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import pandas as pd
import gradio as gr
from datetime import datetime
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
# Global variables
df = None
text_embeddings = None
model = None
results_dict = {}
# Lazy load model and data
def load_resources():
global df, text_embeddings, model
if df is None:
df = pd.read_csv("analyticsvidhyacomplete.csv", parse_dates=["Date"])
df["Date"] = pd.to_datetime(df["Date"], format='mixed', dayfirst=True, errors='coerce')
df["combined_text"] = (
df["Title"].astype(str) + " " +
df["Description"].astype(str) + " " +
df["Content"].astype(str)
)
if model is None:
model = SentenceTransformer("all-MiniLM-L6-v2")
if text_embeddings is None:
text_embeddings = model.encode(df["combined_text"].tolist(), convert_to_tensor=False)
def format_markdown(top_results):
markdown_output = ""
for _, row in top_results.iterrows():
title = row['Title']
link = row['Link']
desc = row['Description']
date_str = row['Date'].strftime('%Y-%m-%d') if pd.notnull(row['Date']) else 'N/A'
markdown_output += f"### [{title}]({link})\n"
markdown_output += f"**Date**: {date_str}\n\n"
markdown_output += f"{desc}\n\n---\n"
return markdown_output
def process_query_csv(query_file):
global results_dict
results_dict = {}
load_resources() # Ensure model/data is loaded
query_df = pd.read_csv(query_file.name)
for idx, row in query_df.iterrows():
topic = row.get("Topic", "")
subtopic = row.get("Subtopic", "")
top_n = int(row.get("TopN", 5))
query = f"Top {top_n} articles about {subtopic} in {topic}"
query_embedding = model.encode([query], convert_to_tensor=False)
scores = cosine_similarity(query_embedding, text_embeddings).flatten()
df["similarity"] = scores
top_results = df.sort_values(by=["similarity", "Date"], ascending=[False, False]).head(top_n)
label = f"{topic} - {subtopic} (Top {top_n})"
results_dict[label] = format_markdown(top_results)
if results_dict:
keys = list(results_dict.keys())
first_output = results_dict[keys[0]]
else:
keys = []
first_output = "### No results found."
return keys, first_output
def display_result(selected_query):
return results_dict.get(selected_query, "No results found.")
# Gradio UI
with gr.Blocks() as demo:
gr.Markdown("## 📄 Batch Query Article Retriever with Clickable Links")
query_input = gr.File(label="Upload Query CSV (Topic, Subtopic, TopN)")
run_btn = gr.Button("Run Retrieval")
dropdown = gr.Dropdown(label="Select Query")
output_md = gr.Markdown()
run_btn.click(fn=process_query_csv, inputs=query_input, outputs=[dropdown, output_md])
dropdown.change(fn=display_result, inputs=dropdown, outputs=output_md)
if __name__ == "__main__":
demo.launch()
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