Update app.py

app.py

@@ -4,23 +4,27 @@ from datetime import datetime
 from sentence_transformers import SentenceTransformer
 from sklearn.metrics.pairwise import cosine_similarity
 
-# Load article dataset
-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)
-)
-
-# Load SentenceTransformer model and compute embeddings once
-model = SentenceTransformer("all-MiniLM-L6-v2")
-text_embeddings = model.encode(df["combined_text"].tolist(), convert_to_tensor=False)
-
-# Store results globally
+# Global variables
+df = None
+text_embeddings = None
+model = None
 results_dict = {}
 
-# Create markdown from results
+# 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 = ""
@@ -29,25 +33,23 @@ def format_markdown(top_results):
         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
 
-
-
-# Auto-query construction and retrieval
 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["Topic"]
-        subtopic = row["Subtopic"]
-        top_n = int(row["TopN"])
+        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)
@@ -59,9 +61,12 @@ def process_query_csv(query_file):
         label = f"{topic} - {subtopic} (Top {top_n})"
         results_dict[label] = format_markdown(top_results)
 
-    return list(results_dict.keys()), results_dict[list(results_dict.keys())[0]] if results_dict else "No results."
+    if results_dict:
+        first_key = list(results_dict.keys())[0]
+        return list(results_dict.keys()), results_dict[first_key]
+    else:
+        return [], "No results."
 
-# Show markdown for selected query
 def display_result(selected_query):
     return results_dict.get(selected_query, "No results found.")
 
@@ -70,13 +75,12 @@ 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)
 
-demo.launch()
+if __name__ == "__main__":
+    demo.launch()