Update app.py

app.py

@@ -572,33 +572,6 @@ def display_videos_and_links():
         col_index += 1  # Increment column index to place the next video in the next column
 
 
-
-@st.cache_resource
-def display_videos_and_links_old():
-    video_files = [f for f in os.listdir('.') if f.endswith('.mp4')]
-    if not video_files:
-        st.write("No MP4 videos found in the current directory.")
-        return
-    video_files_sorted = sorted(video_files, key=lambda x: len(x.split('.')[0]))
-    grid_sizes = [len(f.split('.')[0]) for f in video_files_sorted]
-    col_sizes = ['small' if size <= 4 else 'medium' if size <= 8 else 'large' for size in grid_sizes]
-
-    # Create a map for number of columns to use for each size
-    num_columns_map = {"small": 4, "medium": 3, "large": 2}
-    current_grid_size = 0
-
-    for video_file, col_size in zip(video_files_sorted, col_sizes):
-        if current_grid_size != num_columns_map[col_size]:
-            cols = st.columns(num_columns_map[col_size])
-            current_grid_size = num_columns_map[col_size]
-            col_index = 0
-
-        with cols[col_index % current_grid_size]:
-            st.video(video_file, format='video/mp4', start_time=0)
-            k = video_file.split('.')[0]  # Assumes keyword is the file name without extension
-            display_glossary_entity(k)
-
-
  
 @st.cache_resource
 def display_images_and_wikipedia_summaries():
@@ -606,15 +579,9 @@ def display_images_and_wikipedia_summaries():
     if not image_files:
         st.write("No PNG images found in the current directory.")
         return
-
-    # Sort image_files based on the length of the keyword to create a visually consistent grid
     image_files_sorted = sorted(image_files, key=lambda x: len(x.split('.')[0]))
-    # Calculate the grid size based on the sorted keywords
     grid_sizes = [len(f.split('.')[0]) for f in image_files_sorted]
-    # Dynamically adjust column size based on keyword length
     col_sizes = ['small' if size <= 4 else 'medium' if size <= 8 else 'large' for size in grid_sizes]
-
-    # Create a map for number of columns to use for each size
     num_columns_map = {"small": 4, "medium": 3, "large": 2}
     current_grid_size = 0
     for image_file, col_size in zip(image_files_sorted, col_sizes):
@@ -625,10 +592,8 @@ def display_images_and_wikipedia_summaries():
             with cols[col_index % current_grid_size]:
                 image = Image.open(image_file)
                 st.image(image, caption=image_file, use_column_width=True)
-                # Display search links
                 k = image_file.split('.')[0]  # Assumes keyword is the file name without extension
                 display_glossary_entity(k)
-                #col_index += 1
 
 
 def get_all_query_params(key):
@@ -641,22 +606,17 @@ def clear_query_params():
 # Function to display content or image based on a query
 @st.cache_resource
 def display_content_or_image(query):
-    # Check if the query matches any glossary term
     for category, terms in transhuman_glossary.items():
         for term in terms:
             if query.lower() in term.lower():
                 st.subheader(f"Found in {category}:")
                 st.write(term)
                 return True  # Return after finding and displaying the first match
-    
-    # Check for an image match in a predefined directory (adjust path as needed)
     image_dir = "images"  # Example directory where images are stored
     image_path = f"{image_dir}/{query}.png"  # Construct image path with query
     if os.path.exists(image_path):
         st.image(image_path, caption=f"Image for {query}")
         return True
-    
-    # If no content or image is found
     st.warning("No matching content or image found.")
     return False
 
@@ -676,7 +636,7 @@ 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"..."
 should_save = st.sidebar.checkbox("💾 Save", value=True, help="Save your session data.")
 
 
@@ -1004,10 +964,6 @@ def transcribe_audio(filename):
     return output
 
 def whisper_main():
-    #st.title("Speech to Text")
-    #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)
@@ -1019,14 +975,12 @@ def whisper_main():
             transcript=''
             st.write(transcript)
 
-        # Whisper to GPT: New!! ---------------------------------------------------------------------
-        st.write('Reasoning with your inputs with GPT..')
+        st.write('Reasoning with your inputs..')
         response = chat_with_model(transcript)
         st.write('Response:')
         st.write(response)
         filename = generate_filename(response, "txt")
         create_file(filename, transcript, response, should_save)
-        # Whisper to GPT: New!! ---------------------------------------------------------------------
         
         # Whisper to Llama:
         response = StreamLLMChatResponse(transcript)
@@ -1046,22 +1000,18 @@ def whisper_main():
 def main():
     prompt = PromptPrefix2
     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."):
+        example_input = st.text_input("Enter your prompt text:", value=prompt, help="Enter text to get a response.")
+        if st.button("Run Prompt", help="Click to run."):
             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.')
-
+                st.write('model is asleep. Starting now on A10 GPU.  Please wait one minute then retry.  KEDA triggered.')
         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:
@@ -1092,8 +1042,6 @@ def main():
                         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)
@@ -1144,8 +1092,6 @@ def main():
                 os.remove(file)
                 st.experimental_rerun()
 
-
-
     GiveFeedback=False
     if GiveFeedback:
         with st.expander("Give your feedback 👍", expanded=False):
@@ -1177,9 +1123,7 @@ def main():
 
     try:
         query_params = st.query_params
-        #query = (query_params.get('q') or query_params.get('query') or [''])[0]
         query = (query_params.get('q') or query_params.get('query') or [''])
-        #st.markdown('# Running query: ' + query)
         if query: search_glossary(query)
     except:
         st.markdown(' ')