import gradio as gr
import pandas as pd
from sentence_transformers import SentenceTransformer, util

# Load data
df = pd.read_excel("IslamWeb_output.xlsx")
df2 = pd.read_excel("JordanFatwas_all.xlsx")
model = SentenceTransformer("paraphrase-multilingual-MiniLM-L12-v2")
embeddings = model.encode(df["question"].tolist(), convert_to_tensor=True)
embeddings2 = model.encode(df2["question"].tolist(), convert_to_tensor=True)

def search_fatwa(query):
    query_embedding = model.encode(query, convert_to_tensor=True)
    scores = util.pytorch_cos_sim(query_embedding, embeddings)[0]
    top_idx = int(scores.argmax())
    
    scores2 = util.pytorch_cos_sim(query_embedding, embeddings2)[0]
    top_idx2 = int(scores2.argmax())
    return {
        "question1": df.iloc[top_idx]["question"],
        "link1": df.iloc[top_idx]["link"],
        "question2": df2.iloc[top_idx2]["question"],
        "link2": df2.iloc[top_idx2]["link"],
    }
print("AAAAA")
iface = gr.Interface(
    fn=search_fatwa,
    inputs="text",
    outputs="json",
    allow_flagging="never",
    title="Fatwa Search",
    description="Ask a question and receive a relevant fatwa with a verified link"
)

iface.launch()