Create app.py

app.py

@@ -0,0 +1,44 @@
+import streamlit as st 
+from langchain.vectorstores import faiss
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.document_loaders import TextLoader
+from langchain.embeddings import SentenceTransformerEmbeddings
+from tempfile import NamedTemporaryFile
+import os
+import shutil
+
+try:
+    shutil.rmtree("tempDir")
+except :
+    pass
+try:
+    os.mkdir("tempDir")
+except:
+    pass
+def save_uploadedfile(uploadedfile):
+
+    with open(os.path.join("tempDir",uploadedfile.name),"wb") as f:
+         f.write(uploadedfile.getbuffer())
+    return st.success("Saved File:{} to tempDir".format(uploadedfile.name))
+
+
+def main():
+    st.set_page_config(page_title="chet with unipv")
+    st.text_input("fai una domanda al tuo professore ")
+    with st.sidebar:
+        st.subheader("Your_faiss_index")
+        documents=st.file_uploader("upload your faiss index here ",accept_multiple_files=True)
+        if st.button("Procedi"):
+            for document in documents:
+                save_uploadedfile(document)
+            #with st.spinner("sto processando i tuoi dati"):
+            print(documents)
+            query="chi è matteo salvini?"
+            embeddings= HuggingFaceInstructEmbeddings(model_name="thenlper/gte-base")
+            new_db = FAISS.load_local("tempDir", embeddings)
+            docs = new_db.similarity_search(query)
+            print(docs)
+if __name__=="__main__":
+    main()