Delete api.py

api.py

@@ -1,76 +0,0 @@
-from fastapi import FastAPI
-from langchain.document_loaders import DirectoryLoader, PyPDFLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.vectorstores import FAISS
-from langchain_huggingface import HuggingFaceEndpoint
-from langchain.chains.combine_documents import create_stuff_documents_chain
-from langchain_core.prompts import ChatPromptTemplate
-from langchain.chains import create_retrieval_chain
-import os
-import uvicorn
-
-# Initialize FastAPI App 🚀
-app = FastAPI(title="Vision Transformer Assistant", description="A FastAPI-powered AI assistant for deep learning.")
-
-# Load Hugging Face Token 🔑
-HF_TOKEN = os.getenv("HF_TOKEN")  # Ensure your HF_TOKEN is set in Hugging Face Secrets
-
-# Load Documents 📂
-loader = DirectoryLoader("./data/", glob="*.pdf", loader_cls=PyPDFLoader)
-docs = loader.load()
-
-# Text Splitting 📖
-text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
-texts = text_splitter.split_documents(docs)
-
-# Vector Database 🔍
-db = FAISS.from_documents(documents=texts, embedding=HuggingFaceEmbeddings(model_name='BAAI/bge-base-en-v1.5'))
-retriever = db.as_retriever()
-
-# Load LLM 🚀
-repo_id = "mistralai/Mistral-7B-Instruct-v0.3"
-llm = HuggingFaceEndpoint(repo_id=repo_id, token=HF_TOKEN, task="text-generation")
-
-# Prompt Template ✨
-prompt_temp = ChatPromptTemplate.from_template("""
-You are an AI assistant specializing in deep learning, specifically Vision Transformers.
-
-<context>
-{context}
-<context>
-
-### Instructions:
-- Extract relevant information only from retrieved documents.
-- Provide concise yet detailed responses.
-- Use LaTeX for equations when necessary.
-- Do not make up answers; respond with *'Information not available in retrieved documents.'* if needed.
-""")
-
-# Create Retrieval Chain ⚡
-document_chain = create_stuff_documents_chain(llm, prompt_temp)
-retrieval_chain = create_retrieval_chain(retriever, document_chain)
-
-def get_response(query: str) -> str:
-    """
-    Get AI-generated response based on query.
-    """
-    response = retrieval_chain.invoke({'input': query})
-    return response['answer']
-
-@app.get("/")
-def home():
-    return {"message": "Vision Transformer Assistant API is running 🚀"}
-
-@app.get("/query")
-def get_answer(query: str):
-    """
-    API endpoint to retrieve AI-generated responses.
-    """
-    answer = get_response(query)
-    return {"answer": answer}
-
-# Run FastAPI on Hugging Face Spaces (public)
-if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=8080)
-