adding summarization

app.py

@@ -44,6 +44,9 @@ def chat_application(llm_service,key):
         llm = get_openai_chat_model(API_key=key)
     return llm 
 
+def summarize_contents():
+    question = "Generate a summary of the contents. Do not return the response in json format"
+    return qa.run(question)
 
 def document_loader(file_path,api_key,doc_type='pdf',llm='Huggingface'):
     embedding_model = SentenceTransformerEmbeddings(model_name='all-mpnet-base-v2',model_kwargs={"device": DEVICE})
@@ -126,15 +129,20 @@ with gr.Blocks(css=css) as demo:
                     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=True)
-                        load_pdf = gr.Button("Upload file and generate Embeddings",).style(full_width=False)
-        
+                        load_pdf = gr.Button("Upload File & Generate Embeddings",).style(full_width=False)
+            with gr.Row():
+                summary = gr.Textbox(label="Summary")
+                summarize_pdf = gr.Button("Summarize the Contents").style(full_width=False)
+
         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_file, None, langchain_status, queue=False)    
     load_pdf.click(document_loader, inputs=[pdf_doc,API_key,file_extension,LLM_option], outputs=[langchain_status], queue=False)
     
+    summarize_pdf.click(summarize_contents,outputs=summary)
+    
     # question.submit(add_text, [chatbot, question], [chatbot, question]).then(
     #     bot, chatbot, chatbot
     # )