Update app.py

app.py

@@ -1,6 +1,7 @@
 import os
 import re
 import logging
+import asyncio
 from fastapi import FastAPI, HTTPException
 from fastapi.responses import RedirectResponse
 from pydantic import BaseModel
@@ -9,21 +10,18 @@ from langchain.prompts import PromptTemplate
 from langchain_community.llms import CTransformers
 from langchain_community.vectorstores import FAISS
 from langchain_community.embeddings import HuggingFaceEmbeddings
-import subprocess
 from dotenv import load_dotenv
 
 # Load environment variables
 load_dotenv()
 
-# Set up logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-# FastAPI app
 app = FastAPI()
 
-# Load embeddings and vector database
 embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2", model_kwargs={"device": "cpu"})
+
 try:
     db = FAISS.load_local("vectorstore/db_faiss", embeddings, allow_dangerous_deserialization=True)
     logger.info("Vector database loaded successfully!")
@@ -31,7 +29,6 @@ except Exception as e:
     logger.error(f"Failed to load vector database: {e}")
     raise e
 
-# Load LLM using ctransformers
 try:
     llm = CTransformers(
         model="TheBloke/Llama-2-7B-Chat-GGML",
@@ -44,7 +41,6 @@ except Exception as e:
     logger.error(f"Failed to load LLM model: {e}")
     raise e
 
-# Define custom prompt template
 custom_prompt_template = """Use the following pieces of information to answer the user's question.
 If you don't know the answer, just say that you don't know, don't try to make up an answer.
 
@@ -56,7 +52,6 @@ Helpful answer:
 """
 qa_prompt = PromptTemplate(template=custom_prompt_template, input_variables=["context", "question"])
 
-# Set up RetrievalQA chain
 qa_chain = RetrievalQA.from_chain_type(
     llm=llm,
     chain_type="stuff",
@@ -72,23 +67,14 @@ class AnswerResponse(BaseModel):
     answer: str
 
 def clean_answer(answer):
-    # Remove unnecessary characters and symbols
     cleaned_answer = re.sub(r'[^\w\s.,-]', '', answer)
-    # Remove repetitive phrases by identifying repeated words or sequences
     cleaned_answer = re.sub(r'\b(\w+)( \1\b)+', r'\1', cleaned_answer)
-    # Remove any trailing or leading spaces
     cleaned_answer = cleaned_answer.strip()
-    # Replace multiple spaces with a single space
     cleaned_answer = re.sub(r'\s+', ' ', cleaned_answer)
-    # Replace \n with newline character in markdown
     cleaned_answer = re.sub(r'\\n', '\n', cleaned_answer)
-    # Check for bullet points and replace with markdown syntax
     cleaned_answer = re.sub(r'^\s*-\s+(.*)$', r'* \1', cleaned_answer, flags=re.MULTILINE)
-    # Check for numbered lists and replace with markdown syntax
     cleaned_answer = re.sub(r'^\s*\d+\.\s+(.*)$', r'1. \1', cleaned_answer, flags=re.MULTILINE)
-    # Check for headings and replace with markdown syntax
     cleaned_answer = re.sub(r'^\s*(#+)\s+(.*)$', r'\1 \2', cleaned_answer, flags=re.MULTILINE)
-
     return cleaned_answer
 
 def format_sources(sources):
@@ -107,7 +93,8 @@ async def query(question_request: QuestionRequest):
         if not question:
             raise HTTPException(status_code=400, detail="Question is required")
 
-        result = qa_chain({"query": question})
+        loop = asyncio.get_event_loop()
+        result = await loop.run_in_executor(None, qa_chain, {"query": question})
         answer = result.get("result")
         sources = result.get("source_documents")
 
@@ -117,19 +104,16 @@ async def query(question_request: QuestionRequest):
         else:
             answer += "\nNo sources found"
 
-        # Clean up the answer
         cleaned_answer = clean_answer(answer)
-
         return {"answer": cleaned_answer}
-
     except Exception as e:
         logger.error(f"Error processing query: {e}")
         raise HTTPException(status_code=500, detail="Internal Server Error")
 
+@app.on_event("startup")
+async def startup_event():
+    subprocess.Popen(["streamlit", "run", "frontend.py", "--server.port", "8501"])
 
 @app.get("/")
 async def root():
-    return RedirectResponse(url="/docs")
-
-#if __name__ == '__main__':
-    #uvicorn.run(app, host='0.0.0.0', port=7860)
+    return RedirectResponse(url="http://localhost:8501")