Refactor project

app.py

@@ -1,219 +1,5 @@
-import urllib.request 
-import fitz
-import re
-import numpy as np
-import tensorflow_hub as hub
-import openai
 import gradio as gr
-import os
-from sklearn.neighbors import NearestNeighbors
-
-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
-
-    if end_page is None:
-        end_page = total_pages
-
-    text_list = []
-
-    for i in range(start_page-1, end_page):
-        text = doc.load_page(i).get_text("text")
-        text = preprocess(text)
-        text_list.append(text)
-
-    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]
-                continue
-            chunk = ' '.join(chunk).strip()
-            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)
-        n_neighbors = min(n_neighbors, len(self.embeddings))
-        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)]
-            emb_batch = self.use(text_batch)
-            embeddings.append(emb_batch)
-        embeddings = np.vstack(embeddings)
-        return embeddings
-
-
-
-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"):
-    openai.api_key = openAI_key
-    temperature=0.7
-    max_tokens=256
-    top_p=1
-    frequency_penalty=0
-    presence_penalty=0
-
-    if model == "text-davinci-003":
-        completions = openai.Completion.create(
-            engine=model,
-            prompt=prompt,
-            max_tokens=max_tokens,
-            n=1,
-            stop=None,
-            temperature=temperature,
-        )
-        message = completions.choices[0].text
-    else:
-        message = openai.ChatCompletion.create(
-            model=model,
-            messages=[
-                {"role": "system", "content": "You are a helpful assistant."},
-                {"role": "assistant", "content": "Here is some initial assistant message."},
-                {"role": "user", "content": prompt}
-            ],
-            temperature=.3,
-            max_tokens=max_tokens,
-            top_p=top_p,
-            frequency_penalty=frequency_penalty,
-            presence_penalty=presence_penalty,
-        ).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, openAI_key, model):
-    try:
-        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 =='':
-            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')
-            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'
-        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)
-        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
+import functions as functions
 
 # pre-defined questions
 questions = [
@@ -231,9 +17,6 @@ questions = [
     "what is the dependent variable in this study?",
 ]
 
-
-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."""
 
@@ -248,9 +31,9 @@ with gr.Blocks(css="""#chatbot { font-size: 14px; min-height: 1200; }""") as dem
             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>')
             with gr.Accordion("API Key"):
                 openAI_key = gr.Textbox(label='Enter your OpenAI API key here', password=True)
-                url = gr.Textbox(label='Enter PDF URL here   (Example: https://arxiv.org/pdf/1706.03762.pdf )')
+                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'])
+                files = gr.File(label='Upload your PDF/ Research Paper / Book here', file_types=['.pdf'], file_count="multiple")
             question = gr.Textbox(label='Enter your question here')
             gr.Examples(
                 [[q] for q in questions],
@@ -274,14 +57,12 @@ with gr.Blocks(css="""#chatbot { font-size: 14px; min-height: 1200; }""") as dem
             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, openAI_key, model],
+        functions.question_answer,
+        inputs=[chatbot, url, files, question, openAI_key, model],
         outputs=[chatbot],
     )
 
 demo.launch()
-
-

functions.py

@@ -0,0 +1,154 @@
+import urllib.request 
+import fitz
+import re
+import openai
+import os
+from semantic_search import SemanticSearch
+
+recommender = SemanticSearch()
+
+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
+
+
+# converts pdf to text
+def pdf_to_text(path, start_page=1, end_page=None):
+    doc = fitz.open(path)
+    total_pages = doc.page_count
+
+    if end_page is None:
+        end_page = total_pages
+
+    text_list = []
+
+    for i in range(start_page-1, end_page):
+        text = doc.load_page(i).get_text("text")
+        text = preprocess(text)
+        text_list.append(text)
+
+    doc.close()
+    return text_list
+
+# converts a text into a list of chunks
+def text_to_chunks(texts, word_length=150, start_page=1, file_number=1):
+
+    filtered_texts = [''.join(char for char in text if ord(char) < 128) for text in texts]
+    text_toks = [t.split(' ') for t in filtered_texts]
+    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]
+                continue
+            chunk = ' '.join(chunk).strip()
+            chunk = f'[PDF no. {file_number}] [Page no. {idx+start_page}]' + ' ' + '"' + chunk + '"'
+            chunks.append(chunk)
+    return chunks
+
+
+# merges a list of pdfs into a list of chunks and fits the recommender
+def load_recommender(paths, start_page=1):
+    global recommender
+    chunks = []
+    for idx, path in enumerate(paths):
+        chunks += text_to_chunks(pdf_to_text(path, start_page=start_page), start_page=start_page, file_number=idx+1)
+    recommender.fit(chunks)
+    return 'Corpus Loaded.'
+
+
+# calls the OpenAI API to generate a response for the given query
+def generate_text(openAI_key, 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
+
+    if model == "text-davinci-003":
+        completions = openai.Completion.create(
+            engine=model,
+            prompt=prompt,
+            max_tokens=max_tokens,
+            n=1,
+            stop=None,
+            temperature=temperature,
+        )
+        message = completions.choices[0].text
+    else:
+        message = openai.ChatCompletion.create(
+            model=model,
+            messages=[
+                {"role": "system", "content": "You are a helpful assistant."},
+                {"role": "assistant", "content": "Here is some initial assistant message."},
+                {"role": "user", "content": prompt}
+            ],
+            temperature=.3,
+            max_tokens=max_tokens,
+            top_p=top_p,
+            frequency_penalty=frequency_penalty,
+            presence_penalty=presence_penalty,
+        ).choices[0].message['content']
+    return message
+
+  
+# constructs the prompt for the given query  
+def construct_prompt(question):
+    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 [PDF Number][Page Number] notation. "\
+              "Only answer what is asked. The answer should be short and concise. \n\nQuery: "
+    
+    prompt += f"{question}\nAnswer:"
+    return prompt
+
+# main function that is called when the user clicks the submit button, generates an answer for the query
+def question_answer(chat_history, url, files, question, openAI_key, model):    
+    try:
+        if files == None: 
+            files = []
+        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 files == []:
+            return '[ERROR]: Both URL and PDF is empty. Provide at least one.'
+        if url.strip() != '' and files is not []:
+            return '[ERROR]: Both URL and PDF is provided. Please provide only one (either URL or PDF).'
+        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')
+            load_recommender('corpus.pdf')
+        else:
+            print(files)
+            filenames = []
+            for file in files:
+                old_file_name = file.name
+                file_name = file.name
+                file_name = file_name[:-12] + file_name[-4:]
+                os.rename(old_file_name, file_name)
+                filenames.append(file_name)
+            load_recommender(filenames)
+			
+                 
+        if question.strip() == '':
+            return '[ERROR]: Question field is empty'
+        prompt = construct_prompt(question)
+        answer = generate_text(openAI_key, prompt, 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!'

semantic_search.py

@@ -0,0 +1,41 @@
+import numpy as np
+import tensorflow_hub as hub
+from sklearn.neighbors import NearestNeighbors
+
+
+
+class SemanticSearch:
+    def __init__(self):
+        self.use = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')
+        self.fitted = False
+    
+    # fits the recommender
+    def fit(self, data, batch=1000, n_neighbors=5):
+        self.data = data
+        self.embeddings = self.get_text_embedding(data, batch=batch)
+        n_neighbors = min(n_neighbors, len(self.embeddings))
+        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
+    
+    
+	# returns embeddings
+    def get_text_embedding(self, texts, batch=1000):
+        embeddings = []
+        for i in range(0, len(texts), batch):
+            text_batch = texts[i:(i+batch)]
+            emb_batch = self.use(text_batch)
+            embeddings.append(emb_batch)
+        embeddings = np.vstack(embeddings)
+        return embeddings
+