Update app.py

app.py

@@ -1,3 +1,4 @@
+# apne imp libraries
 from sentence_transformers import SentenceTransformer, util
 from huggingface_hub import hf_hub_download
 import pickle
@@ -9,18 +10,18 @@ import gradio as gr
 
 pd.options.mode.chained_assignment = None
 
-# Load precomputed embeddings from your dataset repo
+# embeddings load kiye dataset repo se 
 embeddings = pickle.load(open(
     hf_hub_download("Go-Raw/semantic-memes", repo_type="dataset", filename="meme-embeddings.pkl"), "rb"))
 
-# Load meme metadata from your dataset repo
+# apne meme ka metadata load kiya
 df = pd.read_csv(
     hf_hub_download("Go-Raw/semantic-memes", repo_type="dataset", filename="semantic_memes.csv"))
 
-# Load model
+# ye apna model hai
 model = SentenceTransformer('sentence-transformers/all-mpnet-base-v2')
 
-# Meme search function
+# iss func se meme search hota hai
 def generate_memes(prompt):
     prompt_embedding = model.encode(prompt, convert_to_tensor=True)
     hits = util.semantic_search(prompt_embedding, embeddings, top_k=6)
@@ -38,7 +39,7 @@ def generate_memes(prompt):
             print(f"Error loading image {url}: {e}")
     return images
 
-# Gradio UI
+# Gradio ka UI
 input_textbox = gr.Textbox(lines=1, label="Type your vibe here 🧠")
 output_gallery = gr.Gallery(label="Your Meme Results", columns=3, rows=2, height="auto")
 
@@ -54,7 +55,7 @@ examples = [
     "This meeting could’ve been an email"
 ]
 
-# Launch app
+# app launch karne ke liye
 iface = gr.Interface(
     fn=generate_memes,
     inputs=input_textbox,