Upload app.py

app.py

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+import datasets
+from sentence_transformers import SentenceTransformer
+import faiss
+import numpy as np
+import gradio as gr 
+from gradio.components import Label
+
+
+
+# Load the dataset
+dataset = datasets.load_dataset("SandipPalit/Movie_Dataset")
+title = dataset['train']['Title']
+overview = dataset['train']['Overview']
+
+model = SentenceTransformer("sentence-transformers/all-mpnet-base-v2")
+
+vectors = model.encode(overview) 
+
+vector_dimension = vectors.shape[1]
+index = faiss.IndexFlatL2(vector_dimension)
+faiss.normalize_L2(vectors)
+index.add(vectors)
+
+def get_model_generated_vector(text): 
+    search_vector = model.encode(text)
+    vector = np.array([search_vector])
+    faiss.normalize_L2(vector)
+    return vector
+
+def find_top_k_matched(vector):
+    distances, ann = index.search(vector, k=5)
+    return [title[ann[0][0]], title[ann[0][1]], title[ann[0][2]], title[ann[0][3]], title[ann[0][4]]]
+
+
+def movie_recommandation(text):
+    vector = get_model_generated_vector(text) 
+    matches = find_top_k_matched(vector) 
+#     print(matches) 
+    return matches[0], matches[1], matches[2], matches[3], matches[4]
+
+demo = gr.Interface(
+    fn=movie_recommandation, 
+    inputs=gr.Textbox(placeholder="Enter the Movie Name"), 
+    outputs=[Label() for i in range(5)], 
+    examples=[["Scarlet Macaw on Perch"], ["horror"]])
+
+demo.launch(debug=True)