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import gradio as gr
import faiss_cpu as faiss
import numpy as np
from sentence_transformers import SentenceTransformer
# Load the model once
model = SentenceTransformer('all-MiniLM-L6-v2')
# Global storage for documents and index
global_docs = []
global_index = None
# Load documents from uploaded file
def load_documents(file_obj):
docs = [line.strip() for line in file_obj if line.strip()]
return docs
# Build FAISS index
def build_index(docs):
embeddings = model.encode(docs).astype(np.float32)
index = faiss.IndexFlatL2(embeddings.shape[1])
index.add(embeddings)
return index, embeddings
# Semantic search
def semantic_search(query, top_k=3):
if not global_index or not global_docs:
return "Please upload a file first."
query_embedding = model.encode([query]).astype(np.float32)
distances, indices = global_index.search(query_embedding, top_k)
results = [
f"Rank {rank + 1}:\nDocument: {global_docs[i]}\nL2 Distance: {distances[0][rank]:.4f}\n"
for rank, i in enumerate(indices[0])
]
return "\n".join(results)
# Handle file upload
def upload_and_index(file):
global global_docs, global_index
contents = file.read().decode("utf-8").splitlines()
global_docs = [line.strip() for line in contents if line.strip()]
global_index, _ = build_index(global_docs)
return "Document indexed successfully!"
# Gradio Interface
with gr.Blocks() as demo:
gr.Markdown("## 🔍 Semantic Search in Academic Papers")
file_input = gr.File(label="Upload Academic Paper (.txt)", file_types=['.txt'])
upload_button = gr.Button("Upload & Index")
upload_output = gr.Textbox(label="Status")
query_input = gr.Textbox(label="Enter Search Query")
search_button = gr.Button("Search")
search_output = gr.Textbox(label="Top 3 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()
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