moved files

app.py

@@ -1,15 +1,8 @@
 import gradio as gr
-<<<<<<< HEAD
 from langchain_community.document_loaders import PyPDFLoader
 from langchain_community.vectorstores import FAISS
 from langchain_community.embeddings import HuggingFaceEmbeddings
 from langchain_community.llms import HuggingFacePipeline
-=======
-from langchain.document_loaders import PyPDFLoader
-from langchain.vectorstores import FAISS
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.llms import HuggingFacePipeline
->>>>>>> 2d55fcd80accaa058042bb792107864492776fea
 from langchain.chains import RetrievalQA
 from transformers import pipeline
 import os

document/app.py

@@ -1,50 +0,0 @@
-import gradio as gr
-from langchain_community.document_loaders import PyPDFLoader
-from langchain_community.vectorstores import FAISS
-from langchain_community.embeddings import HuggingFaceEmbeddings
-from langchain_community.llms import HuggingFacePipeline
-from langchain.chains import RetrievalQA
-from transformers import pipeline
-import os
-
-# 1. Ladda och indexera alla PDF:er i mappen "dokument/"
-def load_vectorstore():
-    all_docs = []
-    for filename in os.listdir("document"):
-        if filename.endswith(".pdf"):
-            path = os.path.join("document", filename)
-            loader = PyPDFLoader(path)
-            docs = loader.load_and_split()
-            all_docs.extend(docs)
-    embedding = HuggingFaceEmbeddings(model_name="KBLab/sentence-bert-swedish-cased")
-    return FAISS.from_documents(all_docs, embedding)
-
-vectorstore = load_vectorstore()
-
-# 2. Initiera Zephyr-modellen
-def load_zephyr():
-    pipe = pipeline("text-generation", model="HuggingFaceH4/zephyr-7b-beta",
-                    model_kwargs={"temperature": 0.3, "max_new_tokens": 512})
-    return HuggingFacePipeline(pipeline=pipe)
-
-llm = load_zephyr()
-
-# 3. Bygg QA-kedjan
-qa_chain = RetrievalQA.from_chain_type(llm=llm, retriever=vectorstore.as_retriever())
-
-# 4. Funktion för Gradio-chat
-chat_history = []
-
-def chat_fn(message, history):
-    svar = qa_chain.run(message)
-    return svar
-
-# 5. Starta Gradio-gränssnittet
-chatbot = gr.ChatInterface(fn=chat_fn,
-                           title="🌟 Dokumentagent på Svenska",
-                           theme="soft",
-                           examples=["Vad handlar dokumentet om?", "Finns det något om diabetes?", "Vilken åtgärd föreslås?"],
-                           retry_btn="↻ Pröva igen",
-                           submit_btn="Ställ fråga")
-
-chatbot.launch()

document/requirements.txt

@@ -1,8 +0,0 @@
-huggingface_hub==0.25.2
-gradio
-langchain
-transformers
-sentence-transformers
-faiss-cpu
-pdfminer.six
-langchain-community