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.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+movies_nonnull.csv filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,2 @@
+venv/
+cosine.pkl

app.py

@@ -0,0 +1,35 @@
+import gradio as gr
+import pandas as pd
+import pickle
+import joblib
+kmeans = joblib.load('kmeans_model.joblib')
+
+movies_pred = pd.read_csv("movies_nonnull.csv")
+with open("movies_df.pkl", "rb") as f:
+    movies_df = pickle.load(f)
+
+with open("cosine.pkl", "rb") as f:
+    cosine_sim = pickle.load(f)
+
+def recommend_movies(name):
+
+    try:
+        
+        idx = movies_pred[movies_pred['title'] == name].index[0]
+    
+        prediction = kmeans.predict(movies_df.iloc[idx,:-1].to_numpy().reshape(1,-1))
+        ans = list(movies_pred[movies_df['KmeansCluster']==prediction[0]].index)
+        scores=[]
+
+        for i in ans:
+            scores.append((i,cosine_sim.at[idx,i]))
+        scores.sort(key = lambda x: x[1],reverse=True)
+        final_ans = []
+        for i in scores[:20]:
+            final_ans.append(movies_pred.iloc[i[0]]['title'])
+    
+        return final_ans
+    except Exception as e:
+            return "Sorry Movie does not exist in the database"
+iface = gr.Interface(fn=recommend_movies, inputs="text", outputs="text")
+iface.launch()

kmeans_model.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:23c5373b223b209fe4dcc3af13b6bbe443c01acf8d9f87b9a63231ac7ae51ea7
+size 3022295

movies_df.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2baf008e9c0e0d1d06cfe1acee686810ab9a67fcf2897f3d5a6ef1996481f92a
+size 8142211

movies_nonnull.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:38d32ed05e190a4acb29e5522d10c60d272e2a5bcd256a7b2b4a218220e246e0
+size 33020290