Renamed midterm_app.py to app.py and updated Dockerfile

Dockerfile

@@ -24,4 +24,4 @@ RUN pip install --no-cache-dir -r requirements.txt
 COPY --chown=user . $HOME/app
 
 # Run the application
-CMD ["chainlit", "run", "midterm_app.py", "--port", "7860"]
+CMD ["chainlit", "run", "app.py", "--port", "7860"]

app.py

@@ -0,0 +1,124 @@
+# Import Required Libraries
+import os
+from dotenv import load_dotenv
+
+import openai
+import fitz  # PyMuPDF
+import pandas as pd
+from transformers import pipeline
+from qdrant_client import QdrantClient
+from qdrant_client.http import models as qdrant_models
+import chainlit as cl
+import tiktoken
+
+# Specific imports from the libraries
+from langchain.document_loaders import PyMuPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.embeddings import OpenAIEmbeddings
+#old import from langchain_openai import OpenAIEmbeddings
+from langchain_community.vectorstores import Qdrant
+from langchain.prompts import ChatPromptTemplate
+from langchain.chat_models import ChatOpenAI
+#old import from langchain_openai import ChatOpenAI
+from operator import itemgetter
+from langchain.schema.output_parser import StrOutputParser
+from langchain.schema.runnable import RunnablePassthrough
+
+# Set Environment Variables
+load_dotenv()
+
+# Load environment variables
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+
+# Initialize OpenAI client after loading the environment variables
+openai.api_key = OPENAI_API_KEY
+
+# Load and split documents
+loader = PyMuPDFLoader("/home/user/app/data/airbnb_q1_2024.pdf")
+#old file path is loader = PyMuPDFLoader("/Users/sampazar/AIE3-Midterm/data/airbnb_q1_2024.pdf")
+documents = loader.load()
+
+def tiktoken_len(text):
+    tokens = tiktoken.encoding_for_model("gpt-4o").encode(text)
+    return len(tokens)
+
+text_splitter = RecursiveCharacterTextSplitter(
+    chunk_size=150, 
+    chunk_overlap=100,
+    length_function = tiktoken_len
+)
+
+split_chunks = text_splitter.split_documents(documents)
+
+
+# Load OpenAI Embeddings Model
+embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
+
+# Creating a Qdrant Vector Store
+qdrant_vector_store = Qdrant.from_documents(
+    split_chunks,
+    embeddings,
+    location=":memory:",
+    collection_name="Airbnb_Q1_2024",
+)
+
+# Create a Retriever
+retriever = qdrant_vector_store.as_retriever()
+
+# Create a prompt template
+template = """Answer the question based only on the following context. If you cannot answer the question with the context, please respond with 'I don't know':
+
+Context:
+{context}
+
+Question:
+{question}
+"""
+
+prompt = ChatPromptTemplate.from_template(template)
+
+# Define the primary LLM
+primary_llm = ChatOpenAI(model_name="gpt-4o", temperature=0)
+
+# Creating a Retrieval Augmented Generation (RAG) Chain
+retrieval_augmented_qa_chain = (
+    # INVOKE CHAIN WITH: {"question" : "<>"}
+    # "question" : populated by getting the value of the "question" key
+    # "context"  : populated by getting the value of the "question" key and chaining it into the base_retriever
+    {"context": itemgetter("question") | retriever, "question": itemgetter("question")}
+    # "context"  : is assigned to a RunnablePassthrough object (will not be called or considered in the next step)
+    #              by getting the value of the "context" key from the previous step
+    | RunnablePassthrough.assign(context=itemgetter("context"))
+    # "response" : the "context" and "question" values are used to format our prompt object and then piped
+    #              into the LLM and stored in a key called "response"
+    # "context"  : populated by getting the value of the "context" key from the previous step
+    | {"response": prompt | primary_llm, "context": itemgetter("context")}
+)
+
+# Chainlit integration for deployment
+@cl.on_chat_start  # marks a function that will be executed at the start of a user session
+async def start_chat():
+    settings = {
+        "model": "gpt-4o",
+        "temperature": 0,
+        "max_tokens": 500,
+        "top_p": 1,
+        "frequency_penalty": 0,
+        "presence_penalty": 0,
+    }
+    cl.user_session.set("settings", settings)
+
+@cl.on_message  # marks a function that should be run each time the chatbot receives a message from a user
+async def handle_message(message: cl.Message):
+    settings = cl.user_session.get("settings")
+
+    response = retrieval_augmented_qa_chain.invoke({"question": message.content})
+    
+    #msg = cl.Message(content=response["response"])
+    #await msg.send()
+
+    # Extracting and sending just the content
+    content = response["response"].content
+    pretty_content = content.strip()  # Remove any leading/trailing whitespace
+
+    await cl.Message(content=pretty_content).send()