Update app.py

app.py

@@ -1,6 +1,4 @@
-from ctransformers import AutoModelForCausalLM
-from flask import Flask, request, jsonify
-import os
+from fastapi import FastAPI, File, UploadFile, Form
 from langchain_community.document_loaders import PyMuPDFLoader
 from LoadLLM import Loadllm
 from langchain_community.embeddings import HuggingFaceEmbeddings
@@ -9,25 +7,21 @@ from langchain.chains import ConversationalRetrievalChain
 
 DB_FAISS_PATH = 'vectorstore/db_faiss'
 
-app = Flask(__name__)
+app = FastAPI()
 
-@app.route('/')
-def home():
+@app.get('/')
+async def home():
     return "API Server Running"
 
-
-@app.route('/PromptBuddy', methods=['GET', 'POST'])
-def PromptLLM():
-    pdf_file = request.files['file']
-    pdf_name = pdf_file.filename
-    user_prompt = request.form.get('query')
-    pdf_file.save(pdf_name)
-
+@app.post('/PromptBuddy')
+async def PromptLLM(file: UploadFile = File(...), query: str = Form(...)):
+    pdf_name = file.filename
+    with open(pdf_name, 'wb') as f:
+        f.write(file.file.read())
 
     loader = PyMuPDFLoader(file_path=pdf_name)
     data = loader.load()
 
-
     # Create embeddings using Sentence Transformers
     embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')
 
@@ -41,9 +35,9 @@ def PromptLLM():
     # Create a conversational chain
     chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=db.as_retriever())
 
-    result = chain({"question": user_prompt, "chat_history":''})
-    return result["answer"]
-
+    result = chain({"question": query, "chat_history": ''})
+    return {"answer": result["answer"]}
 
 if __name__ == '__main__':
-    app.run(debug=True)
+    import uvicorn
+    uvicorn.run(app, host="127.0.0.1", port=8000, log_level="info")