move settings on the sidebar, allow env variables

streamlit_app.py

@@ -18,11 +18,14 @@ from document_qa.grobid_processors import GrobidAggregationProcessor, decorate_t
 from grobid_client_generic import GrobidClientGeneric
 
 if 'rqa' not in st.session_state:
-    st.session_state['rqa'] = None
+    st.session_state['rqa'] = {}
 
 if 'api_key' not in st.session_state:
     st.session_state['api_key'] = False
 
+if 'api_keys' not in st.session_state:
+    st.session_state['api_keys'] = {}
+
 if 'doc_id' not in st.session_state:
     st.session_state['doc_id'] = None
 
@@ -42,13 +45,16 @@ if 'git_rev' not in st.session_state:
 if "messages" not in st.session_state:
     st.session_state.messages = []
 
+if 'ner_processing' not in st.session_state:
+    st.session_state['ner_processing'] = False
+
 
 def new_file():
     st.session_state['loaded_embeddings'] = None
     st.session_state['doc_id'] = None
 
 
-@st.cache_resource
+# @st.cache_resource
 def init_qa(model):
     if model == 'chatgpt-3.5-turbo':
         chat = PromptLayerChatOpenAI(model_name="gpt-3.5-turbo",
@@ -67,6 +73,7 @@ def init_qa(model):
         embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
     else:
         st.error("The model was not loaded properly. Try reloading. ")
+        st.stop()
 
     return DocumentQAEngine(chat, embeddings, grobid_url=os.environ['GROBID_URL'])
 
@@ -94,7 +101,6 @@ def init_ner():
                                      grobid_quantities_client=quantities_client,
                                      grobid_superconductors_client=materials_client
                                      )
-
     return gqa
 
 
@@ -125,51 +131,52 @@ def play_old_messages():
 
 is_api_key_provided = st.session_state['api_key']
 
-model = st.sidebar.radio("Model (cannot be changed after selection or upload)",
-                         ("chatgpt-3.5-turbo", "mistral-7b-instruct-v0.1"),  # , "llama-2-70b-chat"),
-                         index=1,
-                         captions=[
-                             "ChatGPT 3.5 Turbo + Ada-002-text (embeddings)",
-                             "Mistral-7B-Instruct-V0.1 + Sentence BERT (embeddings)"
-                             # "LLama2-70B-Chat + Sentence BERT (embeddings)",
-                         ],
-                         help="Select the model you want to use.",
-                         disabled=is_api_key_provided)
-
-if not st.session_state['api_key']:
+with st.sidebar:
+    model = st.radio(
+        "Model (cannot be changed after selection or upload)",
+        ("chatgpt-3.5-turbo", "mistral-7b-instruct-v0.1"),  # , "llama-2-70b-chat"),
+        index=1,
+        captions=[
+            "ChatGPT 3.5 Turbo + Ada-002-text (embeddings)",
+            "Mistral-7B-Instruct-V0.1 + Sentence BERT (embeddings)"
+            # "LLama2-70B-Chat + Sentence BERT (embeddings)",
+        ],
+        help="Select the model you want to use.")
+
     if model == 'mistral-7b-instruct-v0.1' or model == 'llama-2-70b-chat':
-        api_key = st.sidebar.text_input('Huggingface API Key',
-                                        type="password")  # if 'HUGGINGFACEHUB_API_TOKEN' not in os.environ else os.environ['HUGGINGFACEHUB_API_TOKEN']
+        api_key = st.text_input('Huggingface API Key',
+                                type="password") if 'HUGGINGFACEHUB_API_TOKEN' not in os.environ else os.environ[
+            'HUGGINGFACEHUB_API_TOKEN']
+        st.markdown(
+            "Get it for [Open AI](https://platform.openai.com/account/api-keys) or [Huggingface](https://huggingface.co/docs/hub/security-tokens)")
+
         if api_key:
             st.session_state['api_key'] = is_api_key_provided = True
-            os.environ["HUGGINGFACEHUB_API_TOKEN"] = api_key
-            st.session_state['rqa'] = init_qa(model)
+            st.session_state['api_keys']['mistral-7b-instruct-v0.1'] = api_key
+            if 'HUGGINGFACEHUB_API_TOKEN' not in os.environ:
+                os.environ["HUGGINGFACEHUB_API_TOKEN"] = api_key
+            st.session_state['rqa'][model] = init_qa(model)
+
     elif model == 'chatgpt-3.5-turbo':
-        api_key = st.sidebar.text_input('OpenAI API Key',
-                                        type="password")  # if 'OPENAI_API_KEY' not in os.environ else os.environ['OPENAI_API_KEY']
+        api_key = st.text_input('OpenAI API Key', type="password") if 'OPENAI_API_KEY' not in os.environ else \
+        os.environ['OPENAI_API_KEY']
+        st.markdown(
+            "Get it for [Open AI](https://platform.openai.com/account/api-keys) or [Huggingface](https://huggingface.co/docs/hub/security-tokens)")
         if api_key:
             st.session_state['api_key'] = is_api_key_provided = True
-            os.environ['OPENAI_API_KEY'] = api_key
-            st.session_state['rqa'] = init_qa(model)
-else:
-    is_api_key_provided = st.session_state['api_key']
+            st.session_state['api_keys']['chatgpt-3.5-turbo'] = api_key
+            if 'OPENAI_API_KEY' not in os.environ:
+                os.environ['OPENAI_API_KEY'] = api_key
+            st.session_state['rqa'][model] = init_qa(model)
+    # else:
+    #     is_api_key_provided = st.session_state['api_key']
 
 st.title("📝 Scientific Document Insight Q&A")
 st.subheader("Upload a scientific article in PDF, ask questions, get insights.")
 
-upload_col, radio_col, context_col = st.columns([7, 2, 2])
-with upload_col:
-    uploaded_file = st.file_uploader("Upload an article", type=("pdf", "txt"), on_change=new_file,
-                                     disabled=not is_api_key_provided,
-                                     help="The full-text is extracted using Grobid. ")
-with radio_col:
-    mode = st.radio("Query mode", ("LLM", "Embeddings"), disabled=not uploaded_file, index=0,
-                    help="LLM will respond the question, Embedding will show the "
-                         "paragraphs relevant to the question in the paper.")
-with context_col:
-    context_size = st.slider("Context size", 3, 10, value=4,
-                             help="Number of paragraphs to consider when answering a question",
-                             disabled=not uploaded_file)
+uploaded_file = st.file_uploader("Upload an article", type=("pdf", "txt"), on_change=new_file,
+                                 disabled=not is_api_key_provided,
+                                 help="The full-text is extracted using Grobid. ")
 
 question = st.chat_input(
     "Ask something about the article",
@@ -178,14 +185,29 @@ question = st.chat_input(
 )
 
 with st.sidebar:
+    st.header("Settings")
+    mode = st.radio("Query mode", ("LLM", "Embeddings"), disabled=not uploaded_file, index=0, horizontal=True,
+                    help="LLM will respond the question, Embedding will show the "
+                         "paragraphs relevant to the question in the paper.")
+    chunk_size = st.slider("Chunks size", 100, 2000, value=250,
+                           help="Size of chunks in which the document is partitioned",
+                           disabled=not uploaded_file)
+    context_size = st.slider("Context size", 3, 10, value=4,
+                             help="Number of chunks to consider when answering a question",
+                             disabled=not uploaded_file)
+
+    st.session_state['ner_processing'] = st.checkbox("NER processing on LLM response")
+    st.markdown(
+        '**NER on LLM responses**: The responses from the LLMs are post-processed 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")
     st.markdown("https://github.com/lfoppiano/document-qa")
     st.markdown(
         """After entering your API Key (Open AI or Huggingface). Upload a scientific article as PDF document. You will see a spinner or loading indicator while the processing is in progress. Once the spinner stops, you can proceed to ask your questions.""")
 
-    st.markdown(
-        '**NER on LLM responses**: The responses from the LLMs are post-processed to extract <span style="color:orange">physical quantities, measurements</span> and <span style="color:green">materials</span> mentions.',
-        unsafe_allow_html=True)
     if st.session_state['git_rev'] != "unknown":
         st.markdown("**Revision number**: [" + st.session_state[
             'git_rev'] + "](https://github.com/lfoppiano/document-qa/commit/" + st.session_state['git_rev'] + ")")
@@ -203,9 +225,9 @@ if uploaded_file and not st.session_state.loaded_embeddings:
         tmp_file = NamedTemporaryFile()
         tmp_file.write(bytearray(binary))
         # hash = get_file_hash(tmp_file.name)[:10]
-        st.session_state['doc_id'] = hash = st.session_state['rqa'].create_memory_embeddings(tmp_file.name,
-                                                                                             chunk_size=250,
-                                                                                             perc_overlap=0.1)
+        st.session_state['doc_id'] = hash = st.session_state['rqa'][model].create_memory_embeddings(tmp_file.name,
+                                                                                                    chunk_size=chunk_size,
+                                                                                                    perc_overlap=0.1)
         st.session_state['loaded_embeddings'] = True
         st.session_state.messages = []
 
@@ -226,27 +248,26 @@ if st.session_state.loaded_embeddings and question and len(question) > 0 and st.
     text_response = None
     if mode == "Embeddings":
         with st.spinner("Generating LLM response..."):
-            text_response = st.session_state['rqa'].query_storage(question, st.session_state.doc_id,
-                                                                  context_size=context_size)
+            text_response = st.session_state['rqa'][model].query_storage(question, st.session_state.doc_id,
+                                                                         context_size=context_size)
     elif mode == "LLM":
         with st.spinner("Generating response..."):
-            _, text_response = st.session_state['rqa'].query_document(question, st.session_state.doc_id,
-                                                                      context_size=context_size)
+            _, text_response = st.session_state['rqa'][model].query_document(question, st.session_state.doc_id,
+                                                                             context_size=context_size)
 
     if not text_response:
         st.error("Something went wrong. Contact Luca Foppiano ([email protected]) to report the issue.")
 
     with st.chat_message("assistant"):
         if mode == "LLM":
-            with st.spinner("Processing NER on LLM response..."):
-                entities = gqa.process_single_text(text_response)
-                # for entity in entities:
-                #     entity
-                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)
-                st.markdown(decorated_text, unsafe_allow_html=True)
-                text_response = decorated_text
+            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})