Update app.py

app.py

@@ -11,18 +11,26 @@ from langchain.vectorstores import Chroma
 from langchain.chains import RetrievalQA
 import gradio as gr
 
-HUGGINGFACEHUB_API_TOKEN = os.environ["HUGGINGFACEHUB_API_TOKEN"]
-
-embeddings = HuggingFaceEmbeddings(
+def define_embeddings_llm(openai_key):
+  if openai_key != "":
+    embeddings = OpenAIEmbeddings(openai_api_key=openai_key)
+    llm = OpenAI(
+        temperature=0, model_name="gpt-3.5-turbo-16k", openai_api_key=openai_key, verbose=False
+    )
+  else:
+    HUGGINGFACEHUB_API_TOKEN = os.environ["HUGGINGFACEHUB_API_TOKEN"]
+    embeddings = HuggingFaceEmbeddings(
                 model_name="sentence-transformers/all-MiniLM-L6-v2"
             )
-model="MBZUAI/LaMini-Flan-T5-248M"
-llm = HuggingFaceHub(repo_id=model ,
-                     model_kwargs={"temperature": 0}
-                    )
-langchain.llm_cache = InMemoryCache()
+    llm = HuggingFaceHub(repo_id="MBZUAI/LaMini-Flan-T5-248M",
+                         model_kwargs={"max_length":2048,
+                                       "temperature":0.2}
+                        )
+  langchain.llm_cache = InMemoryCache()
+  return embeddings,llm
 
-def build_context(files,urls):
+def build_context(openai_key,files,urls):
+  embeddings, llm = define_embeddings_llm(openai_key)
   documents = []
   if files is not None:
     for idx, file in enumerate(files):
@@ -72,6 +80,8 @@ def clear_chromadb():
     vectordb._collection.delete(ids=id)
 
 with gr.Blocks() as demo:
+  with gr.Row():
+    openai_key = gr.Textbox(label="Enter your OpenAI API Key if you want to use the gpt-3.5-turbo-16k model. If not, the open source LaMini-Flan-T5-248M is used")
   with gr.Row():
     with gr.Column():
       pdf_docs = gr.Files(label="Load pdf/docx/ppt/pptx files", file_types=['.pdf','.docx','.ppt','.pptx'], type="file")
@@ -88,7 +98,7 @@ with gr.Blocks() as demo:
       clear = gr.ClearButton([msg, hg_chatbot])
       cleardb = gr.Button(value="Réinitialiser le contexte")
   load_context.click(loading, None, loading_status, queue=False)
-  load_context.click(build_context, inputs=[pdf_docs, urls], outputs=[loading_status], queue=False)
+  load_context.click(build_context, inputs=[openai_key,pdf_docs, urls], outputs=[loading_status], queue=False)
   msg.submit(respond, [msg, hg_chatbot], [msg, hg_chatbot])
   cleardb.click(clear_chromadb)