Update app.py

app.py

@@ -53,6 +53,7 @@ def text_to_chunks(texts, word_length=150, start_page=1):
             chunks.append(chunk)
     return chunks
 
+
 class SemanticSearch:
     
     def __init__(self):
@@ -97,6 +98,7 @@ def load_recommender(path, start_page=1):
     recommender.fit(chunks)
     return 'Corpus Loaded.'
 
+
 def generate_text(openAI_key,prompt, engine="text-davinci-003"):
     openai.api_key = openAI_key
     completions = openai.Completion.create(
@@ -110,6 +112,7 @@ def generate_text(openAI_key,prompt, engine="text-davinci-003"):
     message = completions.choices[0].text
     return message
 
+
 def generate_answer(question,openAI_key):
     topn_chunks = recommender(question)
     prompt = ""
@@ -118,13 +121,13 @@ def generate_answer(question,openAI_key):
         prompt += c + '\n\n'
         
     prompt += "Instructions: Compose a comprehensive reply to the query using the search results given. "\
-              "Cite each reference using [ Page Number] notation (every result has this number at the beginning). "\
+              "Cite each reference using [number] notation (every result has this number at the beginning). "\
               "Citation should be done at the end of each sentence. If the search results mention multiple subjects "\
               "with the same name, create separate answers for each. Only include information found in the results and "\
               "don't add any additional information. Make sure the answer is correct and don't output false content. "\
-              "If the text does not relate to the query, simply state 'Text Not Found in PDF'. Ignore outlier "\
+              "If the text does not relate to the query, simply state 'Found Nothing'. Ignore outlier "\
               "search results which has nothing to do with the question. Only answer what is asked. The "\
-              "answer should be short and concise. Answer step-by-step. \n\nQuery: {question}\nAnswer: "
+              "answer should be short and concise.Answer step-by-step. \n\nQuery: {question}\nAnswer: "
     
     prompt += f"Query: {question}\nAnswer:"
     answer = generate_text(openAI_key, prompt,"text-davinci-003")
@@ -163,6 +166,7 @@ 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."""
 
+
 with gr.Blocks() as demo:
 
     gr.Markdown(f'<center><h1>{title}</h1></center>')
@@ -185,4 +189,5 @@ with gr.Blocks() as demo:
 
         btn.click(question_answer, inputs=[url, file, question,openAI_key], outputs=[answer])
 #openai.api_key = os.getenv('Your_Key_Here') 
-demo.launch()
+demo.launch()
+