Update app.py

app.py

@@ -11,57 +11,6 @@ from tempfile import NamedTemporaryFile
 
 openAI_key = os.environ['OpenAPI']
 
-
-
-
-
-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'[{idx+start_page}]' + ' ' + '"' + chunk + '"'
-            chunks.append(chunk)
-    return chunks
-
-
-
 class SemanticSearch:
     
     def __init__(self):
@@ -98,57 +47,29 @@ class SemanticSearch:
         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.'
-
-# The modified function generates embeddings based on PDF file name and page number and checks if the embeddings file exists before loading or generating it.	
-
-def load_recommender(path, start_page=1):
+def load_recommender(paths, start_page=1):
     global recommender
-    pdf_file = os.path.basename(path)
-    embeddings_file = f"{pdf_file}_{start_page}.npy"
-    
-    if os.path.isfile(embeddings_file):
-        embeddings = np.load(embeddings_file)
-        recommender.embeddings = embeddings
-        recommender.fitted = True
-        return "Embeddings loaded from file"
+    chunks = []
+    for path in paths:
+        pdf_file = os.path.basename(path)
+        embeddings_file = f"{pdf_file}_{start_page}.npy"
+        
+        if os.path.isfile(embeddings_file):
+            embeddings = np.load(embeddings_file)
+            recommender.embeddings = embeddings
+            recommender.fitted = True
+            print("Embeddings loaded from file")
+            continue
+        
+        texts = pdf_to_text(path, start_page=start_page)
+        chunks.extend(text_to_chunks(texts, start_page=start_page))
     
-    texts = pdf_to_text(path, start_page=start_page)
-    chunks = text_to_chunks(texts, start_page=start_page)
     recommender.fit(chunks)
     np.save(embeddings_file, recommender.embeddings)
     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 process_file(file):
-    temp_file = NamedTemporaryFile(delete=False, suffix='.pdf')
-    file.save(temp_file.name)
-    temp_file.close()
-    return temp_file.name
-
-
-    
-def generate_text2(openAI_key, prompt, engine="text-davinci-003"):
+def generate_text(openAI_key, prompt, engine="gpt-3.5-turbo"):
     openai.api_key = openAI_key
     messages = [{'role': 'system', 'content': 'You are a helpful assistant.'},
                 {'role': 'user', 'content': prompt}]
@@ -164,7 +85,8 @@ def generate_text2(openAI_key, prompt, engine="text-davinci-003"):
     message = completions.choices[0].message['content']
     return message
 
-def generate_answer(question,openAI_key):
+
+def generate_answer(question, openAI_key):
     topn_chunks = recommender(question)
     prompt = ""
     prompt += 'search results:\n\n'
@@ -173,93 +95,74 @@ def generate_answer(question,openAI_key):
         
     prompt += "Instructions: Compose a comprehensive reply to the query using the search results given. "\
               "Make sure the answer is correct and don't output false content. "\
-              "answer should be short and concise. Answer step-by-step. \n\nQuery: {question}\nAnswer: "
+              "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")
+    answer = generate_text(openAI_key, prompt, "gpt-3.5-turbo")
     return answer
 
-def unique_filename(file_name):
-    counter = 1
-    new_file_name = file_name
-    while os.path.isfile(new_file_name):
-        name, ext = os.path.splitext(file_name)
-        new_file_name = f"{name}_{counter}{ext}"
-        counter += 1
-    return new_file_name
 
-
-def question_answer(url, file, question, openAI_key):
-  #openapi key here
-
-    if url.strip() == '' and file == None:
-        return '[ERROR]: Both URL and PDF is empty. Provide at least one.', False
-    
-    if url.strip() != '' and file != None:
-        return '[ERROR]: Both URL and PDF is provided. Please provide only one (either URL or PDF).', False
+def main_loop(url: str, files: list, question:
+str, openAI_key):
+    paths = []
 
     if url.strip() != '':
         glob_url = url
         download_pdf(glob_url, 'corpus.pdf')
-        load_recommender('corpus.pdf')
-    else:
-        old_file_name = file.name
-        file_name = old_file_name[:-12] + old_file_name[-4:]
-        file_name = unique_filename(file_name)  # Ensure the new file name is unique
+        paths.append('corpus.pdf')
+    if files is not None and len(files) > 0:
+        for file in files:
+            old_file_name = file.name
+            file_name = old_file_name[:-12] + old_file_name[-4:]
+            file_name = unique_filename(file_name)  # Ensure the new file name is unique
 
-        # Copy the content of the old file to the new file and delete the old file
-        with open(old_file_name, 'rb') as src, open(file_name, 'wb') as dst:
-            shutil.copyfileobj(src, dst)
-        os.remove(old_file_name)
+            # Copy the content of the old file to the new file and delete the old file
+            with open(old_file_name, 'rb') as src, open(file_name, 'wb') as dst:
+                shutil.copyfileobj(src, dst)
+            os.remove(old_file_name)
 
-        load_recommender(file_name)
+            paths.append(file_name)
+
+    load_recommender(paths)
 
     if question.strip().lower() == 'exit':
         return '', False
     
     answer = generate_answer(question, openAI_key)
     return answer, True  # Assuming the function returns an answer in all other cases
-   
-
-def main_loop(url: str, file: str, question: str):
-    answer, cont = question_answer(url, file, question, openAI_key)
-    return answer, cont
 
 
 def on_click(*args):
-    answer.value = main_loop(url.value, file.value, question.value)
+    answer.value = main_loop(url.value, files.value, question.value)
 
 
 recommender = SemanticSearch()
 
 title = 'Cognitive pdfGPT'
-description = """ Why use Cognitive pdfGPT?
-The issue is OpenAI has a 4K token constraint, preventing it from processing an entire PDF file as input. Additionally, ChatGPT cannot (as of yet) directly talk to external data. The solution is Cognitive pdfGPT, which allows you to chat with your PDF file using GPT functionalities. The application converts the document into smaller files and generates embeddings using a powerful Deep Averaging Network Encoder. A semantic search is performed on your data, and the top relevant results are used to generate a response. 🛑DO NOT USE CONFIDENTIAL INFORMATION """
-
-
-
+description = """ Why use Cognitive Ask an Expert?
+This is Cognitive Chat. Here you can upload multiple PDF files and query them as a single corpus of knowledge. 🛑DO NOT USE CONFIDENTIAL INFORMATION """
 
 
 with gr.Blocks() as demo:
-   
-   gr.Markdown(f'<center><h1>{title}</h1></center>')
-   gr.Markdown(description)
+    gr.Markdown(f'<center><h1>{title}</h1></center>')
+    gr.Markdown(description)
 
-   with gr.Row():
+    with gr.Row():
 
         with gr.Group():
-            file=gr.File(label='➡️ Upload your PDF ⬅️  NO CONFIDENTIAL FILES ', file_types=['.pdf'])
-            url=gr.Textbox(label=' ')
-            question=gr.Textbox(label='🔤 Enter your question here 🔤')
-            btn=gr.Button(value='Submit')
+            files = gr.Files(label='➡️ Upload your PDFs ⬅️  NO CONFIDENTIAL FILES ', file_types=['.pdf'])
+            url = gr.Textbox(label=' ')
+            question = gr.Textbox(label='🔤 Enter your question here 🔤')
+            btn = gr.Button(value='Submit')
             btn.style(full_width=False)
 
         with gr.Group():
             gr.Image("logo.jpg") 
             answer = gr.Textbox(label='The answer to your question is :')
             
-        btn.click(main_loop, inputs=[url, file, question], outputs=[answer])
+        btn.click(main_loop, inputs=[url, files, question], outputs=[answer])
 
      
 
 demo.launch()
+