Update app.py

app.py

@@ -1,5 +1,5 @@
-import urllib.request
-import fitz
+import urllib.request 
+import fitz 
 import re
 import numpy as np
 import tensorflow_hub as hub
@@ -98,8 +98,90 @@ def load_recommender(path, start_page=1):
     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(openAI_key,prompt, engine="text-davinci-003"):
+
+def generate_text_text_davinci_003(openAI_key,prompt, engine="text-davinci-003"):
     openai.api_key = openAI_key
     completions = openai.Completion.create(
         engine=engine,
@@ -113,7 +195,7 @@ def generate_text(openAI_key,prompt, engine="text-davinci-003"):
     return message
 
 
-def generate_answer(question,openAI_key):
+def generate_answer_text_davinci_003(question,openAI_key):
     topn_chunks = recommender(question)
     prompt = ""
     prompt += 'search results:\n\n'
@@ -127,66 +209,79 @@ def generate_answer(question,openAI_key):
               "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: "
+              "answer should be short and concise. \n\nQuery: {question}\nAnswer: "
     
     prompt += f"Query: {question}\nAnswer:"
-    answer = generate_text(openAI_key, prompt,"text-davinci-003")
+    answer = generate_text_text_davinci_003(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)
+# 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'
-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."""
+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:
 
-with gr.Blocks() as demo:
-
-    gr.Markdown(f'<center><h1>{title}</h1></center>')
+    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>')
-            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'])
+            with gr.Accordion("API Key"):
+                openAI_key = gr.Textbox(label='Enter your OpenAI API key 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')
+            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)
-#openai.api_key = os.getenv('Your_Key_Here') 
+
         with gr.Group():
-            answer = gr.Textbox(label='The answer to your question is :')
+            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],
+        outputs=[chatbot],
+    )
+
+demo.launch()
 
-        btn.click(question_answer, inputs=[url, file, question,openAI_key], outputs=[answer])
 
-demo.launch()