docker file and formatting
Browse files- Dockerfile +32 -0
- app/__init__.py +0 -0
- app/app.py +62 -0
- app/streamlit_app.py +50 -0
- main.py +5 -120
- requirements.txt +11 -11
Dockerfile
ADDED
@@ -0,0 +1,32 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Use an official Python runtime as a parent image
|
2 |
+
FROM python:3.12
|
3 |
+
|
4 |
+
# Set the working directory in the container
|
5 |
+
WORKDIR /app
|
6 |
+
|
7 |
+
# Copy the current directory contents into the container at /app
|
8 |
+
COPY . /app
|
9 |
+
|
10 |
+
# Install system dependencies
|
11 |
+
RUN apt-get update && apt-get install -y \
|
12 |
+
build-essential \
|
13 |
+
curl \
|
14 |
+
software-properties-common \
|
15 |
+
&& rm -rf /var/lib/apt/lists/*
|
16 |
+
|
17 |
+
# Install Python dependencies
|
18 |
+
RUN pip install --no-cache-dir -r requirements.txt
|
19 |
+
|
20 |
+
# Make port 7860 available to the world outside this container
|
21 |
+
EXPOSE 7860
|
22 |
+
|
23 |
+
# Create a script to run both FastAPI and Streamlit
|
24 |
+
RUN echo '#!/bin/bash\n\
|
25 |
+
uvicorn main:app --host 0.0.0.0 --port 8000 &\n\
|
26 |
+
streamlit run streamlit_app.py --server.port 7860 --server.address 0.0.0.0\n\
|
27 |
+
' > /app/run.sh
|
28 |
+
|
29 |
+
RUN chmod +x /app/run.sh
|
30 |
+
|
31 |
+
# Run the script when the container launches
|
32 |
+
CMD ["/app/run.sh"]
|
app/__init__.py
ADDED
File without changes
|
app/app.py
ADDED
@@ -0,0 +1,62 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from llama_index.core import StorageContext, load_index_from_storage, VectorStoreIndex, SimpleDirectoryReader, ChatPromptTemplate
|
2 |
+
from llama_index.llms.huggingface import HuggingFaceInferenceAPI
|
3 |
+
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
|
4 |
+
from llama_index.core import Settings
|
5 |
+
import os
|
6 |
+
from dotenv import load_dotenv
|
7 |
+
|
8 |
+
# Load environment variables
|
9 |
+
load_dotenv()
|
10 |
+
|
11 |
+
|
12 |
+
# Configure the Llama index settings
|
13 |
+
Settings.llm = HuggingFaceInferenceAPI(
|
14 |
+
model_name="meta-llama/Meta-Llama-3-8B-Instruct",
|
15 |
+
tokenizer_name="meta-llama/Meta-Llama-3-8B-Instruct",
|
16 |
+
context_window=3900,
|
17 |
+
token=os.getenv("HF_TOKEN"),
|
18 |
+
max_new_tokens=1000,
|
19 |
+
generate_kwargs={"temperature": 0.5},
|
20 |
+
)
|
21 |
+
Settings.embed_model = HuggingFaceEmbedding(
|
22 |
+
model_name="BAAI/bge-small-en-v1.5"
|
23 |
+
)
|
24 |
+
|
25 |
+
# Define the directory for persistent storage and data
|
26 |
+
PERSIST_DIR = "./db"
|
27 |
+
DATA_DIR = "data"
|
28 |
+
|
29 |
+
# Ensure data directory exists
|
30 |
+
os.makedirs(DATA_DIR, exist_ok=True)
|
31 |
+
os.makedirs(PERSIST_DIR, exist_ok=True)
|
32 |
+
|
33 |
+
def data_ingestion():
|
34 |
+
documents = SimpleDirectoryReader(DATA_DIR).load_data()
|
35 |
+
storage_context = StorageContext.from_defaults()
|
36 |
+
index = VectorStoreIndex.from_documents(documents)
|
37 |
+
index.storage_context.persist(persist_dir=PERSIST_DIR)
|
38 |
+
|
39 |
+
def handle_query(query):
|
40 |
+
storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR)
|
41 |
+
index = load_index_from_storage(storage_context)
|
42 |
+
chat_text_qa_msgs = [
|
43 |
+
(
|
44 |
+
"user",
|
45 |
+
"""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.
|
46 |
+
Context:
|
47 |
+
{context_str}
|
48 |
+
Question:
|
49 |
+
{query_str}
|
50 |
+
"""
|
51 |
+
)
|
52 |
+
]
|
53 |
+
text_qa_template = ChatPromptTemplate.from_messages(chat_text_qa_msgs)
|
54 |
+
query_engine = index.as_query_engine(text_qa_template=text_qa_template)
|
55 |
+
answer = query_engine.query(query)
|
56 |
+
|
57 |
+
if hasattr(answer, 'response'):
|
58 |
+
return answer.response
|
59 |
+
elif isinstance(answer, dict) and 'response' in answer:
|
60 |
+
return answer['response']
|
61 |
+
else:
|
62 |
+
return "Sorry, I couldn't find an answer."
|
app/streamlit_app.py
ADDED
@@ -0,0 +1,50 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import streamlit as st
|
2 |
+
import requests
|
3 |
+
|
4 |
+
|
5 |
+
# Streamlit UI
|
6 |
+
def streamlit_ui():
|
7 |
+
st.title("Chat with your Document 📄")
|
8 |
+
st.markdown("Chat here👇")
|
9 |
+
|
10 |
+
icons = {"assistant": "🤖", "user": "👤"}
|
11 |
+
|
12 |
+
if 'messages' not in st.session_state:
|
13 |
+
st.session_state.messages = [{'role': 'assistant', "content": 'Hello! Upload a PDF, DOCX, or TXT file and ask me anything about its content.'}]
|
14 |
+
|
15 |
+
for message in st.session_state.messages:
|
16 |
+
with st.chat_message(message['role'], avatar=icons[message['role']]):
|
17 |
+
st.write(message['content'])
|
18 |
+
|
19 |
+
with st.sidebar:
|
20 |
+
st.title("Menu:")
|
21 |
+
uploaded_file = st.file_uploader("Upload your document (PDF, DOCX, TXT)", type=["pdf", "docx", "txt"])
|
22 |
+
if st.button("Submit & Process") and uploaded_file:
|
23 |
+
with st.spinner("Processing..."):
|
24 |
+
files = {"file": (uploaded_file.name, uploaded_file.getvalue(), uploaded_file.type)}
|
25 |
+
response = requests.post("http://localhost:8000/upload", files=files)
|
26 |
+
if response.status_code == 200:
|
27 |
+
st.success("File uploaded and processed successfully")
|
28 |
+
else:
|
29 |
+
st.error("Error uploading file")
|
30 |
+
|
31 |
+
user_prompt = st.chat_input("Ask me anything about the content of the document:")
|
32 |
+
|
33 |
+
if user_prompt:
|
34 |
+
st.session_state.messages.append({'role': 'user', "content": user_prompt})
|
35 |
+
with st.chat_message("user", avatar=icons["user"]):
|
36 |
+
st.write(user_prompt)
|
37 |
+
|
38 |
+
# Trigger assistant's response retrieval and update UI
|
39 |
+
with st.spinner("Thinking..."):
|
40 |
+
response = requests.post("http://localhost:8000/query", json={"question": user_prompt})
|
41 |
+
if response.status_code == 200:
|
42 |
+
assistant_response = response.json()["response"]
|
43 |
+
with st.chat_message("assistant", avatar=icons["assistant"]):
|
44 |
+
st.write(assistant_response)
|
45 |
+
st.session_state.messages.append({'role': 'assistant', "content": assistant_response})
|
46 |
+
else:
|
47 |
+
st.error("Error querying document")
|
48 |
+
|
49 |
+
if __name__ == "__main__":
|
50 |
+
streamlit_ui()
|
main.py
CHANGED
@@ -1,79 +1,16 @@
|
|
1 |
-
# File: main.py
|
2 |
-
from fastapi import FastAPI, UploadFile, File, HTTPException
|
3 |
-
from pydantic import BaseModel
|
4 |
-
from llama_index.core import StorageContext, load_index_from_storage, VectorStoreIndex, SimpleDirectoryReader, ChatPromptTemplate
|
5 |
-
from llama_index.llms.huggingface import HuggingFaceInferenceAPI
|
6 |
-
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
|
7 |
-
from llama_index.core import Settings
|
8 |
import os
|
9 |
-
from dotenv import load_dotenv
|
10 |
import shutil
|
11 |
import uvicorn
|
12 |
-
import streamlit as st
|
13 |
-
import requests
|
14 |
-
import base64
|
15 |
-
import docx2txt
|
16 |
-
import threading
|
17 |
-
|
18 |
-
# Load environment variables
|
19 |
-
load_dotenv()
|
20 |
-
|
21 |
-
app = FastAPI()
|
22 |
-
|
23 |
-
# Configure the Llama index settings
|
24 |
-
Settings.llm = HuggingFaceInferenceAPI(
|
25 |
-
model_name="meta-llama/Meta-Llama-3-8B-Instruct",
|
26 |
-
tokenizer_name="meta-llama/Meta-Llama-3-8B-Instruct",
|
27 |
-
context_window=3900,
|
28 |
-
token=os.getenv("HF_TOKEN"),
|
29 |
-
max_new_tokens=1000,
|
30 |
-
generate_kwargs={"temperature": 0.5},
|
31 |
-
)
|
32 |
-
Settings.embed_model = HuggingFaceEmbedding(
|
33 |
-
model_name="BAAI/bge-small-en-v1.5"
|
34 |
-
)
|
35 |
|
36 |
-
|
37 |
-
|
38 |
-
DATA_DIR = "data"
|
39 |
|
40 |
-
|
41 |
-
os.makedirs(DATA_DIR, exist_ok=True)
|
42 |
-
os.makedirs(PERSIST_DIR, exist_ok=True)
|
43 |
|
44 |
class Query(BaseModel):
|
45 |
question: str
|
46 |
|
47 |
-
|
48 |
-
documents = SimpleDirectoryReader(DATA_DIR).load_data()
|
49 |
-
storage_context = StorageContext.from_defaults()
|
50 |
-
index = VectorStoreIndex.from_documents(documents)
|
51 |
-
index.storage_context.persist(persist_dir=PERSIST_DIR)
|
52 |
-
|
53 |
-
def handle_query(query):
|
54 |
-
storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR)
|
55 |
-
index = load_index_from_storage(storage_context)
|
56 |
-
chat_text_qa_msgs = [
|
57 |
-
(
|
58 |
-
"user",
|
59 |
-
"""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.
|
60 |
-
Context:
|
61 |
-
{context_str}
|
62 |
-
Question:
|
63 |
-
{query_str}
|
64 |
-
"""
|
65 |
-
)
|
66 |
-
]
|
67 |
-
text_qa_template = ChatPromptTemplate.from_messages(chat_text_qa_msgs)
|
68 |
-
query_engine = index.as_query_engine(text_qa_template=text_qa_template)
|
69 |
-
answer = query_engine.query(query)
|
70 |
-
|
71 |
-
if hasattr(answer, 'response'):
|
72 |
-
return answer.response
|
73 |
-
elif isinstance(answer, dict) and 'response' in answer:
|
74 |
-
return answer['response']
|
75 |
-
else:
|
76 |
-
return "Sorry, I couldn't find an answer."
|
77 |
|
78 |
@app.post("/upload")
|
79 |
async def upload_file(file: UploadFile = File(...)):
|
@@ -96,57 +33,5 @@ async def query_document(query: Query):
|
|
96 |
response = handle_query(query.question)
|
97 |
return {"response": response}
|
98 |
|
99 |
-
# Streamlit UI
|
100 |
-
def streamlit_ui():
|
101 |
-
st.title("Chat with your Document 📄")
|
102 |
-
st.markdown("Chat here👇")
|
103 |
-
|
104 |
-
icons = {"assistant": "🤖", "user": "👤"}
|
105 |
-
|
106 |
-
if 'messages' not in st.session_state:
|
107 |
-
st.session_state.messages = [{'role': 'assistant', "content": 'Hello! Upload a PDF, DOCX, or TXT file and ask me anything about its content.'}]
|
108 |
-
|
109 |
-
for message in st.session_state.messages:
|
110 |
-
with st.chat_message(message['role'], avatar=icons[message['role']]):
|
111 |
-
st.write(message['content'])
|
112 |
-
|
113 |
-
with st.sidebar:
|
114 |
-
st.title("Menu:")
|
115 |
-
uploaded_file = st.file_uploader("Upload your document (PDF, DOCX, TXT)", type=["pdf", "docx", "txt"])
|
116 |
-
if st.button("Submit & Process") and uploaded_file:
|
117 |
-
with st.spinner("Processing..."):
|
118 |
-
files = {"file": (uploaded_file.name, uploaded_file.getvalue(), uploaded_file.type)}
|
119 |
-
response = requests.post("http://localhost:8000/upload", files=files)
|
120 |
-
if response.status_code == 200:
|
121 |
-
st.success("File uploaded and processed successfully")
|
122 |
-
else:
|
123 |
-
st.error("Error uploading file")
|
124 |
-
|
125 |
-
user_prompt = st.chat_input("Ask me anything about the content of the document:")
|
126 |
-
|
127 |
-
if user_prompt:
|
128 |
-
st.session_state.messages.append({'role': 'user', "content": user_prompt})
|
129 |
-
with st.chat_message("user", avatar=icons["user"]):
|
130 |
-
st.write(user_prompt)
|
131 |
-
|
132 |
-
# Trigger assistant's response retrieval and update UI
|
133 |
-
with st.spinner("Thinking..."):
|
134 |
-
response = requests.post("http://localhost:8000/query", json={"question": user_prompt})
|
135 |
-
if response.status_code == 200:
|
136 |
-
assistant_response = response.json()["response"]
|
137 |
-
with st.chat_message("assistant", avatar=icons["assistant"]):
|
138 |
-
st.write(assistant_response)
|
139 |
-
st.session_state.messages.append({'role': 'assistant', "content": assistant_response})
|
140 |
-
else:
|
141 |
-
st.error("Error querying document")
|
142 |
-
|
143 |
-
def run_fastapi():
|
144 |
-
uvicorn.run(app, host="0.0.0.0", port=8000)
|
145 |
-
|
146 |
if __name__ == "__main__":
|
147 |
-
|
148 |
-
fastapi_thread = threading.Thread(target=run_fastapi)
|
149 |
-
fastapi_thread.start()
|
150 |
-
|
151 |
-
# Run Streamlit (this will run in the main thread)
|
152 |
-
streamlit_ui()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
import os
|
|
|
2 |
import shutil
|
3 |
import uvicorn
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
4 |
|
5 |
+
from pydantic import BaseModel
|
6 |
+
from fastapi import FastAPI, File, UploadFile, HTTPException
|
|
|
7 |
|
8 |
+
from app.app import data_ingestion, handle_query, DATA_DIR
|
|
|
|
|
9 |
|
10 |
class Query(BaseModel):
|
11 |
question: str
|
12 |
|
13 |
+
app = FastAPI()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
14 |
|
15 |
@app.post("/upload")
|
16 |
async def upload_file(file: UploadFile = File(...)):
|
|
|
33 |
response = handle_query(query.question)
|
34 |
return {"response": response}
|
35 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
36 |
if __name__ == "__main__":
|
37 |
+
uvicorn.run(app, host="0.0.0.0", port=8000)
|
|
|
|
|
|
|
|
|
|
requirements.txt
CHANGED
@@ -1,11 +1,11 @@
|
|
1 |
-
streamlit
|
2 |
-
python-dotenv
|
3 |
-
llama-index
|
4 |
-
llama-index-embeddings-huggingface
|
5 |
-
llama-index-llms-huggingface
|
6 |
-
gradio==3.50
|
7 |
-
docx2txt
|
8 |
-
tf-keras
|
9 |
-
fastapi
|
10 |
-
pydantic
|
11 |
-
uvicorn
|
|
|
1 |
+
streamlit
|
2 |
+
python-dotenv
|
3 |
+
llama-index
|
4 |
+
llama-index-embeddings-huggingface
|
5 |
+
llama-index-llms-huggingface
|
6 |
+
gradio==3.50
|
7 |
+
docx2txt
|
8 |
+
tf-keras
|
9 |
+
fastapi
|
10 |
+
pydantic
|
11 |
+
uvicorn
|