from typing import List
import requests
import pandas as pd
from haystack import Document
from haystack.document_store.faiss import FAISSDocumentStore
from haystack.generator.transformers import RAGenerator
from haystack.retriever.dense import DensePassageRetriever


# Add documents from which you want generate answers
# Download a csv containing some sample documents data
# Here some sample documents data
temp = requests.get("https://raw.githubusercontent.com/deepset-ai/haystack/master/tutorials/small_generator_dataset.csv")
open('small_generator_dataset.csv', 'wb').write(temp.content)

# Get dataframe with columns "title", and "text"
df = pd.read_csv("small_generator_dataset.csv", sep=',')
# Minimal cleaning
df.fillna(value="", inplace=True)

print(df.head())

titles = list(df["title"].values)
texts = list(df["text"].values)

# Create to haystack document format
documents: List[Document] = []
for title, text in zip(titles, texts):
    documents.append(
        Document(
            text=text,
            meta={
                "name": title or ""
            }
        )
    )


# Initialize FAISS document store to documents and corresponding index for embeddings
# Set `return_embedding` to `True`, so generator doesn't have to perform re-embedding
document_store = FAISSDocumentStore(
    faiss_index_factory_str="Flat",
    return_embedding=True
)

# Initialize DPR Retriever to encode documents, encode question and query documents
retriever = DensePassageRetriever(
    document_store=document_store,
    query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
    passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
    use_gpu=True,
    embed_title=True,
)

# Initialize RAG Generator
generator = RAGenerator(
    model_name_or_path="facebook/rag-token-nq",
    use_gpu=True,
    top_k_answers=1,
    max_length=200,
    min_length=2,
    embed_title=True,
    num_beams=2,
)

# Delete existing documents in documents store
document_store.delete_all_documents()
# Write documents to document store
document_store.write_documents(documents)
# Add documents embeddings to index
document_store.update_embeddings(
    retriever=retriever
)

# Now ask your questions
# We have some sample questions
QUESTIONS = [
    "who got the first nobel prize in physics",
    "when is the next deadpool movie being released",
    "which mode is used for short wave broadcast service",
    "who is the owner of reading football club",
    "when is the next scandal episode coming out",
    "when is the last time the philadelphia won the superbowl",
    "what is the most current adobe flash player version",
    "how many episodes are there in dragon ball z",
    "what is the first step in the evolution of the eye",
    "where is gall bladder situated in human body",
    "what is the main mineral in lithium batteries",
    "who is the president of usa right now",
    "where do the greasers live in the outsiders",
    "panda is a national animal of which country",
    "what is the name of manchester united stadium",
]

# Now generate answer for question
for question in QUESTIONS:
    # Retrieve related documents from retriever
    retriever_results = retriever.retrieve(
        query=question
    )

    # Now generate answer from question and retrieved documents
    predicted_result = generator.predict(
        query=question,
        documents=retriever_results,
        top_k=1
    )

    # Print you answer
    answers = predicted_result["answers"]
    print(f'Generated answer is \'{answers[0]["answer"]}\' for the question = \'{question}\'')