Upload app.py

app.py

@@ -1,91 +1,8 @@
-# # -*- coding: utf-8 -*-
-# """fiver-app.ipynb
-
-# Automatically generated by Colaboratory.
-
-# Original file is located at
-#     https://colab.research.google.com/drive/1YQm_fGxa2nfiV8pTN4oBrlzzfefGadaP
-# """
-
-# !pip uninstall -y numpy
-# !pip install --ignore-installed numpy==1.22.0
-
-# !pip install langchain
-# !pip install PyPDF2
-# !pip install docx2txt
-# !pip install gradio
-# !pip install faiss-gpu
-# !pip install openai
-# !pip install tiktoken
-# !pip install python-docx
-
-# !pip install git+https://github.com/openai/whisper.git
-# !pip install sounddevice
-
-# import shutil
-# import os
-
-# def copy_files(source_folder, destination_folder):
-#     # Create the destination folder if it doesn't exist
-#     if not os.path.exists(destination_folder):
-#         os.makedirs(destination_folder)
-
-#     # Get a list of files in the source folder
-
-#     files_to_copy = os.listdir(source_folder)
-#     for file_name in files_to_copy:
-#         source_file_path = os.path.join(source_folder, file_name)
-#         destination_file_path = os.path.join(destination_folder, file_name)
-
-#         # Copy the file to the destination folder
-#         shutil.copy(source_file_path, destination_file_path)
-
-#         print(f"Copied {file_name} to {destination_folder}")
-
-# # Specify the source folder and destination folder paths
-# source_folder = "/kaggle/input/fiver-app5210"
-# destination_folder = "/home/user/app/local_db"
-
-# copy_files(source_folder, destination_folder)
-
-# import shutil
-# import os
-
-# def copy_files(source_folder, destination_folder):
-#     # Create the destination folder if it doesn't exist
-#     if not os.path.exists(destination_folder):
-#         os.makedirs(destination_folder)
-
-#     # Get a list of files in the source folder
-#     files_to_copy = os.listdir(source_folder)
-
-#     for file_name in files_to_copy:
-#         source_file_path = os.path.join(source_folder, file_name)
-#         destination_file_path = os.path.join(destination_folder, file_name)
-
-#         # Copy the file to the destination folder
-#         shutil.copy(source_file_path, destination_file_path)
-
-#         print(f"Copied {file_name} to {destination_folder}")
-
-# # Specify the source folder and destination folder paths
-# source_folder = "/kaggle/input/fiver-app-docs"
-# destination_folder = "/home/user/app/docs"
-
-# copy_files(source_folder, destination_folder)
-
-
-def api_key(key):
-    import os
-    import openai
-
-    os.environ["TOKENIZERS_PARALLELISM"] = "false"
-    os.environ["OPENAI_API_KEY"] = key
-    openai.api_key = key
-
-    return "Successful!"
-
+import os
+import openai
 
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+os.environ["OPENAI_API_KEY"] 
 def save_file(input_file):
     import shutil
     import os
@@ -93,14 +10,13 @@ def save_file(input_file):
     destination_dir = "/home/user/app/file/"
     os.makedirs(destination_dir, exist_ok=True)
 
-    output_dir = "/home/user/app/file/"
+    output_dir="/home/user/app/file/"
 
     for file in input_file:
-        shutil.copy(file.name, output_dir)
+      shutil.copy(file.name, output_dir)
 
     return "File(s) saved successfully!"
 
-
 def process_file():
     from langchain.document_loaders import PyPDFLoader
     from langchain.document_loaders import DirectoryLoader
@@ -108,30 +24,26 @@ def process_file():
     from langchain.document_loaders import Docx2txtLoader
     from langchain.vectorstores import FAISS
     from langchain.embeddings.openai import OpenAIEmbeddings
-    from langchain.text_splitter import CharacterTextSplitter
+    from langchain.text_splitter import RecursiveCharacterTextSplitter
     import openai
 
-    loader1 = DirectoryLoader(
-        "/home/user/app/file/", glob="./*.pdf", loader_cls=PyPDFLoader
-    )
+    loader1 = DirectoryLoader('/home/user/app/file/', glob="./*.pdf", loader_cls=PyPDFLoader)
     document1 = loader1.load()
 
-    loader2 = DirectoryLoader(
-        "/home/user/app/file/", glob="./*.txt", loader_cls=TextLoader
-    )
+    loader2 = DirectoryLoader('/home/user/app/file/', glob="./*.txt", loader_cls=TextLoader)
     document2 = loader2.load()
 
-    loader3 = DirectoryLoader(
-        "/home/user/app/file/", glob="./*.docx", loader_cls=Docx2txtLoader
-    )
+    loader3 = DirectoryLoader('/home/user/app/file/', glob="./*.docx", loader_cls=Docx2txtLoader)
     document3 = loader3.load()
 
     document1.extend(document2)
     document1.extend(document3)
 
-    text_splitter = CharacterTextSplitter(
-        separator="\n", chunk_size=1000, chunk_overlap=200, length_function=len
-    )
+    text_splitter = RecursiveCharacterTextSplitter(
+        separator="\n",
+        chunk_size=1000,
+        chunk_overlap=200,
+        length_function=len)
 
     docs = text_splitter.split_documents(document1)
     embeddings = OpenAIEmbeddings()
@@ -141,16 +53,14 @@ def process_file():
 
     return "File(s) processed successfully!"
 
-
 def formatted_response(docs, response):
     formatted_output = response + "\n\nSources"
 
     for i, doc in enumerate(docs):
-        source_info = doc.metadata.get("source", "Unknown source")
-        page_info = doc.metadata.get("page", None)
+        source_info = doc.metadata.get('source', 'Unknown source')
+        page_info = doc.metadata.get('page', None)
 
-        # Get the file name without the directory path
-        file_name = source_info.split("/")[-1].strip()
+        file_name = source_info.split('/')[-1].strip()
 
         if page_info is not None:
             formatted_output += f"\n{file_name}\tpage no {page_info}"
@@ -159,7 +69,6 @@ def formatted_response(docs, response):
 
     return formatted_output
 
-
 def search_file(question):
     from langchain.embeddings.openai import OpenAIEmbeddings
     from langchain.vectorstores import FAISS
@@ -173,16 +82,16 @@ def search_file(question):
     file_db = FAISS.load_local("/home/user/app/file_db/", embeddings)
     docs = file_db.similarity_search(question)
 
-    llm = ChatOpenAI(model_name="gpt-3.5-turbo")
+    llm = ChatOpenAI(model_name='gpt-3.5-turbo')
     chain = load_qa_chain(llm, chain_type="stuff")
+
     with get_openai_callback() as cb:
         response = chain.run(input_documents=docs, question=question)
         print(cb)
 
     return formatted_response(docs, response)
 
-
-def search_local(question):
+def local_search(question):
     from langchain.embeddings.openai import OpenAIEmbeddings
     from langchain.vectorstores import FAISS
     from langchain.chains.question_answering import load_qa_chain
@@ -195,18 +104,17 @@ def search_local(question):
     file_db = FAISS.load_local("/home/user/app/local_db/", embeddings)
     docs = file_db.similarity_search(question)
 
-    print(docs)
-    type(docs)
-    llm = ChatOpenAI(model_name="gpt-3.5-turbo")
+    llm = ChatOpenAI(model_name='gpt-3.5-turbo')
     chain = load_qa_chain(llm, chain_type="stuff")
+
     with get_openai_callback() as cb:
         response = chain.run(input_documents=docs, question=question)
         print(cb)
 
     return formatted_response(docs, response)
 
-
 def delete_file():
+
     import shutil
 
     path1 = "/home/user/app/file/"
@@ -220,34 +128,46 @@ def delete_file():
     except:
         return "Already Deleted"
 
+def soap_refresh():
+    import os
+    import gradio as gr
 
-import os
-import gradio as gr
-
+    destination_folder = "/home/user/app/soap_docs/"
+    if not os.path.exists(destination_folder):
+        os.makedirs(destination_folder)
 
-def list_files():
-    directory = "/home/user/app/docs"
+    directory = '/home/user/app/soap_docs/'
     file_list = []
+
     for root, dirs, files in os.walk(directory):
         for file in files:
             file_list.append(file)
     return gr.Dropdown.update(choices=file_list)
 
+def sbar_refresh():
+    import os
+    import gradio as gr
 
-file_list = list_files()
+    destination_folder = "/home/user/app/sbar_docs/"
+    if not os.path.exists(destination_folder):
+        os.makedirs(destination_folder)
 
-print("List of file names in the directory:")
-for file_name in file_list:
-    print(file_name)
+    directory = '/home/user/app/sbar_docs/'
+    file_list = []
 
+    for root, dirs, files in os.walk(directory):
+        for file in files:
+            file_list.append(file)
+    return gr.Dropdown.update(choices=file_list)
 
-def soap_report(doc_name, question):
+def ask_soap(doc_name, question):
     from langchain.llms import OpenAI
     from langchain import PromptTemplate, LLMChain
+    from langchain.chat_models import ChatOpenAI
     import openai
     import docx
 
-    docx_path = "/home/user/app/docs/" + doc_name
+    docx_path = "/home/user/app/soap_docs/" + doc_name
 
     doc = docx.Document(docx_path)
     extracted_text = "Extracted text:\n\n\n"
@@ -270,52 +190,88 @@ def soap_report(doc_name, question):
     Answer: Let's think step by step."""
 
     prompt = PromptTemplate(template=template, input_variables=["question"])
-    llm = OpenAI()
+
+    llm = ChatOpenAI(model_name="gpt-3.5-turbo")
     llm_chain = LLMChain(prompt=prompt, llm=llm)
     response = llm_chain.run(extracted_text)
 
     return response
 
+def ask_sbar(doc_name, question):
+    from langchain.llms import OpenAI
+    from langchain import PromptTemplate, LLMChain
+    from langchain.chat_models import ChatOpenAI
+    import openai
+    import docx
+
+    docx_path = "/home/user/app/sbar_docs/" + doc_name
+
+    doc = docx.Document(docx_path)
+    extracted_text = "Extracted text:\n\n\n"
+
+    for paragraph in doc.paragraphs:
+        extracted_text += paragraph.text + "\n"
+
+    question = (
+        "\n\nUse the 'Extracted text' to answer the following question:\n" + question
+    )
+    extracted_text += question
+
+    if extracted_text:
+        print(extracted_text)
+    else:
+        print("failed")
+
+    template = """Question: {question}
+
+    Answer: Let's think step by step."""
+
+    prompt = PromptTemplate(template=template, input_variables=["question"])
+
+    llm = ChatOpenAI(model_name="gpt-3.5-turbo")
+    llm_chain = LLMChain(prompt=prompt, llm=llm)
+    response = llm_chain.run(extracted_text)
+
+    return response
 
 def search_gpt(question):
     from langchain.llms import OpenAI
     from langchain import PromptTemplate, LLMChain
+    from langchain.chat_models import ChatOpenAI
 
     template = """Question: {question}
 
     Answer: Let's think step by step."""
 
     prompt = PromptTemplate(template=template, input_variables=["question"])
-    llm = OpenAI()
+
+    llm = ChatOpenAI(model_name="gpt-3.5-turbo")
     llm_chain = LLMChain(prompt=prompt, llm=llm)
     response = llm_chain.run(question)
 
     return response
 
-
 def local_gpt(question):
     from langchain.llms import OpenAI
     from langchain import PromptTemplate, LLMChain
+    from langchain.chat_models import ChatOpenAI
 
     template = """Question: {question}
 
     Answer: Let's think step by step."""
 
     prompt = PromptTemplate(template=template, input_variables=["question"])
-    llm = OpenAI()
+
+    llm = ChatOpenAI(model_name="gpt-3.5-turbo")
     llm_chain = LLMChain(prompt=prompt, llm=llm)
     response = llm_chain.run(question)
 
     return response
 
-
 global output
-global response
-
 
 def audio_text(filepath):
     import openai
-
     global output
 
     audio = open(filepath, "rb")
@@ -324,12 +280,15 @@ def audio_text(filepath):
 
     return output
 
+global soap_response
+global sbar_response
 
-def transcript(text):
+def transcript_soap(text):
     from langchain.llms import OpenAI
     from langchain import PromptTemplate, LLMChain
+    from langchain.chat_models import ChatOpenAI
 
-    global response
+    global soap_response
 
     question = (
         "Use the following context given below to generate a detailed SOAP Report:\n\n"
@@ -341,20 +300,60 @@ def transcript(text):
 
     Answer: Let's think step by step."""
 
+    word_count = len(text.split())
     prompt = PromptTemplate(template=template, input_variables=["question"])
-    llm = OpenAI()
+
+    if word_count < 2000:
+        llm = ChatOpenAI(model="gpt-3.5-turbo")
+    elif word_count < 5000:
+        llm = ChatOpenAI(model="gpt-4")
+    else:
+        llm = ChatOpenAI(model="gpt-4-32k")
+
     llm_chain = LLMChain(prompt=prompt, llm=llm)
-    response = llm_chain.run(question)
+    soap_response = llm_chain.run(question)
 
-    return response
+    return soap_response
+
+def transcript_sbar(text):
+    from langchain.llms import OpenAI
+    from langchain import PromptTemplate, LLMChain
+    from langchain.chat_models import ChatOpenAI
+
+    global sbar_response
 
+    question = (
+        "Use the following context given below to generate a detailed SBAR Report:\n\n"
+    )
+    question += text
+    print(question)
+
+    template = """Question: {question}
+
+    Answer: Let's think step by step."""
+
+    word_count = len(text.split())
+    prompt = PromptTemplate(template=template, input_variables=["question"])
+
+    if word_count < 2000:
+        llm = ChatOpenAI(model="gpt-3.5-turbo")
+    elif word_count < 5000:
+        llm = ChatOpenAI(model="gpt-4")
+    else:
+        llm = ChatOpenAI(model="gpt-4-32k")
+
+    llm_chain = LLMChain(prompt=prompt, llm=llm)
+    sbar_response = llm_chain.run(question)
+
+    return sbar_response
 
 def text_soap():
     from langchain.llms import OpenAI
     from langchain import PromptTemplate, LLMChain
+    from langchain.chat_models import ChatOpenAI
 
     global output
-    global response
+    global soap_response
     output = output
 
     question = (
@@ -367,31 +366,90 @@ def text_soap():
 
     Answer: Let's think step by step."""
 
+    word_count = len(output.split())
     prompt = PromptTemplate(template=template, input_variables=["question"])
-    llm = OpenAI()
+
+    if word_count < 2000:
+        llm = ChatOpenAI(model="gpt-3.5-turbo")
+    elif word_count < 5000:
+        llm = ChatOpenAI(model="gpt-4")
+    else:
+        llm = ChatOpenAI(model="gpt-4-32k")
+
     llm_chain = LLMChain(prompt=prompt, llm=llm)
-    response = llm_chain.run(question)
+    soap_response = llm_chain.run(question)
 
-    return response
+    return soap_response
+
+def text_sbar():
+    from langchain.llms import OpenAI
+    from langchain import PromptTemplate, LLMChain
+    from langchain.chat_models import ChatOpenAI
+
+    global output
+    global sbar_response
+    output = output
+
+    question = (
+        "Use the following context given below to generate a detailed SBAR Report:\n\n"
+    )
+    question += output
+    print(question)
 
+    template = """Question: {question}
+
+    Answer: Let's think step by step."""
 
-global path
+    word_count = len(output.split())
+    prompt = PromptTemplate(template=template, input_variables=["question"])
 
+    if word_count < 2000:
+        llm = ChatOpenAI(model="gpt-3.5-turbo")
+    elif word_count < 5000:
+        llm = ChatOpenAI(model="gpt-4")
+    else:
+        llm = ChatOpenAI(model="gpt-4-32k")
+
+    llm_chain = LLMChain(prompt=prompt, llm=llm)
+    sbar_response = llm_chain.run(question)
 
-def docx(name):
-    global response
-    response = response
+    return sbar_response
+
+def soap_docx(name):
+    global soap_response
+    soap_response = soap_response
     import docx
+    import os
+
+    destination_folder = "/home/user/app/soap_docs/"
+    if not os.path.exists(destination_folder):
+        os.makedirs(destination_folder)
 
-    global path
-    path = f"/home/user/app/docs/{name}.docx"
+    path = f"/home/user/app/soap_docs/SOAP_{name}.docx"
 
     doc = docx.Document()
-    doc.add_paragraph(response)
+    doc.add_paragraph(soap_response)
     doc.save(path)
 
     return "Successfully saved .docx File"
 
+def sbar_docx(name):
+    global sbar_response
+    sbar_response = sbar_response
+    import docx
+    import os
+
+    destination_folder = "/home/user/app/sbar_docs/"
+    if not os.path.exists(destination_folder):
+        os.makedirs(destination_folder)
+
+    path = f"/home/user/app/sbar_docs/SBAR_{name}.docx"
+
+    doc = docx.Document()
+    doc.add_paragraph(sbar_response)
+    doc.save(path)
+
+    return "Successfully saved .docx File"
 
 import gradio as gr
 
@@ -407,111 +465,167 @@ css = """
 """
 
 with gr.Blocks(css=css) as demo:
-    gr.Markdown("File Chatting App")
-
-    with gr.Tab("Chat with Files"):
-        with gr.Column(elem_classes="col"):
-            with gr.Tab("Upload and Process Files"):
-                with gr.Column():
-                    api_key_input = gr.Textbox(label="Enter API Key here")
-                    api_key_button = gr.Button("Submit")
-                    api_key_output = gr.Textbox(label="Output")
-
-                    file_input = gr.Files(label="Upload File(s) here")
-                    upload_button = gr.Button("Upload")
-                    file_output = gr.Textbox(label="Output")
-
-                    process_button = gr.Button("Process")
-                    process_output = gr.Textbox(label="Output")
-
-            with gr.Tab("Ask Questions to Files"):
-                with gr.Column():
-                    search_input = gr.Textbox(label="Enter Question here")
-                    search_button = gr.Button("Search")
-                    search_output = gr.Textbox(label="Output")
-
-                    search_gpt_button = gr.Button("Ask ChatGPT")
-                    search_gpt_output = gr.Textbox(label="Output")
-
-                    delete_button = gr.Button("Delete")
-                    delete_output = gr.Textbox(label="Output")
-
-    with gr.Tab("Chat with Local Files"):
-        with gr.Column(elem_classes="col"):
-            local_search_input = gr.Textbox(label="Enter Question here")
-            local_search_button = gr.Button("Search")
-            local_search_output = gr.Textbox(label="Output")
-
-            local_gpt_button = gr.Button("Ask ChatGPT")
-            local_gpt_output = gr.Textbox(label="Output")
-
-    with gr.Tab("Ask Question to SOAP Report"):
-        with gr.Column(elem_classes="col"):
-            refresh_button = gr.Button("Refresh")
-            soap_input = gr.Dropdown(label="Choose File")
-            soap_question = gr.Textbox(label="Enter Question here")
-            soap_button = gr.Button("Submit")
-            soap_output = gr.Textbox(label="Output")
-
-    with gr.Tab("Convert Audio to SOAP Report"):
-        with gr.Column(elem_classes="col"):
-            mic_text_input = gr.Audio(
-                source="microphone", type="filepath", label="Speak to the Microphone"
-            )
-            mic_text_button = gr.Button("Generate Transcript")
-            mic_text_output = gr.Textbox(label="Output")
-
-            upload_text_input = gr.Audio(
-                source="upload", type="filepath", label="Upload Audio File here"
-            )
-            upload_text_button = gr.Button("Generate Transcript")
-            upload_text_output = gr.Textbox(label="Output")
-
-            transcript_input = gr.Textbox(label="Enter Transcript here")
-            transcript_button = gr.Button("Generate SOAP Report")
-            transcript_output = gr.Textbox(label="Output")
-
-            text_soap_button = gr.Button("Generate SOAP Report")
-            text_soap_output = gr.Textbox(label="Output")
-
-            docx_input = gr.Textbox(label="Enter the name of .docx File")
-            docx_button = gr.Button("Save .docx File")
-            docx_output = gr.Textbox(label="Output")
-
-    api_key_button.click(api_key, inputs=api_key_input, outputs=api_key_output)
-
-    upload_button.click(save_file, inputs=file_input, outputs=file_output)
-
-    process_button.click(process_file, inputs=None, outputs=process_output)
-
-    search_button.click(search_file, inputs=search_input, outputs=search_output)
-    search_gpt_button.click(search_gpt, inputs=search_input, outputs=search_gpt_output)
-
-    delete_button.click(delete_file, inputs=None, outputs=delete_output)
-
-    local_search_button.click(
-        search_local, inputs=local_search_input, outputs=local_search_output
-    )
-    local_gpt_button.click(
-        local_gpt, inputs=local_search_input, outputs=local_gpt_output
-    )
+    gr.Markdown('<div class="centered">## Medical App</div>')
 
-    refresh_button.click(list_files, inputs=None, outputs=soap_input)
-    soap_button.click(
-        soap_report, inputs=[soap_input, soap_question], outputs=soap_output
-    )
+    with gr.Tab("SOAP and SBAR Note Creation"):
+      with gr.Column(elem_classes="col"):
 
-    mic_text_button.click(audio_text, inputs=mic_text_input, outputs=mic_text_output)
-    upload_text_button.click(
-        audio_text, inputs=upload_text_input, outputs=upload_text_output
-    )
+        with gr.Tab("From Recorded Audio"):
+          with gr.Column():
 
-    transcript_button.click(
-        transcript, inputs=transcript_input, outputs=transcript_output
-    )
-    text_soap_button.click(text_soap, inputs=None, outputs=text_soap_output)
-    docx_button.click(docx, inputs=docx_input, outputs=docx_output)
+            mic_audio_input = gr.Audio(source="microphone", type="filepath", label="Speak to the Microphone")
+            mic_audio_button = gr.Button("Generate Transcript")
+            mic_audio_output = gr.Textbox(label="Output")
+
+            mic_text_soap_button = gr.Button("Generate SOAP Report")
+            mic_text_soap_output = gr.Textbox(label="Output")
+            mic_text_sbar_button = gr.Button("Generate SBAR Report")
+            mic_text_sbar_output = gr.Textbox(label="Output")
+
+            mic_docx_input = gr.Textbox(label="Enter the name of .docx File")
+            mic_soap_docx_button = gr.Button("Save SOAP .docx File")
+            mic_soap_docx_output = gr.Textbox(label="Output")
+            mic_sbar_docx_button = gr.Button("Save SBAR .docx File")
+            mic_sbar_docx_output = gr.Textbox(label="Output")
+
+        with gr.Tab("From Uploaded Audio"):
+          with gr.Column():
+
+            upload_audio_input = gr.Audio(source="upload", type="filepath", label="Upload Audio File here")
+            upload_audio_button = gr.Button("Generate Transcript")
+            upload_audio_output = gr.Textbox(label="Output")
+
+            upload_text_soap_button = gr.Button("Generate SOAP Report")
+            upload_text_soap_output = gr.Textbox(label="Output")
+            upload_text_sbar_button = gr.Button("Generate SBAR Report")
+            upload_text_sbar_output = gr.Textbox(label="Output")
+
+            upload_docx_input = gr.Textbox(label="Enter the name of .docx File")
+            upload_soap_docx_button = gr.Button("Save SOAP .docx File")
+            upload_soap_docx_output = gr.Textbox(label="Output")
+            upload_sbar_docx_button = gr.Button("Save SBAR .docx File")
+            upload_sbar_docx_output = gr.Textbox(label="Output")
+
+        with gr.Tab("From Text Transcript"):
+          with gr.Column():
+
+            text_transcript_input = gr.Textbox(label="Enter your Transcript here")
+
+            text_text_soap_button = gr.Button("Generate SOAP Report")
+            text_text_soap_output = gr.Textbox(label="Output")
+            text_text_sbar_button = gr.Button("Generate SBAR Report")
+            text_text_sbar_output = gr.Textbox(label="Output")
+
+            text_docx_input = gr.Textbox(label="Enter the name of .docx File")
+            text_soap_docx_button = gr.Button("Save SOAP .docx File")
+            text_soap_docx_output = gr.Textbox(label="Output")
+            text_sbar_docx_button = gr.Button("Save SBAR .docx File")
+            text_sbar_docx_output = gr.Textbox(label="Output")
+
+    with gr.Tab("SOAP and SBAR Queries"):
+      with gr.Column(elem_classes="col"):
+
+        with gr.Tab("Query SOAP Reports"):
+          with gr.Column():
+
+            soap_refresh_button = gr.Button("Refresh")
+            ask_soap_input = gr.Dropdown(label="Choose File")
+
+            ask_soap_question = gr.Textbox(label="Enter Question here")
+            ask_soap_button = gr.Button("Submit")
+            ask_soap_output = gr.Textbox(label="Output")
+
+        with gr.Tab("Query SBAR Reports"):
+          with gr.Column():
 
+            sbar_refresh_button = gr.Button("Refresh")
+            ask_sbar_input = gr.Dropdown(label="Choose File")
+
+            ask_sbar_question = gr.Textbox(label="Enter Question here")
+            ask_sbar_button = gr.Button("Submit")
+            ask_sbar_output = gr.Textbox(label="Output")
+
+    with gr.Tab("All Queries"):
+      with gr.Column(elem_classes="col"):
+
+        local_search_input = gr.Textbox(label="Enter Question here")
+        local_search_button = gr.Button("Search")
+        local_search_output = gr.Textbox(label="Output")
+
+        local_gpt_button = gr.Button("Ask ChatGPT")
+        local_gpt_output = gr.Textbox(label="Output")
+
+
+    with gr.Tab("Documents Queries"):
+      with gr.Column(elem_classes="col"):
+
+        with gr.Tab("Upload and Process Documents"):
+          with gr.Column():
+
+            file_upload_input = gr.Files(label="Upload File(s) here")
+            file_upload_button = gr.Button("Upload")
+            file_upload_output = gr.Textbox(label="Output")
+
+            file_process_button = gr.Button("Process")
+            file_process_output = gr.Textbox(label="Output")
+
+        with gr.Tab("Query Documents"):
+          with gr.Column():
+
+            file_search_input = gr.Textbox(label="Enter Question here")
+            file_search_button = gr.Button("Search")
+            file_search_output = gr.Textbox(label="Output")
+
+            search_gpt_button = gr.Button("Ask ChatGPT")
+            search_gpt_output = gr.Textbox(label="Output")
+
+            file_delete_button = gr.Button("Delete")
+            file_delete_output = gr.Textbox(label="Output")
+
+    ######################################################################################################
+    file_upload_button.click(save_file, inputs=file_upload_input, outputs=file_upload_output)
+    file_process_button.click(process_file, inputs=None, outputs=file_process_output)
+
+    file_search_button.click(search_file, inputs=file_search_input, outputs=file_search_output)
+    search_gpt_button.click(search_gpt, inputs=file_search_input, outputs=search_gpt_output)
+
+    file_delete_button.click(delete_file, inputs=None, outputs=file_delete_output)
+
+    ######################################################################################################
+    local_search_button.click(local_search, inputs=local_search_input, outputs=local_search_output)
+    local_gpt_button.click(local_gpt, inputs=local_search_input, outputs=local_gpt_output)
+
+    #######################################################################################################
+    soap_refresh_button.click(soap_refresh, inputs=None, outputs=ask_soap_input)
+    ask_soap_button.click(ask_soap, inputs=[ask_soap_input, ask_soap_question], outputs=ask_soap_output)
+
+    sbar_refresh_button.click(sbar_refresh, inputs=None, outputs=ask_sbar_input)
+    ask_sbar_button.click(ask_sbar, inputs=[ask_sbar_input, ask_sbar_question], outputs=ask_sbar_output)
+
+    ####################################################################################################
+    mic_audio_button.click(audio_text, inputs=mic_audio_input, outputs=mic_audio_output)
+
+    mic_text_soap_button.click(text_soap, inputs=None, outputs=mic_text_soap_output)
+    mic_text_sbar_button.click(text_sbar, inputs=None, outputs=mic_text_sbar_output)
+
+    mic_soap_docx_button.click(soap_docx, inputs=mic_docx_input, outputs=mic_soap_docx_output)
+    mic_sbar_docx_button.click(sbar_docx, inputs=mic_docx_input, outputs=mic_sbar_docx_output)
+    ####################################################################################################
+    upload_audio_button.click(audio_text, inputs=upload_audio_input, outputs=upload_audio_output)
+
+    upload_text_soap_button.click(text_soap, inputs=None, outputs=upload_text_soap_output)
+    upload_text_sbar_button.click(text_sbar, inputs=None, outputs=upload_text_sbar_output)
+
+    upload_soap_docx_button.click(soap_docx, inputs=upload_docx_input, outputs=upload_soap_docx_output)
+    upload_sbar_docx_button.click(sbar_docx, inputs=upload_docx_input, outputs=upload_sbar_docx_output)
+    ###########################################################################################################
+    text_text_soap_button.click(transcript_soap, inputs=text_transcript_input, outputs=text_text_soap_output)
+    text_text_sbar_button.click(transcript_sbar, inputs=text_transcript_input, outputs=text_text_sbar_output)
+
+    text_soap_docx_button.click(soap_docx, inputs=text_docx_input, outputs=text_soap_docx_output)
+    text_sbar_docx_button.click(sbar_docx, inputs=text_docx_input, outputs=text_sbar_docx_output)
+    #############################################################################################################
 
 demo.queue()
 demo.launch()
+