refactored the project

app.py

@@ -1,12 +1,11 @@
 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
+from semantic_search import SemanticSearch
+
+recommender = SemanticSearch()
 
 def download_pdf(url, output_path):
     urllib.request.urlretrieve(url, output_path)
@@ -57,43 +56,6 @@ def text_to_chunks(texts, word_length=150, start_page=1, file_number=1):
     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(paths, start_page=1):
     global recommender
     texts = []
@@ -139,20 +101,18 @@ def generate_text(openAI_key, prompt, model="gpt-3.5-turbo"):
     return message
 
   
-def generate_answer(question, openAI_key, model):
+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 [ Page Number] notation. "\
+              "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:"
-    answer = generate_text(openAI_key, prompt, model)
-    return answer
-
+    return prompt
 
 def question_answer(chat_history, url, files, question, openAI_key, model):    
     try:
@@ -184,10 +144,8 @@ def question_answer(chat_history, url, files, question, openAI_key, model):
                  
         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)
+        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:
@@ -195,110 +153,3 @@ def question_answer(chat_history, url, files, question, openAI_key, model):
 
 
 
-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)
-    # print("topn chunks == " + str(topn_chunks))
-    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 [PDF Number][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\n"
-    
-    prompt += f"Query: {question}\nAnswer:"
-    print("prompt == " + str(prompt))
-    # print("prompt == " + str(prompt))
-    answer = generate_text_text_davinci_003(openAI_key, prompt,"text-davinci-003")
-    return answer
-
-# pre-defined questions
-questions = [
-    "What did the study investigate?",
-    "Can you provide a summary of this paper?",
-    "what are the methodologies used in this study?",
-    "what are the data intervals used in this study? Give me the start dates and end dates?",
-    "what are the main limitations of this study?",
-    "what are the main shortcomings of this study?",
-    "what are the main findings of the study?",
-    "what are the main results of the study?",
-    "what are the main contributions of this study?",
-    "what is the conclusion of this paper?",
-    "what are the input features used in this study?",
-    "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."""
-
-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():
-            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 )')
-                gr.Markdown("<center><h4>OR<h4></center>")
-                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],
-                inputs=[question],
-                label="PRE-DEFINED QUESTIONS: Click on a question to auto-fill the input box, then press Enter!",
-            )
-            model = gr.Radio([
-                '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'
-            ], label='Select Model', default='gpt-3.5-turbo')
-            btn = gr.Button(value='Submit')
-
-            btn.style(full_width=True)
-
-        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, files, question, openAI_key, model],
-        outputs=[chatbot],
-    )
-
-demo.launch()
-
-

semantic_search.py

@@ -0,0 +1,39 @@
+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
+    
+    
+    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
+

ui.py

@@ -0,0 +1,70 @@
+import gradio as gr
+import app as app
+
+
+
+# pre-defined questions
+questions = [
+    "What did the study investigate?",
+    "Can you provide a summary of this paper?",
+    "what are the methodologies used in this study?",
+    "what are the data intervals used in this study? Give me the start dates and end dates?",
+    "what are the main limitations of this study?",
+    "what are the main shortcomings of this study?",
+    "what are the main findings of the study?",
+    "what are the main results of the study?",
+    "what are the main contributions of this study?",
+    "what is the conclusion of this paper?",
+    "what are the input features used in this study?",
+    "what is the dependent variable in this study?",
+]
+
+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."""
+
+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():
+            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 )')
+                gr.Markdown("<center><h4>OR<h4></center>")
+                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],
+                inputs=[question],
+                label="PRE-DEFINED QUESTIONS: Click on a question to auto-fill the input box, then press Enter!",
+            )
+            model = gr.Radio([
+                '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'
+            ], label='Select Model', default='gpt-3.5-turbo')
+            btn = gr.Button(value='Submit')
+
+            btn.style(full_width=True)
+
+        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(
+        app.question_answer,
+        inputs=[chatbot, url, files, question, openAI_key, model],
+        outputs=[chatbot],
+    )
+
+demo.launch()