Add application file

app.py

@@ -27,7 +27,7 @@ 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)
@@ -40,13 +40,14 @@ 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 + '"'
@@ -55,12 +56,12 @@ def text_to_chunks(texts, word_length=150, start_page=1):
 
 
 class SemanticSearch:
-    
+
     def __init__(self):
-        self.use = hub.load("https://tfhub.dev/google/universal-sentence-encoder/4")
+        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)
@@ -68,29 +69,26 @@ 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)
         return embeddings
 
 
-
 def load_recommender(path, start_page=1):
     global recommender
     texts = pdf_to_text(path, start_page=start_page)
@@ -100,37 +98,38 @@ def load_recommender(path, start_page=1):
 
 
 ####################
-def generate_text(openAI_key,prompt,engine="chatgpt"):
+def generate_text(openAI_key,
+                  openAI_base,
+                  openAI_API_version,
+                  prompt,
+                  engine="chatgpt"):
     openai.api_type = "azure"
-    openai.api_base = "https://api.hku.hk"
-    openai.api_version = "2023-03-15-preview"
+    openai.api_base = openAI_base
+    openai.api_version = openAI_API_version
     openai.api_key = openAI_key
-    completions = openai.ChatCompletion.create(
-        engine="chatgpt",
-        max_tokens=1024,
-        n=1,
-        stop=None,
-        temperature=0.7,
-        messages=[
-            {"role": "user", "content": prompt}
-        ]
-    )
+    completions = openai.ChatCompletion.create(engine="chatgpt",
+                                               max_tokens=1024,
+                                               n=1,
+                                               stop=None,
+                                               temperature=1.0,
+                                               messages=[{
+                                                   "role": "user",
+                                                   "content": prompt
+                                               }])
     print(completions)
     message = completions['choices'][0]['message']['content']
     return message
 
 
-
-
-#####################
-
-def generate_answer(question,openAI_key):
+def generate_answer(question, openAI_key, openAI_base, openAI_API_version):
     topn_chunks = recommender(question)
+    print(len(topn_chunks))
+    print(*topn_chunks, sep = "\n")
     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 "\
@@ -139,21 +138,20 @@ def generate_answer(question,openAI_key):
               "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,"chatgpt")
+    answer = generate_text(openAI_key, openAI_base, openAI_API_version, prompt,
+                           "chatgpt")
     return answer
 
 
-
-
-
-def question_answer(url, file, question,openAI_key):
-    if openAI_key.strip()=='':
+def question_answer(url, file, question, openAI_key, openAI_base,
+                    openAI_API_version):
+    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).'
 
@@ -172,13 +170,17 @@ def question_answer(url, file, question,openAI_key):
     if question.strip() == '':
         return '[ERROR]: Question field is empty'
 
-    return generate_answer(question,openAI_key)
-
-    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."""
+    return generate_answer(question, openAI_key, openAI_base,
+                           openAI_API_version)
+recommender = SemanticSearch()
 
+title = 'PDF GPT Azure'
+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:
 
@@ -186,21 +188,40 @@ with gr.Blocks() as demo:
     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')
+            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>'
+            )
+            gr.Dropdown(label="API Type",
+                        choices=["azure", "OpenAI"],
+                        info="Azure or Open AI",
+                        value="azure"),
+            #####################
+            ##
+            ## REMEMBER to remove the key before public deploy
+            ##
+            #####################
+            openAI_key = gr.Textbox(label='Enter your Azure OpenAI API key here')
+            openAI_base = gr.Textbox(label='api_base',
+                                     value="https://api.hku.hk")
+            openAI_API_version = gr.Textbox(label='API version',
+                                            value="2023-03-15-preview")
             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)
-
+            file = gr.File(label='Upload your PDF/ Research Paper / Book here',
+                           file_types=['.pdf'])
+            
         with gr.Group():
+            question = gr.Textbox(label='Enter your question here')
+            btn = gr.Button(value='Submit', scale=1)
             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()
-
+        btn.click(question_answer,
+                  inputs=[
+                      url, file, question, openAI_key, openAI_base,
+                      openAI_API_version
+                  ],
+                  outputs=[answer])
+#openai.api_key = os.getenv('Your_Key_Here')
+demo.launch()