] 31 root - INFO - Text response generated.
+[2024-10-30 10:31:47,369] 28 root - INFO - Text response generated.
+[2024-10-30 10:31:47,521] 30 root - INFO - Sentiment of response generated.
+[2024-10-30 10:31:47,522] 32 root - INFO - Response sentiment received.
+[2024-10-30 10:31:48,241] 19 root - INFO - Response text convertion to audio done.
+[2024-10-30 10:31:48,241] 36 root - INFO - Generated response saved as audio mp3 format.
+[2024-10-30 10:31:48,243] 67 root - INFO - Generated response stored in response.txt file in artifacts folder.
+[2024-10-30 10:31:48,244] 73 root - INFO - Stored text loaded.
+[2024-10-30 10:31:52,912] 31 root - INFO - Text response generated.
+[2024-10-30 10:31:53,222] 28 root - INFO - Text response generated.
+[2024-10-30 10:31:53,273] 30 root - INFO - Sentiment of response generated.
+[2024-10-30 10:31:53,274] 32 root - INFO - Response sentiment received.
+[2024-10-30 10:31:53,780] 19 root - INFO - Response text convertion to audio done.
+[2024-10-30 10:31:53,780] 36 root - INFO - Generated response saved as audio mp3 format.
+[2024-10-30 10:31:53,780] 67 root - INFO - Generated response stored in response.txt file in artifacts folder.
+[2024-10-30 10:31:53,782] 73 root - INFO - Stored text loaded.
+[2024-10-30 10:31:58,394] 19 root - INFO - Response text convertion to audio done.
+[2024-10-30 10:31:58,606] 23 root - INFO - Generated audio file loaded
+[2024-10-30 10:32:01,688] 32 root - INFO - Created audio file removed for entry of new file.
+[2024-10-30 10:37:51,073] 31 root - INFO - Text response generated.
+[2024-10-30 10:37:59,434] 28 root - INFO - Text response generated.
+[2024-10-30 10:38:00,049] 30 root - INFO - Sentiment of response generated.
+[2024-10-30 10:38:00,050] 32 root - INFO - Response sentiment received.
+[2024-10-30 10:38:44,565] 19 root - INFO - Response text convertion to audio done.
+[2024-10-30 10:38:44,566] 36 root - INFO - Generated response saved as audio mp3 format.
+[2024-10-30 10:38:44,567] 67 root - INFO - Generated response stored in response.txt file in artifacts folder.
+[2024-10-30 10:38:44,569] 73 root - INFO - Stored text loaded.

src/components/avatarsys.py

@@ -4,6 +4,7 @@ from src.components.textprocess import TextProcessor
 from src.components.docchat import DocChatProcessor
 from src.components.emotionanalyz import EmotionAnalyzer
 from src.components.voicesynth import VoiceSynthesizer
+import sys
 
 class AvatarConfig:
     def __init__(self):
@@ -22,20 +23,23 @@ class AvatarSystem:
     logging.info("Avatar system initiated.")
 
     def process_input(self, user_input):
-        # Generate response
-        
-        response = self.text_processor.generate_response(user_input)
-        logging.info("Text response generated.")
+        try:
+            # Generate response
+            response = self.text_processor.generate_response(user_input)
+            logging.info("Text response generated.")
 
-        # Analyze emotion
-        emotion = self.emotion_analyzer.analyze_emotion(response)
-        logging.info("Response sentiment received.")
-                
-        # Synthesize voice and saves as mp3 file
-        self.voice_synthesiser.synthesize_speech(response)
-        logging.info("Generated response saved as audio mp3 format.")
+            # Analyze emotion
+            emotion = self.emotion_analyzer.analyze_emotion(response)
+            logging.info("Response sentiment received.")
+                    
+            # Synthesize voice and saves as mp3 file
+            self.voice_synthesiser.synthesize_speech(response)
+            logging.info("Generated response saved as audio mp3 format.")
+            
+            return {
+                'response_text': response,
+                'emotion': emotion
+            }
         
-        return {
-            'response_text': response,
-            'emotion': emotion
-        }
+        except Exception as e:
+            raise customexception(e,sys)

src/components/docchat.py

@@ -1,9 +1,7 @@
 # Text generation
 from src.logger.logger import logging
 from src.exception.exception import customexception
-# import os
-# from dotenv import load_dotenv
-# load_dotenv()
+import sys
 from langchain_huggingface import HuggingFaceEndpoint
 
 # Text generation model
@@ -30,5 +28,8 @@ class DocChatProcessor:
     logging.info("LLM model for medical text generation created.")
 
     def generate_response(self, input_text):
-        logging.info("Text response generated.")
-        return self.llm.invoke(input_text)
+        try:
+            logging.info("Text response generated.")
+            return self.llm.invoke(input_text)
+        except Exception as e:
+            raise customexception(e,sys)

src/components/emotionanalyz.py

@@ -4,6 +4,7 @@ from src.logger.logger import logging
 from transformers import AutoTokenizer
 from transformers import AutoModelForSequenceClassification
 from scipy.special import softmax
+import sys
 
 # Pretrained model
 MODEL = f"cardiffnlp/twitter-roberta-base-sentiment"
@@ -17,16 +18,19 @@ class EmotionAnalyzer:
     
     #  Roberta model
     def analyze_emotion(self, text):
-        encoded_text = tokenizer(text, return_tensors='pt')
-        output = self.emotion_classifier(**encoded_text)
-        scores = output[0][0].detach().numpy()
-        scores = softmax(scores)
-        scores_dict = {
-            'negative': scores[0],
-            'neutral': scores[1],
-            'positive': scores[2],
-        }
-        self.emotion = max(scores_dict)
-        logging.info("Sentiment of response generated.")
+        try:
+            encoded_text = tokenizer(text, return_tensors='pt')
+            output = self.emotion_classifier(**encoded_text)
+            scores = output[0][0].detach().numpy()
+            scores = softmax(scores)
+            scores_dict = {
+                'negative': scores[0],
+                'neutral': scores[1],
+                'positive': scores[2],
+            }
+            self.emotion = max(scores_dict)
+            logging.info("Sentiment of response generated.")
 
-        return self.emotion
+            return self.emotion
+        except Exception as e:
+            raise customexception(e,sys)

src/components/textprocess.py

@@ -1,9 +1,7 @@
 # Text generation
 from src.logger.logger import logging
 from src.exception.exception import customexception
-# import os
-# from dotenv import load_dotenv
-# load_dotenv()
+import sys
 from langchain_huggingface import HuggingFaceEndpoint
 
 # Text generation model
@@ -22,11 +20,14 @@ class TextProcessor:
                                     repetition_penalty=1.03,  
                                     streaming=False,  
                                     # huggingfacehub_api_token= hf_token,
-                                    stop_sequences=['?', '</s>', '.\n\n']  
+                                    # stop_sequences=['?', '</s>', '.\n\n']  
                                 ) 
         
     logging.info("LLM model for text generation created.")
 
     def generate_response(self, input_text):
-        logging.info("Text response generated.")
-        return self.llm.invoke(input_text)
+        try:
+            logging.info("Text response generated.")
+            return self.llm.invoke(input_text)
+        except Exception as e:
+            raise customexception(e,sys)

src/components/voicesynth.py

@@ -2,18 +2,23 @@
 from src.logger.logger import logging
 from src.exception.exception import customexception
 from gtts import gTTS
+import sys
 
 class VoiceSynthesizer:
     def __init__(self):
       pass  
               
-    def synthesize_speech(self, text):       
-        # Text to English 
-        language = 'en'
+    def synthesize_speech(self, text):
+        try:       
+          # Text to English 
+          language = 'en'
 
-        # Conversion engine
-        converter = gTTS(text=text, lang=language, slow=False)
+          # Conversion engine
+          converter = gTTS(text=text, lang=language, slow=False)
 
-        # Saving the converted audio in a mp3 file named
-        converter.save("artifacts/Audio.mp3")
-        logging.info("Response text convertion to audio done.")
+          # Saving the converted audio in a mp3 file named
+          converter.save("artifacts/Audio.mp3")
+          logging.info("Response text convertion to audio done.")
+        
+        except Exception as e:
+            raise customexception(e,sys)

src/utils/accessory.py

@@ -1,4 +1,5 @@
 import os
+import sys
 from pathlib import Path
 import speech_recognition as sr
 import pyttsx3 
@@ -11,34 +12,41 @@ from src.exception.exception import customexception
 r = sr.Recognizer()
 
 def play_speech(text):
-    # Voice syntesizer initiation
-    converter = VoiceSynthesizer()
-    converter.synthesize_speech(text)
+    try:
+        # Voice syntesizer initiation
+        converter = VoiceSynthesizer()
+        converter.synthesize_speech(text)
+
+        # Initialize the mixer module
+        pygame.mixer.init()
 
-    # Initialize the mixer module
-    pygame.mixer.init()
+        # Load the mp3 file
+        pygame.mixer.music.load("artifacts/Audio.mp3")
+        logging.info("Generated audio file loaded")
 
-    # Load the mp3 file
-    pygame.mixer.music.load("artifacts/Audio.mp3")
-    logging.info("Generated audio file loaded")
+        # Play the loaded mp3 file
+        pygame.mixer.music.play()
+        pygame.mixer.music.get_endevent()
+        while pygame.mixer.music.get_busy():
+            continue
+        pygame.mixer.music.load("artifacts/Audio_copy.mp3")
+        os.remove("artifacts/Audio.mp3")
+        logging.info("Created audio file removed for entry of new file.")
 
-    # Play the loaded mp3 file
-    pygame.mixer.music.play()
-    pygame.mixer.music.get_endevent()
-    while pygame.mixer.music.get_busy():
-        continue
-    pygame.mixer.music.load("artifacts/Audio_copy.mp3")
-    os.remove("artifacts/Audio.mp3")
-    logging.info("Created audio file removed for entry of new file.")
+    except Exception as e:
+        raise customexception(e,sys)
 
 # Function to convert text to
 # speech
 def SpeakText(command):
-    # Initialize the engine
-    engine = pyttsx3.init()
-    engine.say(command) 
-    engine.runAndWait()
-
+    try:
+        # Initialize the engine
+        engine = pyttsx3.init()
+        engine.say(command) 
+        engine.runAndWait()
+    except Exception as e:
+        raise customexception(e,sys)
+    
 def listen():
     # obtain audio from the microphone
     r = sr.Recognizer()
@@ -54,22 +62,30 @@ def listen():
         return text
     
     except sr.UnknownValueError:
-        print("Google Speech Recognition could not understand audio")
+        raise ("Google Speech Recognition could not understand audio", sys)
     except sr.RequestError as e:
-        print("Could not request results from Google Speech Recognition service; {0}".format(e))
+        raise customexception("Could not request results from Google Speech Recognition service; {0}".format(e), sys)
+    except Exception as e:
+        raise customexception(e, sys)
 
 def save_output(output_data, output_dir="artifacts"):
-    Path(output_dir).mkdir(exist_ok=True)
-    
-    # Save text response
-    with open(f"{output_dir}/response.txt", "w") as f:
-        f.write(output_data['response_text'])
-    logging.info("Generated response stored in response.txt file in artifacts folder.")
+    try:
+        Path(output_dir).mkdir(exist_ok=True)
+        
+        # Save text response
+        with open(f"{output_dir}/response.txt", "w") as f:
+            f.write(output_data['response_text'])
+        logging.info("Generated response stored in response.txt file in artifacts folder.")
+    except Exception as e:
+        raise customexception(e,sys)
 
-def load_output(output_dir="artifacts"):   
-    # Load text response
-    with open(f"{output_dir}/response.txt", "r") as f:
-        answer = f.read()
-    logging.info("Stored text loaded.")
+def load_output(output_dir="artifacts"): 
+    try:  
+        # Load text response
+        with open(f"{output_dir}/response.txt", "r") as f:
+            answer = f.read()
+        logging.info("Stored text loaded.")
 
-    return answer
+        return answer
+    except Exception as e:
+        raise customexception(e,sys)