docker file and formatting

Dockerfile

@@ -0,0 +1,32 @@
+# Use an official Python runtime as a parent image
+FROM python:3.12
+
+# Set the working directory in the container
+WORKDIR /app
+
+# Copy the current directory contents into the container at /app
+COPY . /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    curl \
+    software-properties-common \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install Python dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Make port 7860 available to the world outside this container
+EXPOSE 7860
+
+# Create a script to run both FastAPI and Streamlit
+RUN echo '#!/bin/bash\n\
+uvicorn main:app --host 0.0.0.0 --port 8000 &\n\
+streamlit run streamlit_app.py --server.port 7860 --server.address 0.0.0.0\n\
+' > /app/run.sh
+
+RUN chmod +x /app/run.sh
+
+# Run the script when the container launches
+CMD ["/app/run.sh"]

app/app.py

@@ -0,0 +1,62 @@
+from llama_index.core import StorageContext, load_index_from_storage, VectorStoreIndex, SimpleDirectoryReader, ChatPromptTemplate
+from llama_index.llms.huggingface import HuggingFaceInferenceAPI
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+from llama_index.core import Settings
+import os
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+
+
+# Configure the Llama index settings
+Settings.llm = HuggingFaceInferenceAPI(
+    model_name="meta-llama/Meta-Llama-3-8B-Instruct",
+    tokenizer_name="meta-llama/Meta-Llama-3-8B-Instruct",
+    context_window=3900,
+    token=os.getenv("HF_TOKEN"),
+    max_new_tokens=1000,
+    generate_kwargs={"temperature": 0.5},
+)
+Settings.embed_model = HuggingFaceEmbedding(
+    model_name="BAAI/bge-small-en-v1.5"
+)
+
+# Define the directory for persistent storage and data
+PERSIST_DIR = "./db"
+DATA_DIR = "data"
+
+# Ensure data directory exists
+os.makedirs(DATA_DIR, exist_ok=True)
+os.makedirs(PERSIST_DIR, exist_ok=True)
+
+def data_ingestion():
+    documents = SimpleDirectoryReader(DATA_DIR).load_data()
+    storage_context = StorageContext.from_defaults()
+    index = VectorStoreIndex.from_documents(documents)
+    index.storage_context.persist(persist_dir=PERSIST_DIR)
+
+def handle_query(query):
+    storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR)
+    index = load_index_from_storage(storage_context)
+    chat_text_qa_msgs = [
+        (
+            "user",
+            """You are Q&A assistant named CHAT-DOC. Your main goal is to provide answers as accurately as possible, based on the instructions and context you have been given. If a question does not match the provided context or is outside the scope of the document, kindly advise the user to ask questions within the context of the document.
+            Context:
+            {context_str}
+            Question:
+            {query_str}
+            """
+        )
+    ]
+    text_qa_template = ChatPromptTemplate.from_messages(chat_text_qa_msgs)
+    query_engine = index.as_query_engine(text_qa_template=text_qa_template)
+    answer = query_engine.query(query)
+    
+    if hasattr(answer, 'response'):
+        return answer.response
+    elif isinstance(answer, dict) and 'response' in answer:
+        return answer['response']
+    else:
+        return "Sorry, I couldn't find an answer."

app/streamlit_app.py

@@ -0,0 +1,50 @@
+import streamlit as st
+import requests
+
+
+# Streamlit UI
+def streamlit_ui():
+    st.title("Chat with your Document 📄")
+    st.markdown("Chat here👇")
+
+    icons = {"assistant": "🤖", "user": "👤"}
+
+    if 'messages' not in st.session_state:
+        st.session_state.messages = [{'role': 'assistant', "content": 'Hello! Upload a PDF, DOCX, or TXT file and ask me anything about its content.'}]
+
+    for message in st.session_state.messages:
+        with st.chat_message(message['role'], avatar=icons[message['role']]):
+            st.write(message['content'])
+
+    with st.sidebar:
+        st.title("Menu:")
+        uploaded_file = st.file_uploader("Upload your document (PDF, DOCX, TXT)", type=["pdf", "docx", "txt"])
+        if st.button("Submit & Process") and uploaded_file:
+            with st.spinner("Processing..."):
+                files = {"file": (uploaded_file.name, uploaded_file.getvalue(), uploaded_file.type)}
+                response = requests.post("http://localhost:8000/upload", files=files)
+                if response.status_code == 200:
+                    st.success("File uploaded and processed successfully")
+                else:
+                    st.error("Error uploading file")
+
+    user_prompt = st.chat_input("Ask me anything about the content of the document:")
+
+    if user_prompt:
+        st.session_state.messages.append({'role': 'user', "content": user_prompt})
+        with st.chat_message("user", avatar=icons["user"]):
+            st.write(user_prompt)
+
+        # Trigger assistant's response retrieval and update UI
+        with st.spinner("Thinking..."):
+            response = requests.post("http://localhost:8000/query", json={"question": user_prompt})
+            if response.status_code == 200:
+                assistant_response = response.json()["response"]
+                with st.chat_message("assistant", avatar=icons["assistant"]):
+                    st.write(assistant_response)
+                st.session_state.messages.append({'role': 'assistant', "content": assistant_response})
+            else:
+                st.error("Error querying document")
+
+if __name__ == "__main__":
+    streamlit_ui()

main.py

@@ -1,79 +1,16 @@
-# File: main.py
-from fastapi import FastAPI, UploadFile, File, HTTPException
-from pydantic import BaseModel
-from llama_index.core import StorageContext, load_index_from_storage, VectorStoreIndex, SimpleDirectoryReader, ChatPromptTemplate
-from llama_index.llms.huggingface import HuggingFaceInferenceAPI
-from llama_index.embeddings.huggingface import HuggingFaceEmbedding
-from llama_index.core import Settings
 import os
-from dotenv import load_dotenv
 import shutil
 import uvicorn
-import streamlit as st
-import requests
-import base64
-import docx2txt
-import threading
-
-# Load environment variables
-load_dotenv()
-
-app = FastAPI()
-
-# Configure the Llama index settings
-Settings.llm = HuggingFaceInferenceAPI(
-    model_name="meta-llama/Meta-Llama-3-8B-Instruct",
-    tokenizer_name="meta-llama/Meta-Llama-3-8B-Instruct",
-    context_window=3900,
-    token=os.getenv("HF_TOKEN"),
-    max_new_tokens=1000,
-    generate_kwargs={"temperature": 0.5},
-)
-Settings.embed_model = HuggingFaceEmbedding(
-    model_name="BAAI/bge-small-en-v1.5"
-)
 
-# Define the directory for persistent storage and data
-PERSIST_DIR = "./db"
-DATA_DIR = "data"
+from pydantic import BaseModel
+from fastapi import FastAPI, File, UploadFile, HTTPException
 
-# Ensure data directory exists
-os.makedirs(DATA_DIR, exist_ok=True)
-os.makedirs(PERSIST_DIR, exist_ok=True)
+from app.app import data_ingestion, handle_query, DATA_DIR
 
 class Query(BaseModel):
     question: str
 
-def data_ingestion():
-    documents = SimpleDirectoryReader(DATA_DIR).load_data()
-    storage_context = StorageContext.from_defaults()
-    index = VectorStoreIndex.from_documents(documents)
-    index.storage_context.persist(persist_dir=PERSIST_DIR)
-
-def handle_query(query):
-    storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR)
-    index = load_index_from_storage(storage_context)
-    chat_text_qa_msgs = [
-        (
-            "user",
-            """You are Q&A assistant named CHAT-DOC. Your main goal is to provide answers as accurately as possible, based on the instructions and context you have been given. If a question does not match the provided context or is outside the scope of the document, kindly advise the user to ask questions within the context of the document.
-            Context:
-            {context_str}
-            Question:
-            {query_str}
-            """
-        )
-    ]
-    text_qa_template = ChatPromptTemplate.from_messages(chat_text_qa_msgs)
-    query_engine = index.as_query_engine(text_qa_template=text_qa_template)
-    answer = query_engine.query(query)
-    
-    if hasattr(answer, 'response'):
-        return answer.response
-    elif isinstance(answer, dict) and 'response' in answer:
-        return answer['response']
-    else:
-        return "Sorry, I couldn't find an answer."
+app = FastAPI()
 
 @app.post("/upload")
 async def upload_file(file: UploadFile = File(...)):
@@ -96,57 +33,5 @@ async def query_document(query: Query):
     response = handle_query(query.question)
     return {"response": response}
 
-# Streamlit UI
-def streamlit_ui():
-    st.title("Chat with your Document 📄")
-    st.markdown("Chat here👇")
-
-    icons = {"assistant": "🤖", "user": "👤"}
-
-    if 'messages' not in st.session_state:
-        st.session_state.messages = [{'role': 'assistant', "content": 'Hello! Upload a PDF, DOCX, or TXT file and ask me anything about its content.'}]
-
-    for message in st.session_state.messages:
-        with st.chat_message(message['role'], avatar=icons[message['role']]):
-            st.write(message['content'])
-
-    with st.sidebar:
-        st.title("Menu:")
-        uploaded_file = st.file_uploader("Upload your document (PDF, DOCX, TXT)", type=["pdf", "docx", "txt"])
-        if st.button("Submit & Process") and uploaded_file:
-            with st.spinner("Processing..."):
-                files = {"file": (uploaded_file.name, uploaded_file.getvalue(), uploaded_file.type)}
-                response = requests.post("http://localhost:8000/upload", files=files)
-                if response.status_code == 200:
-                    st.success("File uploaded and processed successfully")
-                else:
-                    st.error("Error uploading file")
-
-    user_prompt = st.chat_input("Ask me anything about the content of the document:")
-
-    if user_prompt:
-        st.session_state.messages.append({'role': 'user', "content": user_prompt})
-        with st.chat_message("user", avatar=icons["user"]):
-            st.write(user_prompt)
-
-        # Trigger assistant's response retrieval and update UI
-        with st.spinner("Thinking..."):
-            response = requests.post("http://localhost:8000/query", json={"question": user_prompt})
-            if response.status_code == 200:
-                assistant_response = response.json()["response"]
-                with st.chat_message("assistant", avatar=icons["assistant"]):
-                    st.write(assistant_response)
-                st.session_state.messages.append({'role': 'assistant', "content": assistant_response})
-            else:
-                st.error("Error querying document")
-
-def run_fastapi():
-    uvicorn.run(app, host="0.0.0.0", port=8000)
-
 if __name__ == "__main__":
-    # Start FastAPI in a separate thread
-    fastapi_thread = threading.Thread(target=run_fastapi)
-    fastapi_thread.start()
-
-    # Run Streamlit (this will run in the main thread)
-    streamlit_ui()
+    uvicorn.run(app, host="0.0.0.0", port=8000)

requirements.txt

@@ -1,11 +1,11 @@
-streamlit
-python-dotenv
-llama-index
-llama-index-embeddings-huggingface
-llama-index-llms-huggingface
-gradio==3.50
-docx2txt
-tf-keras
-fastapi
-pydantic
-uvicorn
+streamlit
+python-dotenv
+llama-index
+llama-index-embeddings-huggingface
+llama-index-llms-huggingface
+gradio==3.50
+docx2txt
+tf-keras
+fastapi
+pydantic
+uvicorn