import streamlit as st

# Set Streamlit page configuration
st.set_page_config(page_title="Documentation", layout="wide")

# Set up the Streamlit app layout
st.title("Documentation")

st.header("Dataset creation")

st.subheader(":blue[HAL API harvest]")

st.write("HAL is the french national open archive for scientific publications based on the principles of open access and self-archiving.")
st.write("All the API documentation is available [here](https://api.archives-ouvertes.fr/docs/search)")
st.write("All records of article type publications reported in the UNIV-COTEDAZUR collection of HAL are obtained with this looping function that populates a pandas Dataframe as output ")
st.code("""
# we retrieve first the total number of records
url_for_total_count = "https://api.archives-ouvertes.fr/search/UNIV-COTEDAZUR/?q=docType_s:ART&rows=0"
response = requests.request("GET", url_for_total_count).text
data = json.loads(response)
total_count = data["response"]["numFound"]

""", language='python')
st.code("""
step = 1000
df = []
for i in range(1, int(total_count), int(step)):
  url = f"https://api.archives-ouvertes.fr/search/UNIV-COTEDAZUR/?q=docType_s:ART&rows={step}&start={i}&wt=csv&fl=uri_s,title_s,subTitle_s,authFullName_s,producedDate_s,domain_t,journalTitle_s,journalPublisher_s,abstract_s"
  data = pd.read_csv(url, encoding="utf-8")
  df.append(data)
df = pd.concat(df)
# clean up a little bit
df = df.drop_duplicates(subset=['uri_s'])
df = df.replace(np.nan, '')
""", language='python')

st.write("The dataframe's colmumns of metadata are then concatenated into a single combined text in a new column. It is therefore on this new column that the different embeddings models will be applied to encode this combined text and output a single vector embedding.")
st.code("""
df = df.astype(str)
df["combined"] = df.title_s + ". " + df.subTitle_s + ". " +df.abstract_s
""", language='python')

st.subheader(":blue[Huggingface open models for Embeddings]")

st.write("The open source Huggingface platform hosts a large number of pre-trained models that can then be reused for many tasks (text or image classification, summarization, document QA etc...). We can then use the sentence-transformers library applied on some of these available embedding pre-trained models for creating embeddings.")
st.write("There is two ways of working with the Huggingface hosted models : by using the [inference API endpoint](https://huggingface.co/inference-api) or by locally importing the model. Here we choose the second way")
st.write("Two open source transformers-based models have been used to convert the textual metadata into numerical vector representation, which generated two vector embeddings datasets : embeddings_all-MiniLM-L6-v2.pt and embeddings_multi-qa-mpnet-base-dot-v1.pt")
st.code("""
import torch
from sentence_transformers import SentenceTransformer
        
embedder = SentenceTransformer('all-MiniLM-L6-v2') # or 'multi-qa-mpnet-base-dot-v1'

corpus_embeddings = embedder.encode(df.combined, convert_to_tensor=True)
        
# how to save and reload
torch.save(corpus_embeddings, f"{LOCAL_PATH}/embeddings_all-MiniLM-L6-v2.pt")
corpus_embeddings = torch.load(f"{LOCAL_PATH}/embeddings_all-MiniLM-L6-v2.pt")

""", language='python')

st.subheader(":blue[Bonus : OpenAI Embeddings]")

st.write("If you want to do the same with text-embedding-ada-002 (the OpenAI embeddings model)")

st.code("""
import openai
import tiktoken
from openai.embeddings_utils import get_embedding
        
openai.api_key = os.getenv("OPENAI_API_KEY")

# embedding model parameters
embedding_model = "text-embedding-ada-002"
embedding_encoding = "cl100k_base"  # this the encoding for text-embedding-ada-002
max_tokens = 8000  # the maximum for text-embedding-ada-002 is 8191

# filtering dataset on text under the max tokens limit      
encoding = tiktoken.get_encoding(embedding_encoding)
df["n_tokens"] = df.combined.apply(lambda x: len(encoding.encode(x)))
df = df[df.n_tokens <= max_tokens]
        
# generate embeddings
def custom_get_embedding(text: str) -> list[float]:
    return openai.Embedding.create(input=[text], model="text-embedding-ada-002")["data"][0]["embedding"]
df["openai_embedding"] = df.combined.apply(lambda x: custom_get_embedding(x) )

""", language='python')

st.write("And the Steamlit UI code would be :")

st.code("""
df["openai_embedding"] = df.openai_embedding.apply(literal_eval).apply(np.array)
def custom_get_embedding(text: str) -> list[float]:
    return openai.Embedding.create(input=[text], model="text-embedding-ada-002", openai_api_key=OPENAI_API_KEY)["data"][0]["embedding"]
def openai_response(query):
    query_embedding = np.array(custom_get_embedding(
        query
    ))
    df["similarity"] = df.openai_embedding.apply(lambda x: cosine_similarity(x, query_embedding))
    return df.sort_values("similarity", ascending=False).head(5).to_json(orient="records")

""", language='python')

st.header("Dataset hosting")

st.write("The csv file of the dataset is avalaible in the data folder")