Update app.py

app.py

@@ -8,11 +8,10 @@ import gradio as gr
 import os
 from sklearn.neighbors import NearestNeighbors
 
+
 def download_pdf(url, output_path):
     urllib.request.urlretrieve(url, output_path)
 
-PDF_URL = 'https://www.westlondon.nhs.uk/download_file/view/1459/615'
-OPENAI_API_KEY = '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'
 
 def preprocess(text):
     text = text.replace('\n', ' ')
@@ -29,7 +28,7 @@ def pdf_to_text(path, start_page=1, end_page=None):
 
     text_list = []
 
-    for i in range(start_page-1, end_page):
+    for i in range(start_page - 1, end_page):
         text = doc.load_page(i).get_text("text")
         text = preprocess(text)
         text_list.append(text)
@@ -40,28 +39,25 @@ def pdf_to_text(path, start_page=1, end_page=None):
 
 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]
+            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 + '"'
+            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)
@@ -69,22 +65,20 @@ class SemanticSearch:
         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)]
+            text_batch = texts[i:(i + batch)]
             emb_batch = self.use(text_batch)
             embeddings.append(emb_batch)
         embeddings = np.vstack(embeddings)
@@ -99,6 +93,7 @@ def load_recommender():
     recommender.fit(chunks)
     return 'Corpus Loaded.'
 
+
 def generate_text(prompt, engine="text-davinci-003"):
     openai.api_key = OPENAI_API_KEY
     completions = openai.Completion.create(
@@ -109,63 +104,82 @@ def generate_text(prompt, engine="text-davinci-003"):
         stop=None,
         temperature=0.7,
     )
-    message = completions.choices[0].text
+    message = completions.choices[message = completions.choices[0].text
     return message
 
-def generate_answer(question):
-topn_chunks = recommender(question)
-prompt = ""
-prompt += 'search results:\n\n'
-for c in topn_chunks:
-prompt += c + '\n\n'
-
-swift
-
-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 'Text Not Found in PDF'. Ignore outlier "\
-          "search results which has 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: "
-
-prompt += f"Query: {question}\nAnswer:"
-answer = generate_text(prompt, "text-davinci-003")
-return answer
-
-def question_answer(question):
-if OPENAI_API_KEY.strip()=='':
-return '[ERROR]: Please enter your OpenAI API Key. Get your key here : https://platform.openai.com/account/api-keys'
-load_recommender()
-if question.strip() == '':
-    return '[ERROR]: Question field is empty'
-
-return generate_answer(question)
+def generate_answer(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 'Text Not Found in PDF'. Ignore outlier "\
+              "search results which has 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: "
+    
+    prompt += f"Query: {question}\nAnswer:"
+    answer = generate_text(openAI_key, prompt,"text-davinci-003")
+    return answer
 
-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."""
+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).'
 
-with gr.Blocks() as demo:
+    if url.strip() != '':
+        glob_url = url
+        download_pdf(glob_url, 'corpus.pdf')
+        load_recommender('corpus.pdf')
 
-scss
+    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)
 
-gr.Markdown(f'<center><h1>{title}</h1></center>')
-gr.Markdown(description)
+    if question.strip() == '':
+        return '[ERROR]: Question field is empty'
 
-with gr.Row():
-    
-    with gr.Group():
-        openAI_key=gr.Textbox(label='OpenAI API key', default=OPENAI_API_KEY)
-        question = gr.Textbox(label='Enter your question here')
-        btn = gr.Button(value='Submit')
-        btn.style(full_width=True)
+    return generate_answer(question,openAI_key)
 
-    with gr.Group():
-        answer = gr.Textbox(label='The answer to your question is :')
 
-    btn.click(question_answer, inputs=[question], outputs=[answer])
+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."""
 
-demo.launch()
+with gr.Blocks() as demo:
+
+    gr.Markdown(f'<center><h1>{title}</h1></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'])
+            question = gr.Textbox(label='Enter your question here')
+            btn = gr.Button(value='Submit')
+            btn.style(full_width=True)
+
+        with gr.Group():
+            answer = gr.Textbox(label='The answer to your question is :')
+
+        btn.click(question_answer, inputs=[url, file, question,openAI_key], outputs=[answer])
+#openai.api_key = os.getenv('Your_Key_Here') 
+demo.launch()"