cambios en la interfaz

app.py

@@ -1,19 +1,57 @@
 from sentence_transformers import SentenceTransformer
 from pinecone_client import PineconeClient
+import streamlit as st
+import pandas as pd
+from PIL import Image
+
+
+im = Image.open('video.png')
+st.set_page_config(page_title="Movie Recommender System App",page_icon = im)
+
+import base64
+def add_bg_from_local(image_file):
+    with open(image_file, "rb") as image_file:
+        encoded_string = base64.b64encode(image_file.read())
+    st.markdown(
+    f"""
+    <style>
+    .stApp {{
+        background-image: url(data:image/{"gif"};base64,{encoded_string.decode()});
+        background-size: cover
+    }}
+    </style>
+    """,
+    unsafe_allow_html=True
+    )
+add_bg_from_local('wallpaper_home.jpg')
+
+text_search = st.text_input('Provide a movie description', 
+                            placeholder='E.g. A thrilling adventure in space')
+
+genre = st.selectbox('Select movie genre',
+    options = ['All', 'Action', 'Drama', 'Adventure', 'Sci-Fi', 'Animation', 'Crime',
+       'Comedy', 'Thriller', 'Fantasy', 'Horror', 'History', 'Mystery',
+       'Biography', 'War', 'Western', 'Sport', 'Family', 'Romance',
+       'Music', 'Musical', 'Film-Noir', 'Game-Show', 'Adult',
+       'Reality-TV']
+       )
+genre = [] if genre is "All" else [genre]
+
+ratting = st.slider('Movie Rating (IMDb)', 0.0, 10.0, 0.0, 0.1, format = "%f")
+
+top_k = st.slider('Select Movie Limit', 0, 10, 3)
 
 model = SentenceTransformer('all-MiniLM-L6-v2')
 
 def model_embedder(text):
-    embedding = model.encode(text)  # np.array converted to list
+    embedding = model.encode(text)  
     return embedding.tolist()
 
-
-
-text_search = "Mens Casual T-Shirts"
 vector_search = model_embedder(text_search)
 
 index_name = "movies-embeddings"
-namespace = "ianews"
-p_client = PineconeClient(index_name, namespace)
-result = p_client.query_similarity(vector_search)
-print(result)
+p_client = PineconeClient(index_name)
+results = p_client.query_similarity(vector_search, top_k=top_k, genre=genre, filter_rating=ratting)
+
+df = pd.DataFrame([result['metadata'] for result in results])
+df

pinecone_client.py

@@ -1,26 +1,28 @@
 import pinecone
 import os
 
-API_KEY = os.getenv("PINECONE_API_KEY")
+API_KEY = "a46ed9ad-4f13-448d-b8ea-28028777a373"
 pinecone.init(api_key=API_KEY,
-              environment="asia-southeast1-gcp-free")
+              environment="gcp-starter")
 
 class PineconeClient:
 
-    def __init__(self, index_name:str, namespace:str):
+    def __init__(self, index_name:str):
         self.index_name = index_name
-        self.namespace = namespace
         self.index = pinecone.Index(self.index_name)
 
     def upsert_vectors(self, vectors:list):
-        return self.index.upsert(vectors=vectors, namespace=self.namespace)
+        return self.index.upsert(vectors=vectors)
     
-    def query_similarity(self, query_vector:list, top_k=5):
-        return self.index.query(
-            namespace=self.namespace,
+    def query_similarity(self, query_vector:list, top_k=5, genre:list=[], filter_rating=0):
+        response = self.index.query(
             top_k=top_k,
             include_values=False,
             include_metadata=True,
             vector=query_vector,
-            filter={}
-         )
+            filter={
+                "Generes": { "${}in".format('n' if not genre else ''): genre },
+                "Rating": { "$gte": filter_rating }
+                }
+         )
+        return response.to_dict()["matches"]

requirements.txt

@@ -1,2 +1,3 @@
 streamlit==1.28.2
-pinecone-client==2.2.4 
+pinecone-client==2.2.4
+sentence-transformers