Update app.py

app.py

@@ -11,6 +11,8 @@ from sklearn.neighbors import NearestNeighbors
 def download_pdf(url, output_path):
     urllib.request.urlretrieve(url, output_path)
 
+PDF_URL = 'https://www.westlondon.nhs.uk/download_file/view/1459/615'
+OPENAI_API_KEY = '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'
 
 def preprocess(text):
     text = text.replace('\n', ' ')
@@ -89,74 +91,56 @@ class SemanticSearch:
         return embeddings
 
 
-
-def load_recommender(path, start_page=1):
+def load_recommender():
     global recommender
-    texts = pdf_to_text(path, start_page=start_page)
-    chunks = text_to_chunks(texts, start_page=start_page)
+    download_pdf(PDF_URL, 'corpus.pdf')
+    texts = pdf_to_text('corpus.pdf', start_page=1)
+    chunks = text_to_chunks(texts, 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(
-        engine=engine,
-        prompt=prompt,
-        max_tokens=512,
-        n=1,
-        stop=None,
-        temperature=0.7,
-    )
-    message = completions.choices[0].text
-    return message
-
-def generate_answer(question,openAI_key):
-    topn_chunks = recommender(question)
-    prompt = ""
-    prompt += 'search results:\n\n'
-    for c in topn_chunks:
-        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). "\
-              "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 "\
-              "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: "
-    
-    prompt += f"Query: {question}\nAnswer:"
-    answer = generate_text(openAI_key, prompt,"text-davinci-003")
-    return answer
-
-
-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
-        download_pdf(glob_url, 'corpus.pdf')
-        load_recommender('corpus.pdf')
-
-    else:
-        old_file_name = file.name
-        file_name = file.name
-        file_name = file_name[:-12] + file_name[-4:]
-        os.rename(old_file_name, file_name)
-        load_recommender(file_name)
-
-    if question.strip() == '':
-        return '[ERROR]: Question field is empty'
-
-    return generate_answer(question,openAI_key)
-
+    return '
+def generate_text(prompt, engine="text-davinci-003"):
+openai.api_key = OPENAI_API_KEY
+completions = openai.Completion.create(
+engine=engine,
+prompt=prompt,
+max_tokens=512,
+n=1,
+stop=None,
+temperature=0.7,
+)
+message = completions.choices[0].text
+return message
+
+def generate_answer(question):
+topn_chunks = recommender(question)
+prompt = ""
+prompt += 'search results:\n\n'
+for c in topn_chunks:
+prompt += c + '\n\n'
+
+swift
+
+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). "\
+          "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 "\
+          "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: "
+
+prompt += f"Query: {question}\nAnswer:"
+answer = generate_text(prompt, "text-davinci-003")
+return answer
+
+def question_answer(question):
+if OPENAI_API_KEY.strip()=='':
+return '[ERROR]: Please enter your OpenAI API Key. Get your key here : https://platform.openai.com/account/api-keys'
+load_recommender()
+if question.strip() == '':
+    return '[ERROR]: Question field is empty'
+
+return generate_answer(question)
 
 recommender = SemanticSearch()
 
@@ -165,24 +149,22 @@ description = """ PDF GPT allows you to chat with your PDF file using Universal
 
 with gr.Blocks() as demo:
 
-    gr.Markdown(f'<center><h1>{title}</h1></center>')
-    gr.Markdown(description)
+scss
+
+gr.Markdown(f'<center><h1>{title}</h1></center>')
+gr.Markdown(description)
+
+with gr.Row():
+    
+    with gr.Group():
+        openAI_key=gr.Textbox(label='OpenAI API key', default=OPENAI_API_KEY)
+        question = gr.Textbox(label='Enter your question here')
+        btn = gr.Button(value='Submit')
+        btn.style(full_width=True)
+
+    with gr.Group():
+        answer = gr.Textbox(label='The answer to your question is :')
+
+    btn.click(question_answer, inputs=[question], outputs=[answer])
 
-    with gr.Row():
-        
-        with gr.Group():
-            gr.Markdown(f'<p style="text-align:center">Get your Open AI API key <a href="https://platform.openai.com/account/api-keys">here</a></p>')
-            openAI_key=gr.Textbox(label='Enter your OpenAI API key here')
-            url = gr.Textbox(label='Enter PDF URL here')
-            gr.Markdown("<center><h4>OR<h4></center>")
-            file = gr.File(label='Upload your PDF/ Research Paper / Book here', file_types=['.pdf'])
-            question = gr.Textbox(label='Enter your question here')
-            btn = gr.Button(value='Submit')
-            btn.style(full_width=True)
-
-        with gr.Group():
-            answer = gr.Textbox(label='The answer to your question is :')
-
-        btn.click(question_answer, inputs=[url, file, question,openAI_key], outputs=[answer])
-#openai.api_key = os.getenv('Your_Key_Here') 
 demo.launch()