Update app.py

app.py

@@ -1254,137 +1254,6 @@ def transcribe_audio(filename):
     output = query(filename)
     return output
 
-def whisper_main():
-    filename = save_and_play_audio(audio_recorder)
-    if filename is not None:
-        transcription = transcribe_audio(filename)
-        try:
-            transcript = transcription['text']
-            st.write(transcript)
-
-        except:
-            transcript=''
-            st.write(transcript)
-
-        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 Llama:
-        response = StreamLLMChatResponse(transcript)
-        filename_txt = generate_filename(transcript, "md")
-        create_file(filename_txt, transcript, response, should_save)
-        filename_wav = filename_txt.replace('.txt', '.wav')
-        import shutil
-        try: 
-            if os.path.exists(filename):
-                shutil.copyfile(filename, filename_wav)
-        except:
-            st.write('.')
-        if os.path.exists(filename):
-            os.remove(filename)
-
-
-
-    
-    prompt = '''
-What is MoE?
-What are Multi Agent Systems?
-What is Self Rewarding AI?
-What is Semantic and Episodic memory?
-What is AutoGen?
-What is ChatDev?
-What is Omniverse?
-What is Lumiere?
-What is SORA?  
-    '''
-    
-    with st.expander("Prompts 📚", expanded=True):
-        #example_input = st.text_input("Enter your prompt text:", value=prompt, help="Enter text to get a response.")
-        #example_input = st.text_area("Enter Prompt :", '', height=100
-        
-        # Search History to ArXiv
-        session_state = {}
-        if "search_queries" not in session_state:
-            session_state["search_queries"] = []
-        example_input = st.text_input("Search", value=session_state["search_queries"][-1] if session_state["search_queries"] else "")
-        if example_input:
-            session_state["search_queries"].append(example_input)
-
-            # Search AI
-            query=example_input
-            if query: 
-                result = search_arxiv(query)
-                #search_glossary(query)
-                search_glossary(result)
-            st.markdown(' ')
-                                     
-        st.write("Search history:")
-        for example_input in session_state["search_queries"]:
-            st.write(example_input)
-
-        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('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:
-            max_length = st.slider(key='maxlength', label="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:**")
-            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)
-
-
 
 # Sample function to demonstrate a response, replace with your own logic
 def StreamMedChatResponse(topic):
@@ -1492,11 +1361,136 @@ if st.button("Clear Query Parameters", key='ClearQueryParams'):
     st.experimental_set_query_params
     st.experimental_rerun()
 
-# 18. Run AI Pipeline
-if __name__ == "__main__":
-    st.markdown("### 🎲🗺️ Arxiv Paper Search QA RAG MAS using Streamlit and Gradio API")
-    whisper_main()
-    display_glossary_grid(roleplaying_glossary)  # Word Glossary Jump Grid
-    display_videos_and_links()   # Video Jump Grid
-    display_images_and_wikipedia_summaries()   # Image Jump Grid
-    #display_buttons_with_scores()  # Feedback Jump Grid
+
+st.markdown("### 🎲🗺️ Arxiv Paper Search QA RAG MAS using Streamlit and Gradio API")
+
+filename = save_and_play_audio(audio_recorder)
+if filename is not None:
+    transcription = transcribe_audio(filename)
+    try:
+        transcript = transcription['text']
+        st.write(transcript)
+
+    except:
+        transcript=''
+        st.write(transcript)
+
+    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 Llama:
+    response = StreamLLMChatResponse(transcript)
+    filename_txt = generate_filename(transcript, "md")
+    create_file(filename_txt, transcript, response, should_save)
+    filename_wav = filename_txt.replace('.txt', '.wav')
+    import shutil
+    try: 
+        if os.path.exists(filename):
+            shutil.copyfile(filename, filename_wav)
+    except:
+        st.write('.')
+    if os.path.exists(filename):
+        os.remove(filename)
+
+
+
+
+prompt = '''
+What is MoE?
+What are Multi Agent Systems?
+What is Self Rewarding AI?
+What is Semantic and Episodic memory?
+What is AutoGen?
+What is ChatDev?
+What is Omniverse?
+What is Lumiere?
+What is SORA?  
+'''
+
+
+# Search History to ArXiv
+session_state = {}
+if "search_queries" not in session_state:
+    session_state["search_queries"] = []
+example_input = st.text_input("Search", value=session_state["search_queries"][-1] if session_state["search_queries"] else "")
+if example_input:
+    session_state["search_queries"].append(example_input)
+
+    # Search AI
+    query=example_input
+    if query: 
+        result = search_arxiv(query)
+        #search_glossary(query)
+        search_glossary(result)
+    st.markdown(' ')
+                             
+st.write("Search history:")
+for example_input in session_state["search_queries"]:
+    st.write(example_input)
+
+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('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:
+    max_length = st.slider(key='maxlength', label="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:**")
+    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)
+
+display_glossary_grid(roleplaying_glossary)  # Word Glossary Jump Grid
+display_videos_and_links()   # Video Jump Grid
+display_images_and_wikipedia_summaries()   # Image Jump Grid
+#display_buttons_with_scores()  # Feedback Jump Grid