Upload app.py

app.py

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+from sentence_transformers import SentenceTransformer, util
+from huggingface_hub import hf_hub_download
+import os
+import pickle
+import pandas as pd
+import gradio as gr
+
+pd.options.mode.chained_assignment = None  # Turn off SettingWithCopyWarning
+
+corpus_embeddings = pickle.load(open(hf_hub_download("bhavyagiri/semantic-memes", repo_type="dataset", filename="meme-embeddings.pkl"), "rb"))
+df = pd.read_csv(hf_hub_download("bhavyagiri/semantic-memes", repo_type="dataset", filename="input.csv"))
+
+model = SentenceTransformer('sentence-transformers/all-mpnet-base-v2')
+
+def generate_memes(prompt):
+    prompt_embedding = model.encode(prompt, convert_to_tensor=True)
+    hits = util.semantic_search(prompt_embedding, embeddings, top_k=5)
+    hits = pd.DataFrame(hits[0], columns=['corpus_id', 'score'])
+    desired_ids = hits["corpus_id"]
+    filtered_df = df.loc[df['id'].isin(desired_ids)]
+    filtered_list = list(filtered_df["url"]) 
+    images = [gr.Image.update(value=img, visible=True) for img in filtered_list]
+    return (
+       images
+    )
+input_textbox = gr.inputs.Textbox(lines=2, label="Search something cool", max_length=256)
+output_gallery = gr.output.Gallery(
+            label="Retrieved Memes", show_label=False, elem_id="gallery"
+        ).style(columns=[3], rows=[2], object_fit="contain", height="auto")
+title = "Semantic Search for Memes"
+description = "Search Memes from small dataset of 6k memes"
+examples = ['Spiderman giving lecture', 'Angry Karen']
+interpretation='default'
+enable_queue=True
+
+iface = gr.Interface(fn=classify_garbage, inputs=input_textbox, outputs=label,examples=examples,title=title,description=description,interpretation=interpretation,enable_queue=enable_queue)
+iface.launch(inline=False)