Update app.py

app.py

@@ -7,10 +7,6 @@ import openai
 import gradio as gr
 import os
 from sklearn.neighbors import NearestNeighbors
-from jina import Document, DocumentArray
-
-# Create a new DocumentArray for file storage
-doc_array = DocumentArray()
 
 def download_pdf(url, output_path):
     urllib.request.urlretrieve(url, output_path)
@@ -21,17 +17,6 @@ def preprocess(text):
     text = re.sub('\s+', ' ', text)
     return text
 
-# Store a file in the DocumentArray
-def store_file_in_docarray(file_name, file_content):
-    doc = Document(id=file_name, content=file_content)
-    doc_array.append(doc)
-
-# Retrieve a file from the DocumentArray
-def get_file_from_docarray(file_name):
-    for doc in doc_array:
-        if doc.id == file_name:
-            return doc.content
-    return None
 
 def pdf_to_text(path, start_page=1, end_page=None):
     doc = fitz.open(path)
@@ -50,24 +35,13 @@ def pdf_to_text(path, start_page=1, end_page=None):
     doc.close()
     return text_list
 
-def text_to_chunks(texts, file_names, word_length=150, start_page=1):
+
+def text_to_chunks(texts, word_length=150, start_page=1):
     text_toks = [t.split(' ') for t in texts]
     page_nums = []
     chunks = []
     
-    total_pages = len(texts)
-
     for idx, words in enumerate(text_toks):
-        current_file_idx = 0
-        current_page = idx + start_page
-
-        for i, num_pages in enumerate(file_names.values()):
-            if current_page > num_pages:
-                current_page -= num_pages
-                current_file_idx += 1
-            else:
-                break
-
         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 (
@@ -75,18 +49,18 @@ def text_to_chunks(texts, file_names, word_length=150, start_page=1):
                 text_toks[idx+1] = chunk + text_toks[idx+1]
                 continue
             chunk = ' '.join(chunk).strip()
-            chunk = f'[{list(file_names.keys())[current_file_idx]}, Page no. {current_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)
@@ -95,6 +69,7 @@ class SemanticSearch:
         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]
@@ -104,6 +79,7 @@ class SemanticSearch:
         else:
             return neighbors
     
+    
     def get_text_embedding(self, texts, batch=1000):
         embeddings = []
         for i in range(0, len(texts), batch):
@@ -113,21 +89,17 @@ class SemanticSearch:
         embeddings = np.vstack(embeddings)
         return embeddings
 
-def load_recommender(paths, start_page=1):
-    global recommender
-    all_texts = []
-    file_names = {}
 
-    for path in paths:
-        texts = pdf_to_text(path, start_page=start_page)
-        all_texts.extend(texts)
-        file_names[os.path.basename(path)] = len(texts)
 
-    chunks = text_to_chunks(all_texts, file_names, start_page=start_page)
+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, engine="text-davinci-003"):
+
+def generate_text(openAI_key,prompt, engine="text-davinci-003"):
     openai.api_key = openAI_key
     completions = openai.Completion.create(
         engine=engine,
@@ -140,7 +112,8 @@ def generate_text(openAI_key, prompt, engine="text-davinci-003"):
     message = completions.choices[0].text
     return message
 
-def generate_answer(question, openAI_key):
+
+def generate_answer(question,openAI_key):
     topn_chunks = recommender(question)
     prompt = ""
     prompt += 'search results:\n\n'
@@ -148,61 +121,51 @@ def generate_answer(question, openAI_key):
         prompt += c + '\n\n'
         
     prompt += "Instructions: Compose a comprehensive reply to the query using the search results given. "\
-              "Cite each reference using [PDF FILE NAME, PAGE NUMBER:] notation (every result has this number at the beginning). "\
+              "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 'Text Not Found in PDF'. Ignore outlier "\
-              "search results which have nothing to do with the question. Only answer what is asked. The "\
-              "answer should be short and concise. Answer step-by-step. \n\nQuery: {question}\nAnswer: "   
+              "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(openAI_key, prompt, "text-davinci-003")
+    answer = generate_text(openAI_key, prompt,"text-davinci-003")
     return answer
 
-def question_answer(url, files, question, openAI_key):
-    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 not files:
-        return '[ERROR]: Both URL and PDF are empty. Provide at least one.'
-
-    if url.strip() != '' and files:
-        return '[ERROR]: Both URL and PDF are provided. Please provide only one (either URL or PDF).'
 
-    pdf_paths = []
+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
-        output_path = 'corpus.pdf'
-        download_pdf(glob_url, output_path)
-        pdf_paths.append(output_path)
+        download_pdf(glob_url, 'corpus.pdf')
+        load_recommender('corpus.pdf')
 
     else:
-        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)
-            pdf_paths.append(file_name)
+        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'
 
-    # Store the PDF content in the DocumentArray
-    for pdf_path in pdf_paths:
-        with open(pdf_path, "rb") as f:
-            content = f.read()
-            store_file_in_docarray(pdf_path, content)
+    return generate_answer(question,openAI_key)
 
-    # Load the recommender
-    load_recommender(pdf_paths)
-    
-    # Generate an answer
-    return generate_answer(question, openAI_key)
 
 recommender = SemanticSearch()
-title = 'PDF GPT (Sandbox)'
 
-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 and file name in square brackets([]) where the information is located, adding credibility to the responses and helping to locate pertinent information quickly."""
+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."""
+
 
 with gr.Blocks() as demo:
 
@@ -213,10 +176,10 @@ with gr.Blocks() as demo:
         
         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')
+            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>")
-            files = gr.File(label='Upload your PDF/ Research Paper / Book here', file_types=['.pdf'], file_count='multiple')
+            file = gr.File(label='Upload your PDF/ Research Paper / Book here', file_types=['.pdf'])
             question = gr.Textbox(label='Enter your question here')
             btn = gr.Button(value='Submit')
             btn.style(full_width=True)
@@ -224,7 +187,6 @@ with gr.Blocks() as demo:
         with gr.Group():
             answer = gr.Textbox(label='The answer to your question is :')
 
-        btn.click(question_answer, inputs=[url, files, question, openAI_key], outputs=[answer])
-
+        btn.click(question_answer, inputs=[url, file, question,openAI_key], outputs=[answer])
+#openai.api_key = os.getenv('Your_Key_Here') 
 demo.launch()
-