Fix dependencies

.gitignore

@@ -0,0 +1,5 @@
+.cache.sqlite
+.env
+*__pycache__/
+.vscode/
+.venv/

.gradio/certificate.pem

@@ -0,0 +1,31 @@
+-----BEGIN CERTIFICATE-----
+MIIFazCCA1OgAwIBAgIRAIIQz7DSQONZRGPgu2OCiwAwDQYJKoZIhvcNAQELBQAw
+TzELMAkGA1UEBhMCVVMxKTAnBgNVBAoTIEludGVybmV0IFNlY3VyaXR5IFJlc2Vh
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+emyPxgcYxn/eR44/KJ4EBs+lVDR3veyJm+kXQ99b21/+jh5Xos1AnX5iItreGCc=
+-----END CERTIFICATE-----

README.md

@@ -47,16 +47,14 @@ python --version
 
 ### 2. Installer les dépendances
 
-Installez `Gradio` :
+Installez les dependances :
 
 ```sh
-pip install gradio 
-```
+python -m venv .venv
 
-Installez `Chardet` :
+source .venv/bin/activate
 
-```sh
-pip install chardet 
+pip install -r requirements.txt
 ```
 ### 5. Gener un Access token sur Hugging Face
 
@@ -110,15 +108,16 @@ python --version
 
 ### 2. Install dependencies
 
-Install `Gradio` :
-```sh
-pip install gradio 
-```
+Install :
 
-Install `Chardet`
 ```sh
-pip install chardet 
+python -m venv .venv
+
+source .venv/bin/activate
+
+pip install -r requirements.txt
 ```
+
 ### 5. Generate Access Token on Hugging Face
 
 Go to your Hugging Face profile to generate your access token: [https://huggingface.co/settings/tokens](https://huggingface.co/settings/tokens)

app.py

@@ -3,14 +3,14 @@ import sys
 from dotenv import load_dotenv
 import gradio as gr
 from huggingface_hub import InferenceClient
-from langchain_community.embeddings import HuggingFaceBgeEmbeddings
+from langchain_huggingface import HuggingFaceEmbeddings
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_community.vectorstores import FAISS
 from langchain_community.document_loaders import PyPDFLoader
 from langchain.schema import Document
 
 load_dotenv()
-hftoken = os.environ.get("HF_TOKEN")
+hftoken = os.environ.get("HFTOKEN")
 
 
 from huggingface_hub import login
@@ -18,7 +18,15 @@ login(token=hftoken)
 
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta", token=hftoken)
 vector_store = None
-embeddings = HuggingFaceBgeEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+# model_kwargs = {'device': 'cpu'}
+# encode_kwargs = {'normalize_embeddings': False}
+model_name="sentence-transformers/all-MiniLM-L6-v2"
+
+embeddings = HuggingFaceEmbeddings(
+    model_name=model_name
+    # model_kwargs=model_kwargs,
+    # encode_kwargs=encode_kwargs
+)
 
 
 def extract_text_from_file(file_path):

requirements.txt

@@ -1,11 +1,15 @@
 chardet
-huggingface_hub==0.25.2
+faiss-cpu
+faiss-gpu
+huggingface_hub
 python-dotenv
-PyMuPDF==1.21.0
-gradio==3.0.0
-langchain==0.3.18
-langchain-community==0.0.17
-pydantic>=2.7.4
+pypdf
+PyMuPDF
+gradio
+langchain
+langchain-community
+langchain-huggingface
+pydantic