Update app.py

app.py

@@ -5,10 +5,11 @@ from llama_index import (
     SimpleDirectoryReader,
     VectorStoreIndex,
 )
-from llama_index.llms import OpenAI
-from openai import OpenAI
 
-client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+# Import OpenAI only once, avoiding naming conflict
+from openai import OpenAI as OpenAIClient
+
+client = OpenAIClient(api_key=os.getenv("OPENAI_API_KEY"))
 
 # Define Streamlit layout and interaction
 st.title("Grounded Generations")
@@ -25,11 +26,8 @@ def load_data(uploaded_file):
         # Read and index documents using SimpleDirectoryReader
         reader = SimpleDirectoryReader(input_dir="./", recursive=False)
         docs = reader.load_data()
+        # The model configuration should be moved to where you actually call the OpenAI API
         service_context = ServiceContext.from_defaults(
-            llm=OpenAI(
-                model="gpt-3.5-turbo-16k",
-                temperature=0.1,
-            ),
             system_prompt="You are an AI assistant that uses context from a PDF to assist the user in generating text."
         )
         index = VectorStoreIndex.from_documents(docs, service_context=service_context)
@@ -52,26 +50,22 @@ if st.button("Retrieve"):
         # Use VectorStoreIndex to search
         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:
     with st.spinner('Generating text...'):
-        # Generate text using OpenAI API
         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
-            elif content_type == "Tweet":
-                prompt = f"Compose a tweet using the {st.session_state['retrieved_text']}"
+            prompt = f"Write a blog about 500 words in length using {st.session_state['retrieved_text']}" if content_type == "Blog" else f"Compose a tweet using {st.session_state['retrieved_text']}"
             response = client.chat.completions.create(model="gpt-3.5-turbo-16k",
-            messages=[
-                {"role": "system", "content": "You are a helpful assistant."},
-                {"role": "user", "content": prompt}
-            ])
+                                                      messages=[
+                                                          {"role": "system", "content": "You are a helpful assistant."},
+                                                          {"role": "user", "content": prompt}
+                                                      ])
             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}")
+            st.write(f"An error occurred: {e}")