Update app.py

app.py

@@ -34,8 +34,8 @@ from xml.etree import ElementTree as ET
 import streamlit.components.v1 as components  # Import Streamlit Components for HTML5
 
 
-st.set_page_config(page_title="🐪Llama Whisperer🦙 Voice Chat🌟", layout="wide")
-
+st.set_page_config(page_title="🐪Llama🦙Whisperer", layout="wide")
+st.markdown('(Inference Endpoints)[https://ui.endpoints.huggingface.co/awacke1/endpoints]')
 
 def add_Med_Licensing_Exam_Dataset():
     import streamlit as st
@@ -92,9 +92,9 @@ def add_Med_Licensing_Exam_Dataset():
 # 1. Constants and Top Level UI Variables
 
 # My Inference API Copy
-# API_URL = 'https://qe55p8afio98s0u3.us-east-1.aws.endpoints.huggingface.cloud'  # Dr Llama
+API_URL = 'https://qe55p8afio98s0u3.us-east-1.aws.endpoints.huggingface.cloud'  # Dr Llama
 # Original:
-API_URL = "https://api-inference.huggingface.co/models/meta-llama/Llama-2-7b-chat-hf"
+#API_URL = "https://api-inference.huggingface.co/models/meta-llama/Llama-2-7b-chat-hf"
 API_KEY = os.getenv('API_KEY')
 MODEL1="meta-llama/Llama-2-7b-chat-hf"
 MODEL1URL="https://huggingface.co/meta-llama/Llama-2-7b-chat-hf"
@@ -104,7 +104,7 @@ headers = {
     "Content-Type": "application/json"
 }
 key = os.getenv('OPENAI_API_KEY')
-prompt = f"Write instructions to teach discharge planning along with guidelines and patient education. List entities, features and relationships to CCDA and FHIR objects in boldface."
+prompt = f"Write instructions to teach anyone to write a discharge plan. List the entities, features and relationships to CCDA and FHIR objects in boldface."
 should_save = st.sidebar.checkbox("💾 Save", value=True, help="Save your session data.")
 
 # 2. Prompt label button demo for LLM
@@ -128,30 +128,30 @@ def add_witty_humor_buttons():
         col1, col2, col3 = st.columns([1, 1, 1], gap="small")
         
         # Add buttons to columns
-        if col1.button("Wise Limericks 😂"):
+        if col1.button("Generate Limericks 😂"):
             StreamLLMChatResponse(descriptions["Generate Limericks 😂"])
         
         if col2.button("Wise Quotes 🧙"):
             StreamLLMChatResponse(descriptions["Wise Quotes 🧙"])
         
-        #if col3.button("Funny Rhymes 🎤"):
-        #    StreamLLMChatResponse(descriptions["Funny Rhymes 🎤"])
+        if col3.button("Funny Rhymes 🎤"):
+            StreamLLMChatResponse(descriptions["Funny Rhymes 🎤"])
         
         col4, col5, col6 = st.columns([1, 1, 1], gap="small")
         
-        if col4.button("Top Ten Funniest Clean Jokes 💉"):
-            StreamLLMChatResponse(descriptions["Top Ten Funniest Clean Jokes  💉"])
+        if col4.button("Medical Jokes 💉"):
+            StreamLLMChatResponse(descriptions["Medical Jokes 💉"])
         
         if col5.button("Minnesota Humor ❄️"):
             StreamLLMChatResponse(descriptions["Minnesota Humor ❄️"])
         
-        if col6.button("Origins of Medical Science True Stories"):
-            StreamLLMChatResponse(descriptions["Origins of Medical Science True Stories"])
+        if col6.button("Top Funny Stories 📖"):
+            StreamLLMChatResponse(descriptions["Top Funny Stories 📖"])
         
         col7 = st.columns(1, gap="small")
         
-        if col7[0].button("Top Ten Best Write a streamlit python program prompts to build AI programs. 🎙️"):
-            StreamLLMChatResponse(descriptions["Top Ten Best Write a streamlit python program prompts to build AI programs. 🎙️"])
+        if col7[0].button("More Funny Rhymes 🎙️"):
+            StreamLLMChatResponse(descriptions["More Funny Rhymes 🎙️"])
 
 def SpeechSynthesis(result):
     documentHTML5='''
@@ -180,7 +180,7 @@ def SpeechSynthesis(result):
     </html>
     '''
 
-    components.html(documentHTML5, width=1280, height=500)
+    components.html(documentHTML5, width=1280, height=1024)
     #return result
 
 
@@ -191,7 +191,6 @@ def StreamLLMChatResponse(prompt):
         endpoint_url = API_URL
         hf_token = API_KEY
         client = InferenceClient(endpoint_url, token=hf_token)
-        st.write('Opened HF hub Inference Client for endpoint URL: ' + endpoint_url)
         gen_kwargs = dict(
             max_new_tokens=512,
             top_k=30,
@@ -227,20 +226,11 @@ def StreamLLMChatResponse(prompt):
     except:
         st.write('Llama model is asleep. Starting up now on A10 - please give 5 minutes then retry as KEDA scales up from zero to activate running container(s).')
 
-#def query(filename):
-#    with open(filename, "rb") as f:
-#        data = f.read
-#    st.write('Posting request to model ' + API_URL_IE)
-#    response = requests.post(API_URL_IE, headers=headers, data=data)
-#    return response.json()
-    
 # 4. Run query with payload
 def query(payload):
-    st.write('Posting request to model ' + API_URL_IE)
     response = requests.post(API_URL, headers=headers, json=payload)
     st.markdown(response.json())
     return response.json()
-    
 def get_output(prompt):
     return query({"inputs": prompt})
 
@@ -249,8 +239,7 @@ def generate_filename(prompt, file_type):
     central = pytz.timezone('US/Central')
     safe_date_time = datetime.now(central).strftime("%m%d_%H%M")
     replaced_prompt = prompt.replace(" ", "_").replace("\n", "_")
-    safe_prompt = "".join(x for x in replaced_prompt if x.isalnum() or x == "_")[:255]  # 255 is linux max, 260 is windows max
-    #safe_prompt = "".join(x for x in replaced_prompt if x.isalnum() or x == "_")[:45]
+    safe_prompt = "".join(x for x in replaced_prompt if x.isalnum() or x == "_")[:45]
     return f"{safe_date_time}_{safe_prompt}.{file_type}"
 
 # 6. Speech transcription via OpenAI service
@@ -262,7 +251,6 @@ def transcribe_audio(openai_key, file_path, model):
     }
     with open(file_path, 'rb') as f:
         data = {'file': f}
-        st.write('Transcribe Audio is Posting request to ' + OPENAI_API_URL)
         response = requests.post(OPENAI_API_URL, headers=headers, files=data, data={'model': model})
     if response.status_code == 200:
         st.write(response.json())
@@ -338,8 +326,6 @@ def get_table_download_link(file_path):
         mime_type = 'text/html'
     elif ext == '.md':
         mime_type = 'text/markdown'
-    elif ext == '.wav':
-        mime_type = 'audio/wav'
     else:
         mime_type = 'application/octet-stream'  # general binary data type
     href = f'<a href="data:{mime_type};base64,{b64}" target="_blank" download="{file_name}">{file_name}</a>'
@@ -389,7 +375,6 @@ def chat_with_model(prompt, document_section, model_choice='gpt-3.5-turbo'):
     res_box = st.empty()
     collected_chunks = []
     collected_messages = []
-    st.write('Running prompt with ' + model)
     for chunk in openai.ChatCompletion.create(model='gpt-3.5-turbo', messages=conversation, temperature=0.5, stream=True):
         collected_chunks.append(chunk)  
         chunk_message = chunk['choices'][0]['delta']  
@@ -463,7 +448,6 @@ def txt2chunks(text):
 # Vector Store using FAISS
 @st.cache_resource
 def vector_store(text_chunks):
-    st.write('Retrieving OpenAI embeddings')
     embeddings = OpenAIEmbeddings(openai_api_key=key)
     return FAISS.from_texts(texts=text_chunks, embedding=embeddings)
 
@@ -522,22 +506,30 @@ def get_zip_download_link(zip_file):
 
 # 14. Inference Endpoints for Whisper (best fastest STT) on NVIDIA T4 and Llama (best fastest AGI LLM) on NVIDIA A10
 # My Inference Endpoint
-# API_URL_IE = f'https://tonpixzfvq3791u9.us-east-1.aws.endpoints.huggingface.cloud'
+#API_URL_IE = f'https://tonpixzfvq3791u9.us-east-1.aws.endpoints.huggingface.cloud'
 # Original
 #API_URL_IE = "https://api-inference.huggingface.co/models/openai/whisper-small.en"
-# Latest small - 1/26
-API_URL_IE = "https://api-inference.huggingface.co/models/openai/whisper-small"
-#MODEL2 = "openai/whisper-small.en"
-#MODEL2_URL = "https://huggingface.co/openai/whisper-small.en"
-
-HF_KEY = st.secrets['HF_KEY']
+# A10 Inference Endpoint for whisper large tests
+API_URL_IE = "https://hifdvffh2em0wn50.us-east-1.aws.endpoints.huggingface.cloud"
+
+MODEL2 = "openai/whisper-small.en"
+MODEL2_URL = "https://huggingface.co/openai/whisper-small.en"
+#headers = {
+#	"Authorization": "Bearer XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX",
+#	"Content-Type": "audio/wav"
+#}
+HF_KEY = os.getenv('HF_KEY')
 headers = {
     "Authorization": f"Bearer {HF_KEY}",
     "Content-Type": "audio/wav"
 }
 
 #@st.cache_resource
-
+def query(filename):
+    with open(filename, "rb") as f:
+        data = f.read()
+    response = requests.post(API_URL_IE, headers=headers, data=data)
+    return response.json()
 
 def generate_filename(prompt, file_type):
     central = pytz.timezone('US/Central')
@@ -561,28 +553,27 @@ def transcribe_audio(filename):
     output = query(filename)
     return output
 
+
 def whisper_main():
-    #st.title("Speech to Text")
-    #st.write("Record your speech and get the text.")
+    st.title("1🐪Llama🦙Whisperer")
+    st.write("Record your speech and get the text.")
 
     # Audio, transcribe, GPT:
     filename = save_and_play_audio(audio_recorder)
     if filename is not None:
         transcription = transcribe_audio(filename)
-        try:
-            transcript = transcription['text']
-            st.write(transcript)
-            response = StreamLLMChatResponse(transcript)
-            filename_txt = generate_filename(transcript, ".txt")
-            create_file(filename_txt, transcript, response, should_save)
-            filename_wav = filename_txt.replace('.txt', '.wav')
-            import shutil
-            shutil.copyfile(filename, filename_wav)
-            if os.path.exists(filename):
-                os.remove(filename)
-        except:
-            st.write('Starting Whisper Model on GPU.  Please retry in 30 seconds.')
+        #try:
+        
+        transcript = transcription['text']
+        #except:
+        #st.write('Whisper model is asleep. Starting up now on T4 GPU - please give 5 minutes then retry as it scales up from zero to activate running container(s).')
 
+        st.write(transcript)
+        response = StreamLLMChatResponse(transcript)
+        # st.write(response) - redundant with streaming result?
+        filename = generate_filename(transcript, ".txt")
+        create_file(filename, transcript, response, should_save)
+        #st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)
 
 import streamlit as st
 
@@ -590,156 +581,132 @@ import streamlit as st
 def StreamMedChatResponse(topic):
     st.write(f"Showing resources or questions related to: {topic}")
 
+def add_multi_system_agent_topics():
+    with st.expander("Multi-System Agent AI Topics 🤖", expanded=True):
+        st.markdown("🤖 **Explore Multi-System Agent AI Topics**: This section provides a variety of topics related to multi-system agent AI systems.")
 
-
-def add_medical_exam_buttons():
-    # Medical exam terminology descriptions
-    descriptions = {
-        "White Blood Cells 🌊": "3 Q&A with emojis about types, facts, function, inputs and outputs of white blood cells 🎥",
-        "CT Imaging🦠": "3 Q&A with emojis on CT Imaging post surgery, how to, what to look for 💊",
-        "Hematoma 💉": "3 Q&A with emojis about hematoma and infection care and study including bacteria cultures and tests or labs💪",
-        "Post Surgery Wound Care 🍌": "3 Q&A with emojis on wound care, and good bedside manner 🩸",
-        "Healing and humor 💊": "3 Q&A with emojis on stories and humor about healing and caregiving 🚑",
-        "Psychology of bedside manner 🧬": "3 Q&A with emojis on bedside manner and how to make patients feel at ease🛠",
-        "CT scan 💊": "3 Q&A with analysis on infection using CT scan and packing for skin, cellulitus and fascia 🩺"
-    }
-
-    # Expander for medical topics
-    with st.expander("Medical Licensing Exam Topics 📚", expanded=False):
-        st.markdown("🩺 **Important**: Variety of topics for medical licensing exams.")
-
-        # Create buttons for each description with unique keys
-        for idx, (label, content) in enumerate(descriptions.items()):
-            button_key = f"button_{idx}"
-            if st.button(label, key=button_key):
-                st.write(f"Running {label}")
-                input='Create markdown outline for definition of topic ' + label + ' also short quiz with appropriate emojis and definitions for:  ' + content
-                response=StreamLLMChatResponse(input)
-                filename = generate_filename(response, 'txt')   
-                create_file(filename, input, response, should_save)
-
-def add_medical_exam_buttons2():
-    with st.expander("Medical Licensing Exam Topics 📚", expanded=False):
-        st.markdown("🩺 **Important**: This section provides a variety of medical topics that are often encountered in medical licensing exams.")
-
-        # Define medical exam terminology descriptions
+        # Define multi-system agent AI topics and descriptions
         descriptions = {
-            "White Blood Cells 🌊": "3 Questions and Answers with emojis about white blood cells 🎥",
-            "CT Imaging🦠": "3 Questions and Answers with emojis about CT Imaging of post surgery abscess, hematoma, and cerosanguiness fluid 💊",
-            "Hematoma 💉": "3 Questions and Answers with emojis about hematoma and infection and how heat helps white blood cells 💪",
-            "Post Surgery Wound Care 🍌": "3 Questions and Answers with emojis about wound care and how to help as a caregiver🩸",
-            "Healing and humor 💊": "3 Questions and Answers with emojis on the use of stories and humor to help patients and family 🚑",
-            "Psychology of bedside manner 🧬": "3 Questions and Answers with emojis  about good bedside manner 🛠",
-            "CT scan 💊": "3 Questions and Answers with analysis of bacteria and understanding infection using cultures and CT scan 🩺"
+            "Reinforcement Learning 🎮": "Questions related to reinforcement learning algorithms and applications 🕹️",
+            "Natural Language Processing 🗣️": "Questions about natural language processing techniques and chatbot development 🗨️",
+            "Multi-Agent Systems 🤝": "Questions pertaining to multi-agent systems and cooperative AI interactions 🤖",
+            "Conversational AI 🗨️": "Questions on building conversational AI agents and chatbots for various platforms 💬",
+            "Distributed AI Systems 🌐": "Questions about distributed AI systems and their implementation in networked environments 🌐",
+            "AI Ethics and Bias 🤔": "Questions related to ethics and bias considerations in AI systems and decision-making 🧠",
+            "AI in Healthcare 🏥": "Questions about the application of AI in healthcare and medical diagnosis 🩺",
+            "AI in Autonomous Vehicles 🚗": "Questions on the use of AI in autonomous vehicles and self-driving technology 🚗"
         }
 
         # Create columns
         col1, col2, col3, col4 = st.columns([1, 1, 1, 1], gap="small")
         
         # Add buttons to columns
-        if col1.button("Ultrasound with Doppler 🌊"):
-            StreamLLMChatResponse(descriptions["Ultrasound with Doppler 🌊"])
+        if col1.button("Reinforcement Learning 🎮"):
+            st.write(descriptions["Reinforcement Learning 🎮"])
+            StreamLLMChatResponse(descriptions["Reinforcement Learning 🎮"])
 
-        if col2.button("Oseltamivir 🦠"):
-            StreamLLMChatResponse(descriptions["Oseltamivir 🦠"])
+        if col2.button("Natural Language Processing 🗣️"):
+            st.write(descriptions["Natural Language Processing 🗣️"])
+            StreamLLMChatResponse(descriptions["Natural Language Processing 🗣️"])
 
-        if col3.button("IM Epinephrine 💉"):
-            StreamLLMChatResponse(descriptions["IM Epinephrine 💉"])
+        if col3.button("Multi-Agent Systems 🤝"):
+            st.write(descriptions["Multi-Agent Systems 🤝"])
+            StreamLLMChatResponse(descriptions["Multi-Agent Systems 🤝"])
 
-        if col4.button("Hypokalemia 🍌"):
-            StreamLLMChatResponse(descriptions["Hypokalemia 🍌"])
+        if col4.button("Conversational AI 🗨️"):
+            st.write(descriptions["Conversational AI 🗨️"])
+            StreamLLMChatResponse(descriptions["Conversational AI 🗨️"])
 
         col5, col6, col7, col8 = st.columns([1, 1, 1, 1], gap="small")
 
-        if col5.button("Succinylcholine 💊"):
-            StreamLLMChatResponse(descriptions["Succinylcholine 💊"])
+        if col5.button("Distributed AI Systems 🌐"):
+            st.write(descriptions["Distributed AI Systems 🌐"])
+            StreamLLMChatResponse(descriptions["Distributed AI Systems 🌐"])
 
-        if col6.button("Phosphoinositol System 🧬"):
-            StreamLLMChatResponse(descriptions["Phosphoinositol System 🧬"])
+        if col6.button("AI Ethics and Bias 🤔"):
+            st.write(descriptions["AI Ethics and Bias 🤔"])
+            StreamLLMChatResponse(descriptions["AI Ethics and Bias 🤔"])
+
+        if col7.button("AI in Healthcare 🏥"):
+            st.write(descriptions["AI in Healthcare 🏥"])
+            StreamLLMChatResponse(descriptions["AI in Healthcare 🏥"])
+
+        if col8.button("AI in Autonomous Vehicles 🚗"):
+            st.write(descriptions["AI in Autonomous Vehicles 🚗"])
+            StreamLLMChatResponse(descriptions["AI in Autonomous Vehicles 🚗"])
 
-        if col7.button("Ramipril 💊"):
-            StreamLLMChatResponse(descriptions["Ramipril 💊"])
-        
-        
 
 # 17. Main
 def main():
 
-    #st.title("GAIA - Medical License Exam Testing")
+    st.title("Try Some Topics:")
     prompt = f"Write ten funny jokes that are tweet length stories that make you laugh.  Show as markdown outline with emojis for each."
 
     # Add Wit and Humor buttons
     # add_witty_humor_buttons()
-    add_medical_exam_buttons()
-
-
-    with st.expander("Prompts 📚", expanded=False):
-
-        example_input = st.text_input("Enter your prompt text for Llama:", value=prompt, help="Enter text to get a response from DromeLlama.")
-        if st.button("Run Prompt With Llama model", help="Click to run the prompt."):
-            try:
-                response=StreamLLMChatResponse(example_input)
-                create_file(filename, example_input, response, should_save)
-            except:
-                st.write('Llama model is asleep. Starting now on A10 GPU.  Please wait one minute then retry.  KEDA triggered.')
+    # Calling the function to add the multi-system agent AI topics buttons
+    add_multi_system_agent_topics()
 
-        openai.api_key = os.getenv('OPENAI_API_KEY')
-        if openai.api_key == None: openai.api_key = st.secrets['OPENAI_API_KEY']
-        
-        menu = ["txt", "htm", "xlsx", "csv", "md", "py"]
-        choice = st.sidebar.selectbox("Output File Type:", menu)
-        
-        model_choice = st.sidebar.radio("Select Model:", ('gpt-3.5-turbo', 'gpt-3.5-turbo-0301'))        
-        
-        user_prompt = st.text_area("Enter prompts, instructions & questions:", '', height=100)
-        collength, colupload = st.columns([2,3])  # adjust the ratio as needed
-        with collength:
-            max_length = st.slider("File section length for large files", min_value=1000, max_value=128000, value=12000, step=1000)
-        with colupload:
-            uploaded_file = st.file_uploader("Add a file for context:", type=["pdf", "xml", "json", "xlsx", "csv", "html", "htm", "md", "txt"])
-        document_sections = deque()
-        document_responses = {}
-        if uploaded_file is not None:
-            file_content = read_file_content(uploaded_file, max_length)
-            document_sections.extend(divide_document(file_content, max_length))
-        if len(document_sections) > 0:
-            if st.button("👁️ View Upload"):
-                st.markdown("**Sections of the uploaded file:**")
-                for i, section in enumerate(list(document_sections)):
-                    st.markdown(f"**Section {i+1}**\n{section}")
-            st.markdown("**Chat with the model:**")
+    example_input = st.text_input("Enter your example text:", value=prompt, help="Enter text to get a response from DromeLlama.")
+    if st.button("Run Prompt With DromeLlama", help="Click to run the prompt."):
+        try:
+            StreamLLMChatResponse(example_input)
+        except:
+            st.write('DromeLlama is asleep. Starting up now on A10 - please give 5 minutes then retry as KEDA scales up from zero to activate running container(s).')
+
+    openai.api_key = os.getenv('OPENAI_KEY')
+    menu = ["txt", "htm", "xlsx", "csv", "md", "py"]
+    choice = st.sidebar.selectbox("Output File Type:", menu)
+    model_choice = st.sidebar.radio("Select Model:", ('gpt-3.5-turbo', 'gpt-3.5-turbo-0301'))        
+    user_prompt = st.text_area("Enter prompts, instructions & questions:", '', height=100)
+    collength, colupload = st.columns([2,3])  # adjust the ratio as needed
+    with collength:
+        max_length = st.slider("File section length for large files", min_value=1000, max_value=128000, value=12000, step=1000)
+    with colupload:
+        uploaded_file = st.file_uploader("Add a file for context:", type=["pdf", "xml", "json", "xlsx", "csv", "html", "htm", "md", "txt"])
+    document_sections = deque()
+    document_responses = {}
+    if uploaded_file is not None:
+        file_content = read_file_content(uploaded_file, max_length)
+        document_sections.extend(divide_document(file_content, max_length))
+    if len(document_sections) > 0:
+        if st.button("👁️ View Upload"):
+            st.markdown("**Sections of the uploaded file:**")
             for i, section in enumerate(list(document_sections)):
-                if i in document_responses:
-                    st.markdown(f"**Section {i+1}**\n{document_responses[i]}")
-                else:
-                    if st.button(f"Chat about Section {i+1}"):
-                        st.write('Reasoning with your inputs...')
-                        #response = chat_with_model(user_prompt, section, model_choice)
-                        st.write('Response:')
-                        st.write(response)
-                        document_responses[i] = response
-                        filename = generate_filename(f"{user_prompt}_section_{i+1}", choice)
-                        create_file(filename, user_prompt, response, should_save)
-                        st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)
-        if st.button('💬 Chat'):
-            st.write('Reasoning with your inputs...')
-            user_prompt_sections = divide_prompt(user_prompt, max_length)
-            full_response = ''
-            for prompt_section in user_prompt_sections:
-                response = chat_with_model(prompt_section, ''.join(list(document_sections)), model_choice)
-                full_response += response + '\n'  # Combine the responses
-            response = full_response
-            st.write('Response:')
-            st.write(response)
-            filename = generate_filename(user_prompt, choice)
-            create_file(filename, user_prompt, response, should_save)
-            #st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)
-
-    # Compose a file sidebar of markdown md files:
-    all_files = glob.glob("*.md")
-    all_files = [file for file in all_files if len(os.path.splitext(file)[0]) >= 10]  # exclude files with short names
+                st.markdown(f"**Section {i+1}**\n{section}")
+        st.markdown("**Chat with the model:**")
+        for i, section in enumerate(list(document_sections)):
+            if i in document_responses:
+                st.markdown(f"**Section {i+1}**\n{document_responses[i]}")
+            else:
+                if st.button(f"Chat about Section {i+1}"):
+                    st.write('Reasoning with your inputs...')
+                    response = chat_with_model(user_prompt, section, model_choice)
+                    st.write('Response:')
+                    st.write(response)
+                    document_responses[i] = response
+                    filename = generate_filename(f"{user_prompt}_section_{i+1}", choice)
+                    create_file(filename, user_prompt, response, should_save)
+                    st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)
+    if st.button('💬 Chat'):
+        st.write('Reasoning with your inputs...')
+        user_prompt_sections = divide_prompt(user_prompt, max_length)
+        full_response = ''
+        for prompt_section in user_prompt_sections:
+            response = chat_with_model(prompt_section, ''.join(list(document_sections)), model_choice)
+            full_response += response + '\n'  # Combine the responses
+        response = full_response
+        st.write('Response:')
+        st.write(response)
+        filename = generate_filename(user_prompt, choice)
+        create_file(filename, user_prompt, response, should_save)
+        st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)
+
+    # Compose a file sidebar of past encounters
+    all_files = glob.glob("*.*")
+    all_files = [file for file in all_files if len(os.path.splitext(file)[0]) >= 20]  # exclude files with short names
     all_files.sort(key=lambda x: (os.path.splitext(x)[1], x), reverse=True)  # sort by file type and file name in descending order
-    if st.sidebar.button("🗑 Delete All Text"):
+    if st.sidebar.button("🗑 Delete All"):
         for file in all_files:
             os.remove(file)
         st.experimental_rerun()
@@ -795,77 +762,36 @@ def main():
             
             st.experimental_rerun()
 
-
-    # Function to encode file to base64
-    def get_base64_encoded_file(file_path):
-        with open(file_path, "rb") as file:
-            return base64.b64encode(file.read()).decode()
-
-    # Function to create a download link
-    def get_audio_download_link(file_path):
-        base64_file = get_base64_encoded_file(file_path)
-        return f'<a href="data:file/wav;base64,{base64_file}" download="{os.path.basename(file_path)}">⬇️ Download Audio</a>'
-
-    # Compose a file sidebar of past encounters
-    all_files = glob.glob("*.wav")
-    all_files = [file for file in all_files if len(os.path.splitext(file)[0]) >= 10]  # exclude files with short names
-    all_files.sort(key=lambda x: (os.path.splitext(x)[1], x), reverse=True)  # sort by file type and file name in descending order
-
-    filekey = 'delall'
-    if st.sidebar.button("🗑 Delete All Audio", key=filekey):
-        for file in all_files:
-            os.remove(file)
-        st.experimental_rerun()
-
-    for file in all_files:
-        col1, col2 = st.sidebar.columns([6, 1])  # adjust the ratio as needed
-        with col1:
-            st.markdown(file)
-            if st.button("🎵", key="play_" + file):  # play emoji button
-                audio_file = open(file, 'rb')
-                audio_bytes = audio_file.read()
-                st.audio(audio_bytes, format='audio/wav')
-                #st.markdown(get_audio_download_link(file), unsafe_allow_html=True)
-                #st.text_input(label="", value=file)
-        with col2:
-            if st.button("🗑", key="delete_" + file):
-                os.remove(file)
-                st.experimental_rerun()
-
-
-
     # Feedback
     # Step: Give User a Way to Upvote or Downvote
-    with st.expander("Give your feedback 👍", expanded=False):
-
-        feedback = st.radio("Step 8: Give your feedback", ("👍 Upvote", "👎 Downvote"))
-        if feedback == "👍 Upvote":
-            st.write("You upvoted 👍. Thank you for your feedback!")
-        else:
-            st.write("You downvoted 👎. Thank you for your feedback!")
-            
-        load_dotenv()
-        st.write(css, unsafe_allow_html=True)
-        st.header("Chat with documents :books:")
-        user_question = st.text_input("Ask a question about your documents:")
-        if user_question:
-            process_user_input(user_question)
-        with st.sidebar:
-            st.subheader("Your documents")
-            docs = st.file_uploader("import documents", accept_multiple_files=True)
-            with st.spinner("Processing"):
-                raw = pdf2txt(docs)
-                if len(raw) > 0:
-                    length = str(len(raw))
-                    text_chunks = txt2chunks(raw)
-                    vectorstore = vector_store(text_chunks)
-                    st.session_state.conversation = get_chain(vectorstore)
-                    st.markdown('# AI Search Index of Length:' + length + ' Created.')  # add timing
-                    filename = generate_filename(raw, 'txt')
-                    create_file(filename, raw, '', should_save)
+    feedback = st.radio("Step 8: Give your feedback", ("👍 Upvote", "👎 Downvote"))
+    if feedback == "👍 Upvote":
+        st.write("You upvoted 👍. Thank you for your feedback!")
+    else:
+        st.write("You downvoted 👎. Thank you for your feedback!")
+        
+    load_dotenv()
+    st.write(css, unsafe_allow_html=True)
+    st.header("Chat with documents :books:")
+    user_question = st.text_input("Ask a question about your documents:")
+    if user_question:
+        process_user_input(user_question)
+    with st.sidebar:
+        st.subheader("Your documents")
+        docs = st.file_uploader("import documents", accept_multiple_files=True)
+        with st.spinner("Processing"):
+            raw = pdf2txt(docs)
+            if len(raw) > 0:
+                length = str(len(raw))
+                text_chunks = txt2chunks(raw)
+                vectorstore = vector_store(text_chunks)
+                st.session_state.conversation = get_chain(vectorstore)
+                st.markdown('# AI Search Index of Length:' + length + ' Created.')  # add timing
+                filename = generate_filename(raw, 'txt')
+                create_file(filename, raw, '', should_save)
 
 # 18. Run AI Pipeline
 if __name__ == "__main__":
     whisper_main()
     main()
-    #add_Med_Licensing_Exam_Dataset()
+    add_Med_Licensing_Exam_Dataset()