Update app.py

app.py

@@ -12,8 +12,6 @@ from langchain_core.output_parsers import StrOutputParser
 
 output_parser = StrOutputParser()
 
-# from langchain_community.prompts import PromptTemplate
-# from langchain.chains import LLMChain
 import json
 
 
@@ -24,35 +22,35 @@ db_name = 'sample_mflix'
 collection_name = 'embedded_movies'
 collection = client[db_name][collection_name]
 
-#try:
-vector_store = MongoDBAtlasVectorSearch(embedding=OpenAIEmbeddings(), collection=collection, index_name='vector_index', text_key='plot', embedding_key='plot_embedding')
-llm = ChatOpenAI()
-prompt = ChatPromptTemplate.from_messages([
-("system", "You are a movie recommendation engine please elaborate on movies."),
-("user", "List of movies: {input}")
- ])
-chain = prompt | llm | output_parser
+try:
+    vector_store = MongoDBAtlasVectorSearch(embedding=OpenAIEmbeddings(), collection=collection, index_name='vector_index', text_key='plot', embedding_key='plot_embedding')
+    llm = ChatOpenAI()
+    prompt = ChatPromptTemplate.from_messages([
+    ("system", "You are a movie recommendation engine please elaborate on movies."),
+    ("user", "List of movies: {input}")
+     ])
+    chain = prompt | llm | output_parser
 
-#except:
-    # If open ai key is wrong
-# print ('Open AI key is wrong')
-# vector_store = None
+except:
+    #If open ai key is wrong
+    print ('Open AI key is wrong')
+    vector_store = None
 
 def get_movies(message, history):
 
-    # try:
-    movies =  vector_store.similarity_search(message, 3)
-    return_text = ''
-    for movie in movies:
-        return_text = return_text + 'Title : ' +  movie.metadata['title'] + '\n------------\n' + 'Plot: ' + movie.page_content + '\n\n'
-
-    print_llm_text = chain.invoke({"input": return_text})
-
-    for i in range(len(print_llm_text)):
-        time.sleep(0.05)
-        yield "Found: " + "\n\n" + print_llm_text[: i+1]
-    # except:
-    #     yield "Please clone the repo and add your open ai key as well as your MongoDB Atlas UR in the Secret Section of you Space\n OPENAI_API_KEY (your Open AI key) and MONGODB_ATLAS_CLUSTER_URI (0.0.0.0/0 whitelisted instance with Vector index created) \n\n For more information : https://mongodb.com/products/platform/atlas-vector-search"
+    try:
+        movies =  vector_store.similarity_search(message, 3)
+        return_text = ''
+        for movie in movies:
+            return_text = return_text + 'Title : ' +  movie.metadata['title'] + '\n------------\n' + 'Plot: ' + movie.page_content + '\n\n'
+    
+        print_llm_text = chain.invoke({"input": return_text})
+    
+        for i in range(len(print_llm_text)):
+            time.sleep(0.05)
+            yield "Found: " + "\n\n" + print_llm_text[: i+1]
+    except:
+        yield "Please clone the repo and add your open ai key as well as your MongoDB Atlas UR in the Secret Section of you Space\n OPENAI_API_KEY (your Open AI key) and MONGODB_ATLAS_CLUSTER_URI (0.0.0.0/0 whitelisted instance with Vector index created) \n\n For more information : https://mongodb.com/products/platform/atlas-vector-search"
     
 
 demo = gr.ChatInterface(get_movies, examples=["What movies are scary?", "Find me a comedy", "Movies for kids"], title="Movies Atlas Vector Search",description="This small chat uses a similarity search to find relevant movies, it uses an MongoDB Atlase Vector Search read more here: https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-tutorial",submit_btn="Search").queue()