Update app.py

app.py

@@ -7,10 +7,6 @@ import openai
 import gradio as gr
 import os
 from sklearn.neighbors import NearestNeighbors
-#import tensorflow as tf
-import tensorflow.compat.v1 as tf
-tf.disable_v2_behavior()
-
 
 def download_pdf(url, output_path):
     urllib.request.urlretrieve(url, output_path)
@@ -136,15 +132,14 @@ def generate_answer(question,openAI_key):
     return answer
 
 
-def question_answer(url, file, question, openAI_key, history=[]):
-    if openAI_key.strip() == '':
-        return '[ERROR]: Please enter your Open AI Key. Get your key here: https://platform.openai.com/account/api-keys'
-
-    if url.strip() == '' and file is None:
-        return '[ERROR]: Both URL and PDF are empty. Provide at least one.'
-
-    if url.strip() != '' and file is not None:
-        return '[ERROR]: Both URL and PDF are provided. Please provide only one (either URL or PDF).'
+def question_answer(url, file, question,openAI_key):
+    if openAI_key.strip()=='':
+        return '[ERROR]: Please enter you Open AI Key. Get your key here : https://platform.openai.com/account/api-keys'
+    if url.strip() == '' and file == None:
+        return '[ERROR]: Both URL and PDF is empty. Provide atleast one.'
+    
+    if url.strip() != '' and file != None:
+        return '[ERROR]: Both URL and PDF is provided. Please provide only one (eiter URL or PDF).'
 
     if url.strip() != '':
         glob_url = url
@@ -161,17 +156,13 @@ def question_answer(url, file, question, openAI_key, history=[]):
     if question.strip() == '':
         return '[ERROR]: Question field is empty'
 
-    answer = generate_answer(question, openAI_key)
-    history.append({'question': question, 'answer': answer})
-    return answer
-
+    return generate_answer(question,openAI_key)
 
 
 recommender = SemanticSearch()
 
 title = 'PDF GPT'
 description = """ PDF GPT allows you to chat with your PDF file using Universal Sentence Encoder and Open AI. It gives hallucination free response than other tools as the embeddings are better than OpenAI. The returned response can even cite the page number in square brackets([]) where the information is located, adding credibility to the responses and helping to locate pertinent information quickly."""
-history = []
 
 with gr.Blocks() as demo:
 
@@ -192,12 +183,7 @@ with gr.Blocks() as demo:
 
         with gr.Group():
             answer = gr.Textbox(label='The answer to your question is :')
-            #history_box = gr.Textbox(label='History of Questions and Answers', value='', type='output', lines=10)
-            #history_box = gr.Textbox(label='History of Questions and Answers', value='', output=True, lines=10)
-            #history_box = gr.Textbox(label='History of Questions and Answers', value='', lines=10)
-            history_box = gr.Textbox(label='History of Questions and Answers', value='', type='text', lines=10)
-        #btn.click(question_answer, inputs=[url, file, question,openAI_key], outputs=[answer])
-        btn.click(question_answer, inputs=[url, file, question,openAI_key, history], outputs=[answer, history_box])
+
+        btn.click(question_answer, inputs=[url, file, question,openAI_key], outputs=[answer])
 #openai.api_key = os.getenv('Your_Key_Here') 
 demo.launch()
-