Create app.py

app.py

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+# Adapted from https://docs.streamlit.io/knowledge-base/tutorials/build-conversational-apps#build-a-simple-chatbot-gui-with-streaming
+import os
+
+import base64
+import gc
+import random
+import tempfile
+import time
+import uuid
+
+from IPython.display import Markdown, display
+
+from llama_index.core import Settings
+from llama_index.llms.ollama import Ollama
+from llama_index.core import PromptTemplate
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+from llama_index.core import VectorStoreIndex, ServiceContext, SimpleDirectoryReader
+
+import streamlit as st
+
+
+if "id" not in st.session_state:
+    st.session_state.id = uuid.uuid4()
+    st.session_state.file_cache = {}
+
+session_id = st.session_state.id
+client = None
+
+def reset_chat():
+    st.session_state.messages = []
+    st.session_state.context = None
+    gc.collect()
+
+
+def display_pdf(file):
+    # Opening file from file path
+
+    st.markdown("### PDF Preview")
+    base64_pdf = base64.b64encode(file.read()).decode("utf-8")
+
+    # Embedding PDF in HTML
+    pdf_display = f"""<iframe src="data:application/pdf;base64,{base64_pdf}" width="400" height="100%" type="application/pdf"
+                        style="height:100vh; width:100%"
+                    >
+                    </iframe>"""
+
+    # Displaying File
+    st.markdown(pdf_display, unsafe_allow_html=True)
+
+
+with st.sidebar:
+    st.header(f"Add your documents!")
+    
+    uploaded_file = st.file_uploader("Choose your `.pdf` file", type="pdf")
+
+    if uploaded_file:
+        try:
+            with tempfile.TemporaryDirectory() as temp_dir:
+                file_path = os.path.join(temp_dir, uploaded_file.name)
+                
+                with open(file_path, "wb") as f:
+                    f.write(uploaded_file.getvalue())
+                
+                file_key = f"{session_id}-{uploaded_file.name}"
+                st.write("Indexing your document...")
+
+                if file_key not in st.session_state.get('file_cache', {}):
+
+                    if os.path.exists(temp_dir):
+                            loader = SimpleDirectoryReader(
+                                input_dir = temp_dir,
+                                required_exts=[".pdf"],
+                                recursive=True
+                            )
+                    else:    
+                        st.error('Could not find the file you uploaded, please check again...')
+                        st.stop()
+                    
+                    docs = loader.load_data()
+
+                    # setup llm & embedding model
+                    llm=Ollama(model="llama3", request_timeout=120.0)
+                    embed_model = HuggingFaceEmbedding( model_name="BAAI/bge-large-en-v1.5", trust_remote_code=True)
+                    # Creating an index over loaded data
+                    Settings.embed_model = embed_model
+                    index = VectorStoreIndex.from_documents(docs, show_progress=True)
+
+                    # Create the query engine, where we use a cohere reranker on the fetched nodes
+                    Settings.llm = llm
+                    query_engine = index.as_query_engine(streaming=True)
+
+                    # ====== Customise prompt template ======
+                    qa_prompt_tmpl_str = (
+                    "Context information is below.\n"
+                    "---------------------\n"
+                    "{context_str}\n"
+                    "---------------------\n"
+                    "Given the context information above I want you to think step by step to answer the query in a crisp manner, incase case you don't know the answer say 'I don't know!'.\n"
+                    "Query: {query_str}\n"
+                    "Answer: "
+                    )
+                    qa_prompt_tmpl = PromptTemplate(qa_prompt_tmpl_str)
+
+                    query_engine.update_prompts(
+                        {"response_synthesizer:text_qa_template": qa_prompt_tmpl}
+                    )
+                    
+                    st.session_state.file_cache[file_key] = query_engine
+                else:
+                    query_engine = st.session_state.file_cache[file_key]
+
+                # Inform the user that the file is processed and Display the PDF uploaded
+                st.success("Ready to Chat!")
+                display_pdf(uploaded_file)
+        except Exception as e:
+            st.error(f"An error occurred: {e}")
+            st.stop()     
+
+col1, col2 = st.columns([6, 1])
+
+with col1:
+    st.header(f"Chat with Docs using Llama-3")
+
+with col2:
+    st.button("Clear ↺", on_click=reset_chat)
+
+# Initialize chat history
+if "messages" not in st.session_state:
+    reset_chat()
+
+
+# Display chat messages from history on app rerun
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+
+# Accept user input
+if prompt := st.chat_input("What's up?"):
+    # Add user message to chat history
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    # Display user message in chat message container
+    with st.chat_message("user"):
+        st.markdown(prompt)
+
+    # Display assistant response in chat message container
+    with st.chat_message("assistant"):
+        message_placeholder = st.empty()
+        full_response = ""
+        
+        # Simulate stream of response with milliseconds delay
+        streaming_response = query_engine.query(prompt)
+        
+        for chunk in streaming_response.response_gen:
+            full_response += chunk
+            message_placeholder.markdown(full_response + "▌")
+
+        # full_response = query_engine.query(prompt)
+
+        message_placeholder.markdown(full_response)
+        # st.session_state.context = ctx
+
+    # Add assistant response to chat history
+    st.session_state.messages.append({"role": "assistant", "content": full_response})