Delete app.py

app.py

@@ -1,31 +0,0 @@
-import gradio as gr
-import pickle
-import pandas as pd
-from sklearn.metrics.pairwise import cosine_similarity
-
-# Load model and dataset
-with open("recommender_model.pkl", "rb") as f:
-    model = pickle.load(f)
-
-posts_df = pd.read_csv("posts_cleaned.csv")  # your full dataset with post content
-post_embeddings = model["embeddings"]  # precomputed post embeddings
-vectorizer = model["vectorizer"]      # for transforming user input
-
-# Predict function
-def recommend_from_input(user_text):
-    user_vec = vectorizer.encode([user_text])
-    sims = cosine_similarity(user_vec, post_embeddings)[0]
-    top_idxs = sims.argsort()[-5:][::-1]
-    top_posts = posts_df.iloc[top_idxs]["post_text"].tolist()
-    return "\n\n".join(top_posts)
-
-# Gradio UI
-interface = gr.Interface(
-    fn=recommend_from_input,
-    inputs="text",
-    outputs="text",
-    title="AI Content Recommender",
-    description="Enter a sample interest or post to receive recommendations"
-)
-
-interface.launch()