Create multiple.py

multiple.py

@@ -0,0 +1,83 @@
+import streamlit as st
+import os
+import tempfile
+from llama_index import (
+    ServiceContext,
+    SimpleDirectoryReader,
+    VectorStoreIndex,
+)
+from llama_index.llms import OpenAI
+import openai
+
+st.title("Grounded Generation")
+
+uploaded_files = st.file_uploader("Choose PDF files", type="pdf", accept_multiple_files=True)
+
+@st.cache_resource(show_spinner=False)
+def load_data(uploaded_files):
+    with st.spinner('Indexing documents...'):
+        temp_dir = tempfile.mkdtemp()  # Create temporary directory
+        file_paths = []  # List to store paths of saved files
+
+        # Save the uploaded files temporarily
+        for i, uploaded_file in enumerate(uploaded_files):
+            temp_path = os.path.join(temp_dir, f"temp_{i}.pdf")
+            with open(temp_path, "wb") as f:
+                f.write(uploaded_file.read())
+            file_paths.append(temp_path)
+
+        # Read and index documents using SimpleDirectoryReader
+        reader = SimpleDirectoryReader(input_dir=temp_dir, recursive=False)
+        docs = reader.load_data()
+        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 PDFs to assist the user in generating text."
+        )
+        index = VectorStoreIndex.from_documents(docs, service_context=service_context)
+
+        # Clean up temporary files and directory
+        for file_path in file_paths:
+            os.remove(file_path)
+        os.rmdir(temp_dir)
+
+    return index
+
+if uploaded_files:
+    index = load_data(uploaded_files)
+
+user_query = st.text_input("Search for the products/info you want to use to ground your generated text content:")
+
+if 'retrieved_text' not in st.session_state:
+    st.session_state['retrieved_text'] = ''
+
+if st.button("Retrieve"):
+    with st.spinner('Retrieving text...'):
+        query_engine = index.as_query_engine(similarity_top_k=1)
+        st.session_state['retrieved_text'] = query_engine.query(user_query)
+        st.write(f"Retrieved Text: {st.session_state['retrieved_text']}")
+
+content_type = st.selectbox("Select content type:", ["Blog", "Tweet"])
+
+if st.button("Generate") and content_type:
+    with st.spinner('Generating text...'):
+        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']}"
+            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",
+                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}")
+