Create app.py

app.py

@@ -0,0 +1,57 @@
+import streamlit as st
+import pandas as pd
+import json
+import numpy as np
+#from fuzzywuzzy import fuzz
+
+import pinecone
+from sentence_transformers import SentenceTransformer
+
+pinecone.init(api_key='f5112f8c-f27d-4af1-b427-0c0953c113b5', environment='asia-southeast1-gcp')
+
+#model = SentenceTransformer('all-mpnet-base-v2',device='cpu')
+
+loaded_model = SentenceTransformer(r"finetiuned_model")
+
+def process_string(s):
+    return s.lower().replace('&', 'and')
+
+
+
+index = pinecone.Index('ingradientsearch')
+
+
+# Create a Streamlit app
+def main():
+    st.set_page_config(page_title="Ingradients Matching App", page_icon=":smiley:", layout="wide")
+    st.title("Ingradients name matching App :smiley:")
+
+   
+    st.header("Matches using embeddings (semantic search)")
+    st.write("Enter a ingradient name:")
+    st.write("e.g. Chicken")
+    input_string = st.text_input("")
+
+    input_string = process_string(input_string)
+
+    if st.button("Enter"):
+        st.write("Top 5 matches using semantic search:")
+
+        xq = model.encode([input_string]).tolist()
+        result = index.query(xq, top_k=5, includeMetadata=True)
+
+        Ingredient=[]
+        Group=[]
+        score=[]
+        for matches in result['matches']:
+            Ingredient.append(matches['metadata']['Ingredient'])
+            #Group.append(matches['metadata']['Group'])
+            score.append(matches['score'])
+        
+        final_result= pd.DataFrame(list(zip(Ingredient, Group, score)),
+            columns =['Ingredient','score' ])
+
+        st.dataframe(final_result)
+
+if __name__ == "__main__":
+    main()