Update app.py

app.py

@@ -53,7 +53,7 @@ def load_docs(files: List) -> str:
     @return: string of all docs concatenated
     """
 
-    st.info("`Reading doc ...`")
+    st.info("Reading doc ...")
     all_text = ""
     for file_path in files:
         file_extension = os.path.splitext(file_path.name)[1]
@@ -69,7 +69,7 @@ def load_docs(files: List) -> str:
             file_content = stringio.read()
             all_text += file_content
         else:
-            st.warning('Please provide txt or pdf.', icon="⚠️")
+            st.warning('Please provide txt or pdf.', icon="⚠")
     return all_text
 
 
@@ -82,7 +82,7 @@ def generate_eval(text: str, num_questions: int, chunk: int):
     @param chunk: chunk size to draw question from in the doc
     @return: eval set as JSON list
     """
-    st.info("`Generating eval set ...`")
+    st.info("Generating eval set ...")
     n = len(text)
     starting_indices = [random.randint(0, n - chunk) for _ in range(num_questions)]
     sub_sequences = [text[i:i + chunk] for i in starting_indices]
@@ -93,7 +93,7 @@ def generate_eval(text: str, num_questions: int, chunk: int):
             qa = chain.run(b)
             eval_set.append(qa)
         except:
-            st.warning('Error generating question %s.' % str(i + 1), icon="⚠️")
+            st.warning('Error generating question %s.' % str(i + 1), icon="⚠")
     eval_set_full = list(itertools.chain.from_iterable(eval_set))
     return eval_set_full
 
@@ -108,7 +108,7 @@ def split_texts(text, chunk_size: int, overlap, split_method: str):
     @param split_method:
     @return: list of str splits
     """
-    st.info("`Splitting doc ...`")
+    st.info("Splitting doc ...")
     if split_method == "RecursiveTextSplitter":
         text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size,
                                                        chunk_overlap=overlap)
@@ -117,7 +117,7 @@ def split_texts(text, chunk_size: int, overlap, split_method: str):
                                               chunk_size=chunk_size,
                                               chunk_overlap=overlap)
     else:
-        st.warning("`Split method not recognized. Using RecursiveCharacterTextSplitter`", icon="⚠️")
+        st.warning("Split method not recognized. Using RecursiveCharacterTextSplitter", icon="⚠")
         text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size,
                                                        chunk_overlap=overlap)
 
@@ -139,7 +139,7 @@ def make_llm(model_version: str):
     elif model_version == "flan-t5-xl":
         chosen_model = HuggingFaceHub(repo_id="google/flan-t5-xl",model_kwargs={"temperature":0,"max_length":64})
     else:
-        st.warning("`Model version not recognized. Using gpt-3.5-turbo`", icon="⚠️")
+        st.warning("Model version not recognized. Using gpt-3.5-turbo", icon="⚠")
         chosen_model = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)
     return chosen_model
 
@@ -154,14 +154,14 @@ def make_retriever(splits, retriever_type, embedding_type, num_neighbors, _llm):
     @param _llm: model
     @return: retriever
     """
-    st.info("`Making retriever ...`")
+    st.info("Making retriever ...")
     # Set embeddings
     if embedding_type == "OpenAI":
         embedding = OpenAIEmbeddings()
     elif embedding_type == "HuggingFace":
         embedding = HuggingFaceEmbeddings()
     else:
-        st.warning("`Embedding type not recognized. Using OpenAI`", icon="⚠️")
+        st.warning("Embedding type not recognized. Using OpenAI", icon="⚠")
         embedding = OpenAIEmbeddings()
 
     # Select retriever
@@ -169,8 +169,8 @@ def make_retriever(splits, retriever_type, embedding_type, num_neighbors, _llm):
         try:
             vector_store = FAISS.from_texts(splits, embedding)
         except ValueError:
-            st.warning("`Error using OpenAI embeddings (disallowed TikToken token in the text). Using HuggingFace.`",
-                       icon="⚠️")
+            st.warning("Error using OpenAI embeddings (disallowed TikToken token in the text). Using HuggingFace.",
+                       icon="⚠")
             vector_store = FAISS.from_texts(splits, HuggingFaceEmbeddings())
         retriever_obj = vector_store.as_retriever(k=num_neighbors)
     elif retriever_type == "SVM":
@@ -185,7 +185,7 @@ def make_retriever(splits, retriever_type, embedding_type, num_neighbors, _llm):
         faiss_index = faiss.IndexFlatL2(d)
         retriever_obj = GPTFaissIndex.from_documents(documents, faiss_index=faiss_index, service_context=context)
     else:
-        st.warning("`Retriever type not recognized. Using SVM`", icon="⚠️")
+        st.warning("Retriever type not recognized. Using SVM", icon="⚠")
         retriever_obj = SVMRetriever.from_texts(splits, embedding)
     return retriever_obj
 
@@ -198,7 +198,7 @@ def make_chain(llm, retriever, retriever_type: str) -> RetrievalQA:
     @param retriever_type: retriever type
     @return: chain (or return retriever for Llama-Index)
     """
-    st.info("`Making chain ...`")
+    st.info("Making chain ...")
     if retriever_type == "Llama-Index":
         qa = retriever
     else:
@@ -218,7 +218,7 @@ def grade_model_answer(predicted_dataset: List, predictions: List, grade_answer_
     @return: A list of scores for the distilled answers.
     """
     # Grade the distilled answer
-    st.info("`Grading model answer ...`")
+    st.info("Grading model answer ...")
     # Set the grading prompt based on the grade_answer_prompt parameter
     if grade_answer_prompt == "Fast":
         prompt = GRADE_ANSWER_PROMPT_FAST
@@ -255,7 +255,7 @@ def grade_model_retrieval(gt_dataset: List, predictions: List, grade_docs_prompt
     @return: list of scores for the retrieved documents.
     """
     # Grade the docs retrieval
-    st.info("`Grading relevance of retrieved docs ...`")
+    st.info("Grading relevance of retrieved docs ...")
 
     # Set the grading prompt based on the grade_docs_prompt parameter
     prompt = GRADE_DOCS_PROMPT_FAST if grade_docs_prompt == "Fast" else GRADE_DOCS_PROMPT
@@ -291,7 +291,7 @@ def run_evaluation(chain, retriever, eval_set, grade_prompt, retriever_type, num
     - latencies_list: A list of latencies in seconds for each question answered.
     - predictions_list: A list of dictionaries containing the model's predicted answers and relevant documents for each question.
     """
-    st.info("`Running evaluation ...`")
+    st.info("Running evaluation ...")
     predictions_list = []
     retrieved_docs = []
     gt_dataset = []
@@ -335,27 +335,27 @@ def run_evaluation(chain, retriever, eval_set, grade_prompt, retriever_type, num
 # Auth
 st.sidebar.image("img/diagnostic.jpg")
 
-oai_api_key = st.sidebar.text_input("`OpenAI API Key:`", type="password")
-ant_api_key = st.sidebar.text_input("`(Optional) Anthropic API Key:`", type="password")
-hf_api_key = st.sidebar.text_input("`(Optional) HuggingFace API Token:`", type="password")
+oai_api_key = st.sidebar.text_input("OpenAI API Key:", type="password")
+ant_api_key = st.sidebar.text_input("(Optional) Anthropic API Key:", type="password")
+hf_api_key = st.sidebar.text_input("(Optional) HuggingFace API Token:", type="password")
 
 with st.sidebar.form("user_input"):
 
-    num_eval_questions = st.select_slider("`Number of eval questions`",
+    num_eval_questions = st.select_slider("Number of eval questions",
                                           options=[1, 5, 10, 15, 20], value=5)
 
-    chunk_chars = st.select_slider("`Choose chunk size for splitting`",
+    chunk_chars = st.select_slider("Choose chunk size for splitting",
                                    options=[500, 750, 1000, 1500, 2000], value=1000)
 
-    overlap = st.select_slider("`Choose overlap for splitting`",
+    overlap = st.select_slider("Choose overlap for splitting",
                                options=[0, 50, 100, 150, 200], value=100)
 
-    split_method = st.radio("`Split method`",
+    split_method = st.radio("Split method",
                             ("RecursiveTextSplitter",
                              "CharacterTextSplitter"),
                             index=0)
 
-    model = st.radio("`Choose model`",
+    model = st.radio("Choose model",
                      ("gpt-3.5-turbo",
                       "gpt-4",
                       "anthropic"),
@@ -363,22 +363,22 @@ with st.sidebar.form("user_input"):
                       #"flan-t5-xl"),
                      index=0)
 
-    retriever_type = st.radio("`Choose retriever`",
+    retriever_type = st.radio("Choose retriever",
                               ("TF-IDF",
                                "SVM",
                                "Llama-Index",
                                "similarity-search"),
                               index=3)
 
-    num_neighbors = st.select_slider("`Choose # chunks to retrieve`",
+    num_neighbors = st.select_slider("Choose # chunks to retrieve",
                                      options=[3, 4, 5, 6, 7, 8])
 
-    embeddings = st.radio("`Choose embeddings`",
+    embeddings = st.radio("Choose embeddings",
                           ("HuggingFace",
                            "OpenAI"),
                           index=1)
 
-    grade_prompt = st.radio("`Grading style prompt`",
+    grade_prompt = st.radio("Grading style prompt",
                             ("Fast",
                              "Descriptive",
                              "Descriptive w/ bias check",
@@ -387,21 +387,21 @@ with st.sidebar.form("user_input"):
 
     submitted = st.form_submit_button("Submit evaluation")
 
-st.sidebar.write("`By:` [@RLanceMartin](https://twitter.com/RLanceMartin)")
+st.sidebar.write("By: [Sentient](https://twitter.com/sentient)")
 
 # App
-st.header("`Auto-evaluator`")
+st.header("Auto-evaluator")
 st.info(
     "`I am an evaluation tool for question-answering built on LangChain. Given documents, I will auto-generate a question-answer eval "
     "set and evaluate using the selected chain settings. Experiments with different configurations are logged. "
     "Optionally, provide your own eval set (as a JSON, see docs/karpathy-pod-eval.json for an example). If you don't have acess to GPT-4 or Anthropic, you can use our free hosted app here: https://autoevaluator.langchain.com/`")
 
 with st.form(key='file_inputs'):
-    uploaded_file = st.file_uploader("`Please upload a file to evaluate (.txt or .pdf):` ",
+    uploaded_file = st.file_uploader("Please upload a file to evaluate (.txt or .pdf): ",
                                      type=['pdf', 'txt'],
                                      accept_multiple_files=True)
 
-    uploaded_eval_set = st.file_uploader("`[Optional] Please upload eval set (.json):` ",
+    uploaded_eval_set = st.file_uploader("[Optional] Please upload eval set (.json): ",
                                          type=['json'],
                                          accept_multiple_files=False)
 
@@ -445,7 +445,7 @@ if uploaded_file and oai_api_key:
     percentage_answer = (correct_answer_count / len(graded_answers)) * 100
     percentage_docs = (correct_docs_count / len(graded_retrieval)) * 100
 
-    st.subheader("`Run Results`")
+    st.subheader("Run Results")
     st.info(
         "`I will grade the chain based on: 1/ the relevance of the retrived documents relative to the question and 2/ "
         "the summarized answer relative to the ground truth answer. You can see (and change) to prompts used for "
@@ -453,7 +453,7 @@ if uploaded_file and oai_api_key:
     st.dataframe(data=d, use_container_width=True)
 
     # Accumulate results
-    st.subheader("`Aggregate Results`")
+    st.subheader("Aggregate Results")
     st.info(
         "`Retrieval and answer scores are percentage of retrived documents deemed relevant by the LLM grader ("
         "relative to the question) and percentage of summarized answers deemed relevant (relative to ground truth "