Create app.py

app.py

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+import gensim
+from gensim.models import Word2Vec
+import gradio as gr
+
+# Prepare and clean your data
+# recipes is a list of lists of ingredients. Each list represents a recipe.
+# For simplicity, let's assume you have already cleaned your data and it's stored in a variable called recipes.
+
+# Train Word2Vec Model
+model = Word2Vec.load("word2vecsg2.model")
+
+def recommend_ingredients(ingredients):
+    similar_ingredients = []
+    for ingredient in ingredients:
+        if ingredient in model.wv.key_to_index:
+            top_similar = model.wv.most_similar(ingredient, topn=5)
+            for ing, similarity in top_similar:
+                similar_ingredients.append((ing, round(similarity, 2)))
+    return similar_ingredients
+
+# Create Gradio Interface
+def gradio_interface(ingredients):
+    ingredients = ingredients.split(',')
+    recommendations = recommend_ingredients(ingredients)
+    formatted_recommendations = [f'{ing} ({similarity})' for ing, similarity in recommendations]
+    return formatted_recommendations
+
+iface = gr.Interface(fn=gradio_interface, 
+                     inputs="text", 
+                     outputs="text",
+                     examples=[["onion,garlic,tomato"], ["beef,chicken"], ["milk,cheese"]])
+
+iface.launch()