Create app.py

app.py

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+import gradio as gr
+from datasets import load_dataset
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+import numpy as np
+
+# Load datasets
+nsfw_datasets = [
+    load_dataset("aifeifei798/DPO_Pairs-Roleplay-NSFW"),
+    load_dataset("Maxx0/sexting-nsfw-adultconten"),
+    load_dataset("QuietImpostor/Claude-3-Opus-Claude-3.5-Sonnnet-9k"),
+    load_dataset("HuggingFaceTB/everyday-conversations-llama3.1-2k"),
+    load_dataset("Chadgpt-fam/sexting_dataset")
+]
+
+# Prepare all texts from datasets
+all_texts = []
+for dataset in nsfw_datasets:
+    for split in dataset.keys():
+        if 'text' in dataset[split].features:
+            all_texts.extend(dataset[split]['text'])
+        elif 'content' in dataset[split].features:
+            all_texts.extend(dataset[split]['content'])
+
+# Create TF-IDF vectorizer
+vectorizer = TfidfVectorizer()
+tfidf_matrix = vectorizer.fit_transform(all_texts)
+
+def find_best_description(input_text):
+    input_vector = vectorizer.transform([input_text])
+    similarities = cosine_similarity(input_vector, tfidf_matrix)
+    most_similar_index = np.argmax(similarities)
+    return all_texts[most_similar_index]
+
+def generate_text(input_text):
+    return find_best_description(input_text)
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=generate_text,
+    inputs=gr.Textbox(label="Enter text to describe"),
+    outputs="text",
+    title="NSFW Text Descriptor",
+    description="Enter text to find the best description from NSFW datasets.",
+    allow_flagging="never"
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()