app.py

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

# # Load files
# df = pd.read_excel("IslamWeb_output.xlsx")
# df2 = pd.read_excel("JordanFatwas_all.xlsx")

# # Validate
# for d, name in [(df, "IslamWeb"), (df2, "JordanFatwas")]:
#     if not {"question", "link"}.issubset(d.columns):
#         raise ValueError(f"❌ Missing required columns in {name}")

# # Load model + encode
# model = SentenceTransformer("paraphrase-multilingual-MiniLM-L12-v2")
# embeddings = model.encode(df["question"].fillna('').tolist(), convert_to_tensor=True)
# embeddings2 = model.encode(df2["question"].fillna('').tolist(), convert_to_tensor=True)

# # Define function
# 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"],
#     }

# # Interface
# iface = gr.Interface(
#     fn=search_fatwa,
#     inputs="text",
#     outputs="json",
#     allow_flagging="never",
#     title="Fatwa Search (Dual Source)",
#     description="Get the most relevant fatwas from both datasets"
# )

# iface.launch()


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

# model = SentenceTransformer("paraphrase-multilingual-MiniLM-L12-v2")
# df = pd.read_csv("cleaned1.csv")
# df2 = pd.read_csv("cleaned2.csv")
# embeddings = torch.load("embeddings1.pt")
# embeddings2 = torch.load("embeddings2.pt")

# # def search_fatwa(data):
# #     query = data[0] if data else ""
# #     query_embedding = model.encode(query, convert_to_tensor=True)
# #     top_idx = int(util.pytorch_cos_sim(query_embedding, embeddings)[0].argmax())
# #     top_idx2 = int(util.pytorch_cos_sim(query_embedding, embeddings2)[0].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"]
# #     }

# def search_fatwa(data):
#     query = data[0] if isinstance(data, list) else data
#     if not query:
#         return {"question1": "", "link1": "", "question2": "", "link2": ""}
#     query_embedding = model.encode(query, convert_to_tensor=True)
#     top_idx = int(util.pytorch_cos_sim(query_embedding, embeddings)[0].argmax())
#     top_idx2 = int(util.pytorch_cos_sim(query_embedding, embeddings2)[0].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"]
#     # }
#     result = f"""Question 1: {df.iloc[top_idx]["question"]}
#         Link 1: {df.iloc[top_idx]["link"]}
        
#         Question 2: {df2.iloc[top_idx2]["question"]}
#         Link 2: {df2.iloc[top_idx2]["link"]}"""
#     return result

# iface = gr.Interface(
#     fn=search_fatwa, 
#     inputs=[gr.Textbox(label="text", lines=3)], 
#     outputs="text"  # Changed from "json" to "text"
# )

# # iface = gr.Interface(fn=search_fatwa, inputs=[gr.Textbox(label="text", lines=3)], outputs="json")




# # iface = gr.Interface(
# #   fn=predict, 
# #   inputs=[gr.Textbox(label="text", lines=3)],
# #   outputs='text',
# #   title=title,
# # )

# iface.launch()


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

model = SentenceTransformer("paraphrase-multilingual-MiniLM-L12-v2")
df = pd.read_csv("cleaned1.csv")
df2 = pd.read_csv("cleaned2.csv")
df3 = pd.read_csv("cleaned3.csv")

embeddings = torch.load("embeddings1.pt")
embeddings2 = torch.load("embeddings2.pt")
embeddings3 = torch.load("embeddings3.pt")
# Pre-extract DataFrame columns to avoid repeated iloc calls
df_questions = df["question"].values
df_links = df["link"].values
df2_questions = df2["question"].values
df2_links = df2["link"].values
df3_questions = df3["question"].values
df3_links = df3["url"].values
def predict(text):
    if not text or text.strip() == "":
        return "No query provided"
    
    query_embedding = model.encode(text, convert_to_tensor=True)
    
    # Compute similarity scores
    sim_scores1 = util.pytorch_cos_sim(query_embedding, embeddings)[0]
    sim_scores2 = util.pytorch_cos_sim(query_embedding, embeddings2)[0]
    sim_scores3 = util.pytorch_cos_sim(query_embedding, embeddings3)[0]
    
    # Get top 3 values and indices in one call
    top3_scores1, top3_idx1 = sim_scores1.topk(3)
    top3_scores2, top3_idx2 = sim_scores2.topk(3)
    top3_scores3, top3_idx3 = sim_scores3.topk(3)
    # Convert to CPU once
    top3_idx1_cpu = top3_idx1.cpu().numpy()
    top3_idx2_cpu = top3_idx2.cpu().numpy()
    top3_idx3_cpu = top3_idx3.cpu().numpy()
    
    top3_scores1_cpu = top3_scores1.cpu().numpy()
    top3_scores2_cpu = top3_scores2.cpu().numpy()
    top3_scores3_cpu = top3_scores3.cpu().numpy()
    # Prepare results using pre-extracted arrays
    results = {
        "top1": [
            {
                "question": df_questions[idx],
                "link": df_links[idx],
                "score": float(score)
            }
            for idx, score in zip(top3_idx1_cpu, top3_scores1_cpu)
        ],
        "top2": [
            {
                "question": df2_questions[idx],
                "link": df2_links[idx], 
                "score": float(score)
            }
            for idx, score in zip(top3_idx2_cpu, top3_scores2_cpu)
        ],
        "top3": [
            {
                "question": df3_questions[idx],
                "link": df3_links[idx], 
                "score": float(score)
            }
            for idx, score in zip(top3_idx3_cpu, top3_scores3_cpu)
        ],
    }
    
    return results

# Match the EXACT structure of your working translation app
title = "Search CSV"
iface = gr.Interface(
    fn=predict,  # Changed from search_fatwa to predict 
    inputs=[gr.Textbox(label="text", lines=3)],
    outputs='json',
    title=title,
)
iface.launch()