Update app.py

app.py

@@ -6,7 +6,7 @@ from llama_index import (
     VectorStoreIndex,
 )
 from llama_index.llms import OpenAI
-import openai
+from openai import OpenAI
 
 # Define Streamlit layout and interaction
 st.title("Grounded Generations")
@@ -25,7 +25,7 @@ def load_data(uploaded_file):
         docs = reader.load_data()
         service_context = ServiceContext.from_defaults(
             llm=OpenAI(
-                model="gpt-3.5-turbo-16k",
+                model="gpt-3.5-turbo-0125",  # Updated model
                 temperature=0.1,
             ),
             system_prompt="You are an AI assistant that uses context from a PDF to assist the user in generating text."
@@ -48,12 +48,12 @@ if 'retrieved_text' not in st.session_state:
 if st.button("Retrieve"):
     with st.spinner('Retrieving text...'):
         # Use VectorStoreIndex to search
-        query_engine = index.as_query_engine(similarity_top_k=3) 
+        query_engine = index.as_query_engine(similarity_top_k=3)
         st.session_state['retrieved_text'] = query_engine.query(user_query)
-        st.write(f"Retrieved Text: {st.session_state['retrieved_text']}") # store the retrieved text as a streamlit state variable 
+        st.write(f"Retrieved Text: {st.session_state['retrieved_text']}")
 
 # Select content type
-content_type = st.selectbox("Select content type:", ["Blog", "Tweet"]) # make some default nonsense 
+content_type = st.selectbox("Select content type:", ["Blog", "Tweet"])
 
 # Generate text based on retrieved text and selected content type
 if st.button("Generate") and content_type:
@@ -62,17 +62,17 @@ if st.button("Generate") and content_type:
         openai.api_key = os.getenv("OPENAI_API_KEY")
         try:
             if content_type == "Blog":
-                prompt = f"Write a blog about 500 words in length using the {st.session_state['retrieved_text']}" #uses content from the stored state
+                prompt = f"Write a blog about 500 words in length using the {st.session_state['retrieved_text']}"
             elif content_type == "Tweet":
                 prompt = f"Compose a tweet using the {st.session_state['retrieved_text']}"
             response = openai.ChatCompletion.create(
-                model="gpt-3.5-turbo-16k",
+                model="gpt-3.5-turbo-0125",  # Updated model
                 messages=[
                     {"role": "system", "content": "You are a helpful assistant."},
                     {"role": "user", "content": prompt}
                 ]
             )
-            generated_text = response['choices'][0]['message']['content']
+            generated_text = response.choices[0].message.content
             st.write(f"Generated Text: {generated_text}")
         except Exception as e:
             st.write(f"An error occurred: {e}")