Update app.py

app.py

@@ -8,6 +8,9 @@ from langchain_openai import OpenAIEmbeddings
 from langchain_community.llms import OpenAI
 from langchain_openai import ChatOpenAI
 from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+
+output_parser = StrOutputParser()
 
 # from langchain_community.prompts import PromptTemplate
 # from langchain.chains import LLMChain
@@ -28,8 +31,7 @@ prompt = ChatPromptTemplate.from_messages([
 ("system", "You are a movie recommendation engine please elaborate on movies."),
 ("user", "List of movies: {input}")
  ])
-chain = prompt | llm 
-
+chain = prompt | llm | output_parser
 
 #except:
     # If open ai key is wrong
@@ -40,15 +42,15 @@ def get_movies(message, history):
 
     # try:
     movies =  vector_store.similarity_search(message, 3)
-    retrun_text = ''
+    return_text = ''
     for movie in movies:
-        retrun_text = retrun_text + 'Title : ' +  movie.metadata['title'] + '\n------------\n' + 'Plot: ' + movie.page_content + '\n\n'
+        return_text = return_text + 'Title : ' +  movie.metadata['title'] + '\n------------\n' + 'Plot: ' + movie.page_content + '\n\n'
 
-    print_llm_text = chain.invoke({"input": retrun_text})
+    print_llm_text = chain.invoke({"input": return_text})
 
-    for i in range(len(print_llm_text['content'])):
+    for i in range(len(print_llm_text)):
         time.sleep(0.05)
-        yield "Found: " + "\n\n" + print_llm_text['content'][: i+1]
+        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"