Adding status to document loading

app.py

@@ -12,7 +12,7 @@ DEVICE = 'cpu '
 FILE_EXT = ['pdf','text','csv','word','wav']
 
 
-def loading_pdf():
+def loading_file():
     return "Loading..."
 
 
@@ -36,9 +36,15 @@ def get_hugging_face_model(model_id,API_key,temperature=0.1):
                                       model_kwargs={"temperature": temperature, "max_new_tokens": 2048})
     return chat_llm
 
-def chat_api(file_data,doc_type='pdf',key=None,llm_model='HuggingFace'):
-    embedding_model = SentenceTransformerEmbeddings(model_name='all-mpnet-base-v2',model_kwargs={"device": DEVICE})
+def chat_application(llm_model,key):
+    if llm_model == 'HuggingFace':
+        llm = get_hugging_face_model(model_id='tiiuae/falcon-7b-instruct',API_key=key)
+    else:
+        llm_model = get_openai_chat_model(API_key=key)
+
 
+def document_loader(file_data,doc_type='pdf',key=None,llm_model='HuggingFace'):
+    embedding_model = SentenceTransformerEmbeddings(model_name='all-mpnet-base-v2',model_kwargs={"device": DEVICE})
     document = None
     if doc_type == 'pdf':
         document = process_pdf_document(document_file_name=file_data)
@@ -49,12 +55,16 @@ def chat_api(file_data,doc_type='pdf',key=None,llm_model='HuggingFace'):
     elif doc_type == 'word':
         document = process_word_document(document_file_name=file_data)
     
-    texts = process_documents(documents=document)
-    vectordb = FAISS.from_documents(documents=texts, embedding= embedding_model)
-    if llm_model == 'HuggingFace':
-        llm = get_hugging_face_model(model_id='tiiuae/falcon-7b-instruct',API_key=key)
+    if document:
+        texts = process_documents(documents=document)
+        global vectordb
+        vectordb = FAISS.from_documents(documents=texts, embedding= embedding_model)
     else:
-        llm_model = get_openai_chat_model(API_key=key)
+        return "Error in loading Documents "
+    
+    return "Document loaded - Embeddings ready "
+
+    
 
 
 
@@ -106,19 +116,20 @@ with gr.Blocks(css=css) as demo:
         
         with gr.Column():
             with gr.Box():
-                LLM_option = gr.Dropdown(['HuggingFace','OpenAI'],label='LLM',info='select the LLM to be used')
-                API_key = gr.Textbox(label="You OpenAI/Huggingface API key", type="password")
+                LLM_option = gr.Dropdown(['HuggingFace','OpenAI'],label='Large Language Model Selection',info='LLM Service')
+                API_key = gr.Textbox(label="Add {} API key".format(LLM_option), type="password")
             with gr.Column():
-                file_extension = gr.Dropdown(FILE_EXT, label="File Extensions", info="Select your files extensions!")
-                pdf_doc = gr.File(label="Load a File", file_types=FILE_EXT, type="file")
+                with gr.row():
+                    file_extension = gr.Dropdown(FILE_EXT, label="File Extensions", info="Select your files extensions!")
+                    pdf_doc = gr.File(label="Upload File to start QA", file_types=FILE_EXT, type="file")
                 with gr.Row():
-                    langchain_status = gr.Textbox(label="Status", placeholder="", interactive=False)
                     load_pdf = gr.Button("Load file to langchain")
+                    langchain_status = gr.Textbox(label="Status", placeholder="", interactive=True)
         
         chatbot = gr.Chatbot()
         question = gr.Textbox(label="Question", placeholder="Type your question and hit Enter")
         submit_button = gr.Button("Send Message")
-    load_pdf.click(loading_pdf, None, langchain_status, queue=False)    
+    load_pdf.click(loading_file, None, langchain_status, queue=False)    
     load_pdf.click(chat_api, inputs=[pdf_doc,file_extension,API_key,LLM_option], outputs=[langchain_status], queue=False)
     # question.submit(add_text, [chatbot, question], [chatbot, question]).then(
     #     bot, chatbot, chatbot