some revision and refactor of the interface

requirements.txt

@@ -7,7 +7,7 @@ grobid_tei_xml==0.1.3
 tqdm==4.66.2
 pyyaml==6.0.1
 pytest==8.1.1
-streamlit==1.33.0
+streamlit==1.36.0
 lxml
 Beautifulsoup4
 python-dotenv

streamlit_app.py

@@ -42,8 +42,6 @@ OPEN_EMBEDDINGS = {
     'Salesforce/SFR-Embedding-Mistral': 'Salesforce/SFR-Embedding-Mistral'
 }
 
-DISABLE_MEMORY = ['zephyr-7b-beta']
-
 if 'rqa' not in st.session_state:
     st.session_state['rqa'] = {}
 
@@ -108,36 +106,6 @@ st.set_page_config(
     }
 )
 
-css_modify_left_column = '''
-<style>                 
-    [data-testid="stHorizontalBlock"] > div:nth-child(1) {
-        overflow: hidden;
-        background-color: red;
-        height: 70vh;
-    }
-</style>
-'''
-css_modify_right_column = '''
-<style>                 
-    [data-testid="stHorizontalBlock"]> div:first-child {
-        background-color: red;
-        position: fixed
-        height: 70vh;
-    }
-</style>
-'''
-css_disable_scrolling_container = '''
-<style>
-    [data-testid="ScrollToBottomContainer"] {
-        overflow: hidden;
-    }
-</style>
-'''
-
-
-# st.markdown(css_lock_column_fixed, unsafe_allow_html=True)
-# st.markdown(css2, unsafe_allow_html=True)
-
 
 def new_file():
     st.session_state['loaded_embeddings'] = None
@@ -188,7 +156,7 @@ def init_qa(model, embeddings_name=None, api_key=None):
         )
         embeddings = HuggingFaceEmbeddings(
             model_name=OPEN_EMBEDDINGS[embeddings_name])
-        st.session_state['memory'] = ConversationBufferWindowMemory(k=4) if model not in DISABLE_MEMORY else None
+        # st.session_state['memory'] = ConversationBufferWindowMemory(k=4) if model not in DISABLE_MEMORY else None
     else:
         st.error("The model was not loaded properly. Try reloading. ")
         st.stop()
@@ -233,23 +201,27 @@ def get_file_hash(fname):
     return hash_md5.hexdigest()
 
 
-def play_old_messages():
+def play_old_messages(container):
     if st.session_state['messages']:
         for message in st.session_state['messages']:
             if message['role'] == 'user':
-                with st.chat_message("user"):
-                    st.markdown(message['content'])
+                container.chat_message("user").markdown(message['content'])
             elif message['role'] == 'assistant':
-                with st.chat_message("assistant"):
-                    if mode == "LLM":
-                        st.markdown(message['content'], unsafe_allow_html=True)
-                    else:
-                        st.write(message['content'])
+                if mode == "LLM":
+                    container.chat_message("assistant").markdown(message['content'], unsafe_allow_html=True)
+                else:
+                    container.chat_message("assistant").write(message['content'])
 
 
 # is_api_key_provided = st.session_state['api_key']
 
 with st.sidebar:
+    st.title("📝 Scientific Document Insights Q/A")
+    st.subheader("Upload a scientific article in PDF, ask questions, get insights.")
+    st.markdown(
+        ":warning: [Usage disclaimer](https://github.com/lfoppiano/document-qa?tab=readme-ov-file#disclaimer-on-data-security-and-privacy-%EF%B8%8F) :warning: ")
+
+    st.divider()
     st.session_state['model'] = model = st.selectbox(
         "Model:",
         options=OPENAI_MODELS + list(OPEN_MODELS.keys()),
@@ -305,22 +277,18 @@ with st.sidebar:
     # else:
     #     is_api_key_provided = st.session_state['api_key']
 
-    st.button(
-        'Reset chat memory.',
-        key="reset-memory-button",
-        on_click=clear_memory,
-        help="Clear the conversational memory. Currently implemented to retrain the 4 most recent messages.",
-        disabled=model in st.session_state['rqa'] and st.session_state['rqa'][model].memory is None)
+    # st.button(
+    #     'Reset chat memory.',
+    #     key="reset-memory-button",
+    #     on_click=clear_memory,
+    #     help="Clear the conversational memory. Currently implemented to retrain the 4 most recent messages.",
+    #     disabled=model in st.session_state['rqa'] and st.session_state['rqa'][model].memory is None)
 
 left_column, right_column = st.columns([1, 1])
+right_column = right_column.container(height=600, border=False)
+left_column = left_column.container(height=600, border=False)
 
 with right_column:
-    st.title("📝 Scientific Document Insights Q/A")
-    st.subheader("Upload a scientific article in PDF, ask questions, get insights.")
-
-    st.markdown(
-        ":warning: [Usage disclaimer](https://github.com/lfoppiano/document-qa?tab=readme-ov-file#disclaimer-on-data-security-and-privacy-%EF%B8%8F) :warning: ")
-
     uploaded_file = st.file_uploader(
         "Upload an article",
         type=("pdf", "txt"),
@@ -330,11 +298,14 @@ with right_column:
         help="The full-text is extracted using Grobid."
     )
 
-question = st.chat_input(
-    "Ask something about the article",
-    # placeholder="Can you give me a short summary?",
-    disabled=not uploaded_file
-)
+    placeholder = st.empty()
+    messages = st.container(height=300, border=False)
+
+    question = st.chat_input(
+        "Ask something about the article",
+        # placeholder="Can you give me a short summary?",
+        disabled=not uploaded_file
+    )
 
 query_modes = {
     "llm": "LLM Q/A",
@@ -355,6 +326,10 @@ with st.sidebar:
              "relevant paragraphs to the question in the paper. "
              "Question coefficient attempt to estimate how effective the question will be answered."
     )
+    st.session_state['ner_processing'] = st.checkbox(
+        "Identify materials and properties.",
+        help='The LLM responses undergo post-processing to extract physical quantities, measurements, and materials mentions.'
+    )
 
     # Add a checkbox for showing annotations
     # st.session_state['show_annotations'] = st.checkbox("Show annotations", value=True)
@@ -372,11 +347,6 @@ with st.sidebar:
                                  help="Number of chunks to consider when answering a question",
                                  disabled=not uploaded_file)
 
-    st.session_state['ner_processing'] = st.checkbox("Identify materials and properties.")
-    st.markdown(
-        'The LLM responses undergo post-processing to extract <span style="color:orange">physical quantities, measurements</span>, and <span style="color:green">materials</span> mentions.',
-        unsafe_allow_html=True)
-
     st.divider()
 
     st.header("Documentation")
@@ -403,7 +373,7 @@ if uploaded_file and not st.session_state.loaded_embeddings:
         st.error("Before uploading a document, you must enter the API key. ")
         st.stop()
 
-    with right_column:
+    with left_column:
         with st.spinner('Reading file, calling Grobid, and creating memory embeddings...'):
             binary = uploaded_file.getvalue()
             tmp_file = NamedTemporaryFile()
@@ -416,8 +386,6 @@ if uploaded_file and not st.session_state.loaded_embeddings:
             st.session_state['loaded_embeddings'] = True
             st.session_state.messages = []
 
-    # timestamp = datetime.utcnow()
-
 
 def rgb_to_hex(rgb):
     return "#{:02x}{:02x}{:02x}".format(*rgb)
@@ -439,41 +407,21 @@ def generate_color_gradient(num_elements):
 
 
 with right_column:
-    # css = '''
-    #     <style>
-    #         [data-testid="column"] {
-    #             overflow: auto;
-    #             height: 70vh;
-    #         }
-    #     </style>
-    #     '''
-    # st.markdown(css, unsafe_allow_html=True)
-
-    # st.markdown(
-    #     """
-    #     <script>
-    #     document.querySelectorAll('[data-testid="column"]').scrollIntoView({behavior: "smooth"});
-    #     </script>
-    #     """,
-    #     unsafe_allow_html=True,
-    # )
-
     if st.session_state.loaded_embeddings and question and len(question) > 0 and st.session_state.doc_id:
         for message in st.session_state.messages:
-            with st.chat_message(message["role"]):
+            with messages.chat_message(message["role"]):
                 if message['mode'] == "llm":
-                    st.markdown(message["content"], unsafe_allow_html=True)
+                    messages.chat_message(message["role"]).markdown(message["content"], unsafe_allow_html=True)
                 elif message['mode'] == "embeddings":
-                    st.write(message["content"])
+                    messages.chat_message(message["role"]).write(message["content"])
                 if message['mode'] == "question_coefficient":
-                    st.markdown(message["content"], unsafe_allow_html=True)
+                    messages.chat_message(message["role"]).markdown(message["content"], unsafe_allow_html=True)
         if model not in st.session_state['rqa']:
             st.error("The API Key for the " + model + " is  missing. Please add it before sending any query. `")
             st.stop()
 
-        with st.chat_message("user"):
-            st.markdown(question)
-            st.session_state.messages.append({"role": "user", "mode": mode, "content": question})
+        messages.chat_message("user").markdown(question)
+        st.session_state.messages.append({"role": "user", "mode": mode, "content": question})
 
         text_response = None
         if mode == "embeddings":
@@ -484,12 +432,13 @@ with right_column:
                     context_size=context_size
                 )
         elif mode == "llm":
-            with st.spinner("Generating LLM response..."):
-                _, text_response, coordinates = st.session_state['rqa'][model].query_document(
-                    question,
-                    st.session_state.doc_id,
-                    context_size=context_size
-                )
+            with placeholder:
+                with st.spinner("Generating LLM response..."):
+                    _, text_response, coordinates = st.session_state['rqa'][model].query_document(
+                        question,
+                        st.session_state.doc_id,
+                        context_size=context_size
+                    )
 
         elif mode == "question_coefficient":
             with st.spinner("Estimate question/context relevancy..."):
@@ -511,28 +460,28 @@ with right_column:
         if not text_response:
             st.error("Something went wrong. Contact Luca Foppiano (Foppiano.Luca@nims.co.jp) to report the issue.")
 
-        with st.chat_message("assistant"):
-            if mode == "llm":
-                if st.session_state['ner_processing']:
-                    with st.spinner("Processing NER on LLM response..."):
-                        entities = gqa.process_single_text(text_response)
-                        decorated_text = decorate_text_with_annotations(text_response.strip(), entities)
-                        decorated_text = decorated_text.replace('class="label material"', 'style="color:green"')
-                        decorated_text = re.sub(r'class="label[^"]+"', 'style="color:orange"', decorated_text)
-                        text_response = decorated_text
-                st.markdown(text_response, unsafe_allow_html=True)
-            else:
-                st.write(text_response)
-            st.session_state.messages.append({"role": "assistant", "mode": mode, "content": text_response})
+        if mode == "llm":
+            if st.session_state['ner_processing']:
+                with st.spinner("Processing NER on LLM response..."):
+                    entities = gqa.process_single_text(text_response)
+                    decorated_text = decorate_text_with_annotations(text_response.strip(), entities)
+                    decorated_text = decorated_text.replace('class="label material"', 'style="color:green"')
+                    decorated_text = re.sub(r'class="label[^"]+"', 'style="color:orange"', decorated_text)
+                    text_response = decorated_text
+            messages.chat_message("assistant").markdown(text_response, unsafe_allow_html=True)
+        else:
+            messages.chat_message("assistant").write(text_response)
+        st.session_state.messages.append({"role": "assistant", "mode": mode, "content": text_response})
 
     elif st.session_state.loaded_embeddings and st.session_state.doc_id:
-        play_old_messages()
+        play_old_messages(messages)
 
 with left_column:
     if st.session_state['binary']:
         pdf_viewer(
             input=st.session_state['binary'],
-            annotation_outline_size=1,
+            annotation_outline_size=2,
             annotations=st.session_state['annotations'],
-            render_text=True
+            render_text=True,
+            height=700
         )