import gradio as gr
import nltk
import numpy as np
import pandas as pd
from librosa import load, resample
from sentence_transformers import SentenceTransformer, util
from transformers import pipeline

# Constants
filename = "df10k_SP500_2020.csv.zip"

model_name = "sentence-transformers/msmarco-distilbert-base-v4"
max_sequence_length = 512
embeddings_filename = "df10k_embeddings_msmarco-distilbert-base-v4.npz"
asr_model = "facebook/wav2vec2-xls-r-300m-21-to-en"

# Load corpus
df = pd.read_csv(filename)
df.drop_duplicates(inplace=True)
print(f"Number of documents: {len(df)}")

nltk.download("punkt")

corpus = []
sentence_count = []
for _, row in df.iterrows():
    # We're interested in the 'mdna' column: 'Management discussion and analysis'
    sentences = nltk.tokenize.sent_tokenize(str(row["mdna"]), language="english")
    sentence_count.append(len(sentences))
    for _, s in enumerate(sentences):
        corpus.append(s)
print(f"Number of sentences: {len(corpus)}")

# Load pre-embedded corpus
corpus_embeddings = np.load(embeddings_filename)["arr_0"]
print(f"Number of embeddings: {corpus_embeddings.shape[0]}")

# Load embedding model
model = SentenceTransformer(model_name)
model.max_seq_length = max_sequence_length

# Load speech to text model
asr = pipeline(
    "automatic-speech-recognition", model=asr_model, feature_extractor=asr_model
)


def find_sentences(query, hits):
    query_embedding = model.encode(query)
    hits = util.semantic_search(query_embedding, corpus_embeddings, top_k=hits)
    hits = hits[0]

    output = pd.DataFrame(
        columns=["Ticker", "Form type", "Filing date", "Text", "Score"]
    )
    for hit in hits:
        corpus_id = hit["corpus_id"]
        # Find source document based on sentence index
        count = 0
        for idx, c in enumerate(sentence_count):
            count += c
            if corpus_id > count - 1:
                continue
            else:
                doc = df.iloc[idx]
                new_row = {
                    "Ticker": doc["ticker"],
                    "Form type": doc["form_type"],
                    "Filing date": doc["filing_date"],
                    "Text": corpus[corpus_id][:80],
                    "Score": "{:.2f}".format(hit["score"]),
                }
                output = output.append(new_row, ignore_index=True)
                break
    return output


def process(input_selection, query, filepath, hits):
    if input_selection == "speech":
        speech, sampling_rate = load(filepath)
        if sampling_rate != 16000:
            speech = resample(speech, orig_sr=sampling_rate, target_sr=16000)
        text = asr(speech)["text"]
    else:
        text = query
    return text, find_sentences(text, hits)


# Gradio inputs
buttons = gr.Radio(
    ["text", "speech"], type="value", value="speech", label="Input selection"
)
text_query = gr.Textbox(
    lines=1,
    label="Text input",
    value="The company is under investigation by tax authorities for potential fraud.",
)
mic = gr.Audio(
    source="microphone", type="filepath", label="Speech input", optional=True
)
slider = gr.Slider(minimum=1, maximum=10, step=1, value=3, label="Number of hits")

# Gradio outputs
speech_query = gr.Textbox(type="text", label="Query string")
results = gr.Dataframe(
    type="pandas",
    headers=["Ticker", "Form type", "Filing date", "Text", "Score"],
    label="Query results",
)

iface = gr.Interface(
    theme="huggingface",
    description="This Spaces lets you query a text corpus containing 2020 annual filings for all S&P500 companies. You can type a text query in English, or record an audio query in 21 languages. You can find a technical deep dive at https://www.youtube.com/watch?v=YPme-gR0f80",
    fn=process,
    inputs=[buttons, text_query, mic, slider],
    outputs=[speech_query, results],
    examples=[
        [
            "speech",
            "Nos ventes internationales ont significativement augmenté.",
            "sales_16k_fr.wav",
            3,
        ],
        [
            "speech",
            "Le prix de l'énergie pourrait avoir un impact négatif dans le futur.",
            "energy_16k_fr.wav",
            3,
        ],
        [
            "speech",
            "El precio de la energía podría tener un impacto negativo en el futuro.",
            "energy_24k_es.wav",
            3,
        ],
        [
            "speech",
            "Mehrere Steuerbehörden untersuchen unser Unternehmen.",
            "tax_24k_de.wav",
            3,
        ],
    ],
)
iface.launch()