Delete app.py

app.py

@@ -1,27 +0,0 @@
-import gradio as gr
-import pandas as pd
-from sentence_transformers import SentenceTransformer
-from sklearn.metrics.pairwise import cosine_similarity
-
-# Load your trained SentenceTransformer model
-
-import joblib
-model = joblib.load("recommender_model.pkl")
-
-# Load the posts
-posts = pd.read_csv("posts_cleaned.csv")
-
-# Precompute embeddings for all posts
-post_texts = posts["post_text"].astype(str).tolist()
-post_embeddings = model.encode(post_texts, convert_to_tensor=False)
-
-# Recommendation function
-def recommend(user_input):
-    user_embedding = model.encode([user_input], convert_to_tensor=False)
-    similarities = cosine_similarity(user_embedding, post_embeddings)[0]
-    top_indices = similarities.argsort()[-5:][::-1]
-    recommended = posts.iloc[top_indices]["post_text"].tolist()
-    return "\n\n".join(recommended)
-
-# Launch Gradio app
-gr.Interface(fn=recommend, inputs="text", outputs="text", title="AI Post Recommender").launch()