Update app.py

app.py

@@ -1,7 +1,7 @@
 import os
 from dotenv import load_dotenv
-import urllib.request 
-import fitz
+import urllib.request
+import fitz  # PyMuPDF
 import re
 import numpy as np
 import tensorflow_hub as hub
@@ -18,13 +18,11 @@ openAI_key = os.getenv('OPENAI_API_KEY')
 def download_pdf(url, output_path):
     urllib.request.urlretrieve(url, output_path)
 
-
 def preprocess(text):
     text = text.replace('\n', ' ')
     text = re.sub('\s+', ' ', text)
     return text
 
-
 def pdf_to_text(path, start_page=1, end_page=None):
     doc = fitz.open(path)
     total_pages = doc.page_count
@@ -34,7 +32,7 @@ def pdf_to_text(path, start_page=1, end_page=None):
 
     text_list = []
 
-    for i in range(start_page-1, end_page):
+    for i in range(start_page - 1, end_page):
         text = doc.load_page(i).get_text("text")
         text = preprocess(text)
         text_list.append(text)
@@ -42,32 +40,28 @@ def pdf_to_text(path, start_page=1, end_page=None):
     doc.close()
     return text_list
 
-
 def text_to_chunks(texts, word_length=150, start_page=1):
     text_toks = [t.split(' ') for t in texts]
-    page_nums = []
     chunks = []
-    
+
     for idx, words in enumerate(text_toks):
         for i in range(0, len(words), word_length):
             chunk = words[i:i+word_length]
-            if (i+word_length) > len(words) and (len(chunk) < word_length) and (
-                len(text_toks) != (idx+1)):
-                text_toks[idx+1] = chunk + text_toks[idx+1]
+            if (i + word_length) > len(words) and (len(chunk) < word_length) and (
+                len(text_toks) != (idx + 1)):
+                text_toks[idx + 1] = chunk + text_toks[idx + 1]
                 continue
             chunk = ' '.join(chunk).strip()
-            chunk = f'[Page no. {idx+start_page}]' + ' ' + '"' + chunk + '"'
+            chunk = f'[Page no. {idx + start_page}]' + ' ' + '"' + chunk + '"'
             chunks.append(chunk)
     return chunks
 
-
 class SemanticSearch:
-    
+
     def __init__(self):
         self.use = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')
         self.fitted = False
-    
-    
+
     def fit(self, data, batch=1000, n_neighbors=5):
         self.data = data
         self.embeddings = self.get_text_embedding(data, batch=batch)
@@ -75,43 +69,40 @@ class SemanticSearch:
         self.nn = NearestNeighbors(n_neighbors=n_neighbors)
         self.nn.fit(self.embeddings)
         self.fitted = True
-    
-    
+
     def __call__(self, text, return_data=True):
         inp_emb = self.use([text])
         neighbors = self.nn.kneighbors(inp_emb, return_distance=False)[0]
-        
+
         if return_data:
             return [self.data[i] for i in neighbors]
         else:
             return neighbors
-    
-    
+
     def get_text_embedding(self, texts, batch=1000):
         embeddings = []
         for i in range(0, len(texts), batch):
-            text_batch = texts[i:(i+batch)]
+            text_batch = texts[i:(i + batch)]
             emb_batch = self.use(text_batch)
             embeddings.append(emb_batch)
         embeddings = np.vstack(embeddings)
         return embeddings
 
-
+recommender = SemanticSearch()
 
 def load_recommender(path, start_page=1):
-    global recommender
     texts = pdf_to_text(path, start_page=start_page)
     chunks = text_to_chunks(texts, start_page=start_page)
     recommender.fit(chunks)
     return 'Corpus Loaded.'
 
-def generate_text(openAI_key, prompt, model="gpt-3.5-turbo"):
+def generate_text(prompt, model="gpt-3.5-turbo"):
     openai.api_key = openAI_key
-    temperature=0.7
-    max_tokens=256
-    top_p=1
-    frequency_penalty=0
-    presence_penalty=0
+    temperature = 0.7
+    max_tokens = 256
+    top_p = 1
+    frequency_penalty = 0
+    presence_penalty = 0
 
     if model == "text-davinci-003":
         completions = openai.Completion.create(
@@ -139,35 +130,16 @@ def generate_text(openAI_key, prompt, model="gpt-3.5-turbo"):
         ).choices[0].message['content']
     return message
 
-  
-def generate_answer(question, openAI_key, model):
-    topn_chunks = recommender(question)
-    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. "\
-              "Only answer what is asked. The answer should be short and concise. \n\nQuery: "
-    
-    prompt += f"{question}\nAnswer:"
-    answer = generate_text(openAI_key, prompt, model)
-    return answer
-
-
 def question_answer(chat_history, url, file, question, model):
     try:
-        if openAI_key.strip()=='':
+        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 is empty. Provide at least one.'
         if url.strip() != '' and file is not None:
             return '[ERROR]: Both URL and PDF is provided. Please provide only one (either URL or PDF).'
-        if model is None or model =='':
+        if model is None or model == '':
             return '[ERROR]: You have not selected any model. Please choose an LLM model.'
         if url.strip() != '':
-            glob_url = url
-            download_pdf(glob_url, 'corpus.pdf')
+            download_pdf(url, 'corpus.pdf')
             load_recommender('corpus.pdf')
         else:
             old_file_name = file.name
@@ -177,74 +149,30 @@ def question_answer(chat_history, url, file, question, model):
             load_recommender(file_name)
         if question.strip() == '':
             return '[ERROR]: Question field is empty'
-        if model == "text-davinci-003" or model == "gpt-4" or model == "gpt-4-32k":
-            answer = generate_answer_text_davinci_003(question, openAI_key)
-        else:
-            answer = generate_answer(question, openAI_key, model)
+        answer = generate_text(question, model)
         chat_history.append([question, answer])
         return chat_history
     except openai.error.InvalidRequestError as e:
         return f'[ERROR]: Either you do not have access to GPT4 or you have exhausted your quota!'
 
-
-
-def generate_text_text_davinci_003(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_text_davinci_003(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 '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. \n\nQuery: {question}\nAnswer: "
-    
-    prompt += f"Query: {question}\nAnswer:"
-    answer = generate_text_text_davinci_003(openAI_key, prompt,"text-davinci-003")
-    return answer
-
-recommender = SemanticSearch()
-
 title = 'PDF GPT Turbo'
 description = """ PDF GPT Turbo allows you to chat with your PDF files. It uses Google's Universal Sentence Encoder with Deep averaging network (DAN) to give hallucination free response by improving the embedding quality of OpenAI. It cites the page number in square brackets([Page No.]) and shows where the information is located, adding credibility to the responses."""
 
-# Modify the interface setup to remove the OpenAI key input
 with gr.Blocks(css="""#chatbot { font-size: 14px; min-height: 1200; }""") as demo:
-
     gr.Markdown(f'<center><h3>{title}</h3></center>')
     gr.Markdown(description)
 
     with gr.Row():
         with gr.Group():
-            # Remove the OpenAI key input setup from here
             url = gr.Textbox(label='Enter PDF URL here   (Example: https://arxiv.org/pdf/1706.03762.pdf )')
             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')
             model = gr.Radio([
-                'gpt-3.5-turbo', 
-                'gpt-3.5-turbo-16k', 
-                'gpt-3.5-turbo-0613', 
-                'gpt-3.5-turbo-16k-0613', 
+                'gpt-3.5-turbo',
+                'gpt-3.5-turbo-16k',
+                'gpt-3.5-turbo-0613',
+                'gpt-3.5-turbo-16k-0613',
                 'text-davinci-003',
                 'gpt-4',
                 'gpt-4-32k'
@@ -255,7 +183,6 @@ with gr.Blocks(css="""#chatbot { font-size: 14px; min-height: 1200; }""") as dem
         with gr.Group():
             chatbot = gr.Chatbot(placeholder="Chat History", label="Chat History", lines=50, elem_id="chatbot")
 
-    # Bind the click event of the button to the question_answer function
     btn.click(
         question_answer,
         inputs=[chatbot, url, file, question, model],
@@ -263,5 +190,3 @@ with gr.Blocks(css="""#chatbot { font-size: 14px; min-height: 1200; }""") as dem
     )
 
 demo.launch()
-
-