Docchat removed

app.py

@@ -9,20 +9,20 @@ import speech_recognition as sr
 salutation = "Pleasure meeting you. Have a nice day!"
 
 # Page title
-st.title("Hi, Chatmate here!")
-st.markdown("<h3 style='text-align: center;'>Hello, I am your chatbot assistant.</h1>", unsafe_allow_html=True)
+st.markdown("<h1 style='text-align: center;'>Hi, Dr Bot Junior here.</h1>", unsafe_allow_html=True)
+st.markdown("<h3 style='text-align: center;'>I am your chatbot assistant.</h3>", unsafe_allow_html=True)
 
-mode = option_menu("Choose mode of interaction", ["Doc-Bot", "Text", "Voice"], 
-    icons=['heart-pulse','chat-text', 'mic'], 
+mode = option_menu("Choose mode of interaction", ["Text", "Voice"], 
+    icons=['heart-pulse', 'mic'], 
     menu_icon="cast", default_index=0, orientation="horizontal")
 
 if "HF_TOKEN" not in st.session_state:
     st.session_state.HF_TOKEN = ''
-st.write("Add your Huggingface Access Token to use the chatbot.")
-st.session_state.HF_TOKEN = st.text_input("Your Access Token: ")
+# st.write("Add your Huggingface Access Token to use the chatbot.")
+# st.session_state.HF_TOKEN = st.text_input("Your Access Token: ")
 
 # Chatbot configuration initiation
-if st.session_state.HF_TOKEN != '':
+if st.session_state.HF_TOKEN == '':
     avatar = AvatarSystem(st.session_state.HF_TOKEN)
 
 def chat_history(input, response, sentiment):
@@ -31,9 +31,9 @@ def chat_history(input, response, sentiment):
     st.session_state.history[input] = [response, sentiment]
     return st.session_state.history
 
-def response(input_text, docbot):
+def response(input_text):
     # Getting response and sentiment of response 
-    output = avatar.process_input(input_text, docbot)
+    output = avatar.process_input(input_text)
     # Save output response in txt
     save_output(output)
     response_sentiment = output['emotion']
@@ -41,44 +41,7 @@ def response(input_text, docbot):
         
     return ans, response_sentiment
 
-if mode == "Doc-Bot" and st.session_state.HF_TOKEN != '':
-    st.write("Doc-Bot implementation")
-   
-    if 'doc_chat_hist' not in st.session_state:
-        st.session_state.doc_chat_hist = 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'")
-        col1, col2, col3, col4, col5, col6 = st.columns(6)
-        save = col6.form_submit_button("Click here")
-        
-    if save and user_input != "":
-        user_input = user_input.lower() + '?'
-
-        # Exiting the chat
-        if 'exit' in user_input:
-            st.write(salutation)
-            play_speech(salutation)
-           
-        # Getting response and sentiment of response 
-        ans, senti = response(user_input, docbot=True)
-        
-        # Chat history 
-        st.session_state.doc_chat_hist = chat_history(user_input, ans, senti)
-    
-    # Chat history display
-    st.markdown("### Chat History: ")
-    with st.container(border=True):
-        for key in st.session_state.doc_chat_hist.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.doc_chat_hist[key][0])
-
-elif mode == "Text" and st.session_state.HF_TOKEN != '':    
+if mode == "Text" and st.session_state.HF_TOKEN == '':    
     if 'chathist' not in st.session_state:
         st.session_state.chathist = dict()
     
@@ -96,7 +59,7 @@ elif mode == "Text" and st.session_state.HF_TOKEN != '':
             st.write(salutation)
            
         # Getting response and sentiment of response 
-        ans, senti = response(user_input, docbot=False)
+        ans, senti = response(user_input)
         
         # Chat history 
         st.session_state.chathist = chat_history(user_input, ans, senti)
@@ -135,4 +98,42 @@ elif mode == "Voice" and st.session_state.HF_TOKEN != '':
                 break
         
             #Getting response and sentiment of response 
-            response(user_input, docbot=False)
+            response(user_input)
+
+
+# if mode == "Doc-Bot" and st.session_state.HF_TOKEN != '':
+#     st.write("Doc-Bot implementation")
+   
+#     if 'doc_chat_hist' not in st.session_state:
+#         st.session_state.doc_chat_hist = 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'")
+#         col1, col2, col3, col4, col5, col6 = st.columns(6)
+#         save = col6.form_submit_button("Click here")
+        
+#     if save and user_input != "":
+#         user_input = user_input.lower() + '?'
+
+#         # Exiting the chat
+#         if 'exit' in user_input:
+#             st.write(salutation)
+#             play_speech(salutation)
+           
+#         # Getting response and sentiment of response 
+#         ans, senti = response(user_input, docbot=True)
+        
+#         # Chat history 
+#         st.session_state.doc_chat_hist = chat_history(user_input, ans, senti)
+    
+#     # Chat history display
+#     st.markdown("### Chat History: ")
+#     with st.container(border=True):
+#         for key in st.session_state.doc_chat_hist.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.doc_chat_hist[key][0])

artifacts/Docmate.ipynb

@@ -821,84 +821,69 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Collecting streamlit-TTS\n",
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-      "Requirement already satisfied: rpds-py>=0.7.1 in c:\\users\\umesh\\anaconda3\\envs\\genai\\lib\\site-packages (from jsonschema->altair<6,>=4.0->streamlit>=0.63->streamlit-TTS) (0.10.6)\n",
-      "Using cached streamlit_TTS-0.0.7-py3-none-any.whl (446 kB)\n",
-      "Installing collected packages: streamlit-TTS\n",
-      "Successfully installed streamlit-TTS-0.0.7\n",
-      "Note: you may need to restart the kernel to use updated packages.\n"
+      "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"
      ]
     },
     {
-     "name": "stderr",
+     "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`)"
+     ]
+    }
+   ],
+   "source": [
+    "# Use a pipeline as a high-level helper\n",
+    "from transformers import pipeline\n",
+    "\n",
+    "pipe = pipeline(\"text-generation\", model=\"TheBloke/meditron-7B-chat-AWQ\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n",
-      "[notice] A new release of pip is available: 24.2 -> 24.3.1\n",
-      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
+      "^C\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
      ]
     }
    ],
    "source": [
-    "pip install streamlit-TTS"
+    "pip install autoawq"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "GenAI",
+   "display_name": "DeepLearning",
    "language": "python",
    "name": "python3"
   },
@@ -912,7 +897,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,

artifacts/response.txt

@@ -1,3 +1,3 @@
 
 
-I am a 25 year old woman from the Netherlands. I have been working as a software developer for 4 years now, and I am currently working at a company that specializes in developing software for the healthcare industry. In my free time, I enjoy playing video games, reading books, and spending time with my friends and family
+I'm doing well, thank you! How about you

src/components/avatarsys.py

@@ -10,7 +10,6 @@ class AvatarConfig:
         self.image_size = 512
         self.voice_sample_rate = 22050
         self.max_text_length = 512
-        # self.emotion_categories = ['negative','neutral', 'positive']
 
 class AvatarSystem:
     def __init__(self, hf_token):
@@ -18,18 +17,15 @@ class AvatarSystem:
         self.text_processor = TextProcessor(hf_token)
         self.emotion_analyzer = EmotionAnalyzer()
         self.voice_synthesiser = VoiceSynthesizer()
-        self.doc_chat_processor = DocChatProcessor(hf_token)
+        # self.doc_chat_processor = DocChatProcessor(hf_token)
     
     logging.info("Avatar system initiated.")
 
-    def process_input(self, user_input, docbot):
+    def process_input(self, user_input):
         # Generate response
-        if docbot:
-            response = self.doc_chat_processor.generate_response(user_input)
-            logging.info("Docbot Text response generated.")
-        else:
-            response = self.text_processor.generate_response(user_input)
-            logging.info("Text response generated.")
+        
+        response = self.text_processor.generate_response(user_input)
+        logging.info("Text response generated.")
 
         # Analyze emotion
         emotion = self.emotion_analyzer.analyze_emotion(response)

src/components/docchat.py

@@ -9,7 +9,8 @@ from langchain_huggingface import HuggingFaceEndpoint
 # Text generation model
 # repo_id="Laim/Llama-3.1-MedPalm2-imitate-8B-Instruct"
 # repo_id="Joycean0301/Llama-3.2-3B-Instruct-Medical-Conversational"
-repo_id = "TheBloke/medalpaca-13B-GGML"
+# repo_id = "TheBloke/medalpaca-13B-GGML"
+repo_id="mistralai/Mistral-7B-Instruct-v0.3"
 
 class DocChatProcessor:
     def __init__(self, hf_token):

src/components/textprocess.py

@@ -21,7 +21,7 @@ class TextProcessor:
                                     temperature=0.01,  
                                     repetition_penalty=1.03,  
                                     streaming=False,  
-                                    huggingfacehub_api_token= hf_token,
+                                    # huggingfacehub_api_token= hf_token,
                                     stop_sequences=['?', '</s>', '.\n\n']  
                                 )