Create app.py

app.py

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+import streamlit as st
+import os
+from llama_index import (
+    ServiceContext,
+    SimpleDirectoryReader,
+    VectorStoreIndex,
+)
+
+# 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")
+
+# Upload PDF
+uploaded_file = st.file_uploader("Choose a PDF file", type="pdf")
+
+@st.cache_resource(show_spinner=False)
+def load_data(uploaded_file):
+    with st.spinner('Indexing document...'):
+        # Save the uploaded file temporarily
+        with open("temp.pdf", "wb") as f:
+            f.write(uploaded_file.read())
+        # 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(
+            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)
+    return index
+
+# Placeholder for document indexing
+if uploaded_file is not None:
+    index = load_data(uploaded_file)
+
+# Take user query input
+user_query = st.text_input("Search for the products/info you want to use to ground your generated text content:")
+
+# Initialize session_state for retrieved_text if not already present
+if 'retrieved_text' not in st.session_state:
+    st.session_state['retrieved_text'] = ''
+
+# Search and display retrieved text
+if st.button("Retrieve"):
+    with st.spinner('Retrieving text...'):
+        # 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']}")
+
+# Select content type
+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...'):
+        try:
+            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}
+                                                      ])
+            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}")