Update app.py

app.py

@@ -13,7 +13,8 @@ import gradio as gr
 title = '''
 <div style="text-align: left; font-family:Arial; color:Black; font-size: 16px; max-width: 750px;">
     <h1>Small PDF Summarizer</h1>
-    <p style="text-align: left;">How to Use:<br/>
+    <p style="text-align: left;">This App can be used to summarize small PDF (max. 1 MB, 15 pages)<br/>
+    How to Use:<br/>
     1. Upload a .PDF from your computer and fill OpenAI API key.<br/>
     2. Click the "Upload PDF" button, if successful a preview of your PDF text will be shown.<br/>
     3. Click "Summarize!" and the output will be shown on the textbox bellow.<br/>
@@ -58,36 +59,6 @@ model_list = {'gpt-3.5-turbo':'chat',
               'text-davinci-003':'instruct'}
 
 text_splitter = RecursiveCharacterTextSplitter(separators=["\n\n", "\n"], chunk_size=10000, chunk_overlap=250)
-
-# def parse_pdf(file_path):
-#     output = []
-#     print(file_path)
-#     pdf = PdfReader(file_path)
-    
-#     for page in pdf.pages:
-#         text = page.extract_text()
-#         output.append(text)
-
-#     return output, len(pdf.pages)
-
-# def preprocess_pdf_text(pdf_file): #(list_of_text):
-#   global page_num
-
-#   pdf_txt, page_num = parse_pdf(pdf_file.name)
-#   file_check(pdf_file.name)
-    
-#   page_docs = [Document(page_content=page) for page in pdf_txt]
-
-#   text_splitter = RecursiveCharacterTextSplitter(separators=["\n\n", "\n"], chunk_size=250, chunk_overlap=50)
-#   doc_sections = []
-#   for page in page_docs:
-#     sections_text = text_splitter.split_text(page.page_content)
-#     sections_doc = [Document(page_content=section) for section in sections_text]
-
-#     for section in sections_doc:
-#       doc_sections.append(section)
-
-    # return doc_sections
     
 def parse_pdf(pdf_file):
     global pdf_docs, page_count
@@ -110,7 +81,12 @@ def file_check(pdf_file):
 def summarize_pdf(api_key, 
                   model_name, temperature, llm_max_tokens, 
                   custom_map_prompt, custom_combine_prompt):
-
+  try:
+    if pdf_docs[0].page_content[:1]:
+         pass
+  except:
+    raise gr.Error("No PDF File Detected!")
+      
   # Build LLM Model
   os.environ["OPENAI_API_KEY"] = api_key
   if model_list[model_name] == 'chat':
@@ -185,11 +161,10 @@ def main():
         prompt_preview_button.click(generate_template, inputs=[user_map_prompt], outputs=[custom_map_view])
         prompt_preview_button.click(generate_template, inputs=[user_comb_prompt], outputs=[custom_comb_view])
 
-    list_inputs = [API_KEY, llm_model, temperature, llm_max_tokens, user_map_prompt, user_comb_prompt]
-    
-    # summarize_click = summarize_button.click(preprocess_pdf_text, inputs=[pdf_doc], outputs=[ingest_pdf]).\
-    #   then(summarize_pdf, inputs=list_inputs, outputs=[summarized_text])
+    inputs_list = [API_KEY, llm_model, temperature, llm_max_tokens, user_map_prompt, user_comb_prompt]
+
     submit_button.click(parse_pdf, inputs=[pdf_doc], outputs=[pdf_preview])
+    summarize_button.click(summarize_pdf, inputs=inputs_list, outputs=[summarized_text])
       
   demo.queue(concurrency_count=1).launch(share=True)