app.py

import gradio as gr
import numpy as np
from sentence_transformers import SentenceTransformer
from sklearn.neighbors import NearestNeighbors

# Load model
model = SentenceTransformer('all-MiniLM-L6-v2')

# Global storage
global_docs = []
nn_model = None
doc_embeddings = None

# Load documents
def load_documents(file_obj):
    docs = [line.strip() for line in file_obj if line.strip()]
    return docs

# Build index using Nearest Neighbors
def build_index(docs):
    embeddings = model.encode(docs)
    nn = NearestNeighbors(n_neighbors=3, metric='euclidean')
    nn.fit(embeddings)
    return nn, embeddings

# Search
def semantic_search(query, top_k=3):
    if not nn_model or not global_docs:
        return "Please upload and index a file first."
    query_vec = model.encode([query])
    distances, indices = nn_model.kneighbors(query_vec, n_neighbors=top_k)
    results = [
        f"Rank {i+1}:\nDocument: {global_docs[idx]}\nDistance: {distances[0][i]:.4f}\n"
        for i, idx in enumerate(indices[0])
    ]
    return "\n".join(results)

# Upload and index
def upload_and_index(file):
    global global_docs, nn_model, doc_embeddings
    contents = file.read().decode("utf-8").splitlines()
    global_docs = [line.strip() for line in contents if line.strip()]
    nn_model, doc_embeddings = build_index(global_docs)
    return "Documents indexed successfully!"

# Gradio UI
with gr.Blocks() as demo:
    gr.Markdown("## 🔍 Semantic Search in Academic Papers (No FAISS)")

    file_input = gr.File(label="Upload .txt file", file_types=[".txt"])
    upload_button = gr.Button("Upload & Index")
    upload_output = gr.Textbox(label="Status")

    query_input = gr.Textbox(label="Enter your query")
    search_button = gr.Button("Search")
    search_output = gr.Textbox(label="Results")

    upload_button.click(upload_and_index, inputs=file_input, outputs=upload_output)
    search_button.click(semantic_search, inputs=query_input, outputs=search_output)

demo.launch()