Update app.py

app.py

@@ -3,158 +3,6 @@ import os
 from pinecone_integration import PineconeIndex
 from qa_model import QAModel
 
-# !pip install transformers accelerate
-# !pip install -qU pinecone-client[grpc] sentence-transformers
-# !pip install gradio
-
-# class PineconeIndex:
-
-#     def __init__(self):
-#         device = 'cuda' if torch.cuda.is_available() else 'cpu'
-
-#         self.sm = SentenceTransformer('all-MiniLM-L6-v2', device=device)
-#         self.index_name = 'semantic-search-fast-med'
-#         self.index = None
-
-#     def init_pinecone(self):
-
-#         index_name = self.index_name
-#         sentence_model = self.sm
-
-#         # get api key from app.pinecone.io
-#         PINECONE_API_KEY = "b97d5759-dd39-428b-a1fd-ed30f3ba74ee"  # os.environ.get('PINECONE_API_KEY') or 'PINECONE_API_KEY'
-#         # find your environment next to the api key in pinecone console
-#         PINECONE_ENV = "us-west4-gcp"  # os.environ.get('PINECONE_ENV') or 'PINECONE_ENV'
-
-#         pinecone.init(
-#             api_key=PINECONE_API_KEY,
-#             environment=PINECONE_ENV
-#         )
-
-# #         pinecone.delete_index(index_name)
-
-#         # only create index if it doesn't exist
-#         if index_name not in pinecone.list_indexes():
-#             pinecone.create_index(
-#                 name=index_name,
-#                 dimension=sentence_model.get_sentence_embedding_dimension(),
-#                 metric='cosine'
-#             )
-
-#         # now connect to the index
-#         self.index = pinecone.GRPCIndex(index_name)
-#         return self.index
-
-#     def build_index(self):
-
-#         if self.index is None:
-#             index = self.init_pinecone()
-#         else:
-#             index = self.index
-
-#         if index.describe_index_stats()['total_vector_count']:
-#             "Index already built"
-#             return
-
-#         sentence_model = self.sm
-
-#         x = pd.read_excel('/kaggle/input/drug-p/Diseases_data_W.xlsx')
-
-#         question_dict = {'About': 'What is {}?', 'Symptoms': 'What are symptoms of {}?',
-#                          'Causes': 'What are causes of {}?',
-#                          'Diagnosis': 'What are diagnosis for {}?', 'Risk Factors': 'What are the risk factors for {}?',
-#                          'Treatment Options': 'What are the treatment options for {}?',
-#                          'Prognosis and Complications': 'What are the prognosis and complications?'}
-#         context = []
-#         disease_list = []
-
-#         for i in range(len(x)):
-#             disease = x.iloc[i, 0]
-#             if disease.strip().lower() in disease_list:
-#                 continue
-
-#             disease_list.append(disease.strip().lower())
-
-#             conditions = x.iloc[i, 1:].dropna().index
-#             answers = x.iloc[i, 1:].dropna()
-
-#             for cond in conditions:
-#                 context.append(f"{question_dict[cond].format(disease)}\n\n{answers[cond]}")
-
-#         batch_size = 128
-#         for i in tqdm(range(0, len(context), batch_size)):
-#             # find end of batch
-#             i_end = min(i + batch_size, len(context))
-#             # create IDs batch
-#             ids = [str(x) for x in range(i, i_end)]
-#             # create metadata batch
-#             metadatas = [{'text': text} for text in context[i:i_end]]
-#             # create embeddings
-#             xc = sentence_model.encode(context[i:i_end])
-#             # create records list for upsert
-#             records = zip(ids, xc, metadatas)
-#             # upsert to Pinecone
-#             index.upsert(vectors=records)
-
-#         # check number of records in the index
-#         index.describe_index_stats()
-
-#     def search(self, query: str = "medicines for fever"):
-
-#         sentence_model = self.sm
-
-#         if self.index is None:
-#             self.build_index()
-
-#         index = self.index
-
-#         # create the query vector
-#         xq = sentence_model.encode(query).tolist()
-
-#         # now query
-#         xc = index.query(xq, top_k = 3, include_metadata = True)
-        
-#         return xc
-
-
-# class QAModel():
-#     def __init__(self, checkpoint="google/flan-t5-xl"):
-#         self.checkpoint = checkpoint
-#         self.tmpdir = f"{self.checkpoint.split('/')[-1]}-sharded"
-
-#     def store_sharded_model(self):
-#         tmpdir = self.tmpdir
-        
-#         checkpoint = self.checkpoint
-        
-#         if not os.path.exists(tmpdir):
-#             os.mkdir(tmpdir)
-#             print(f"Directory created - {tmpdir}")
-#             model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)
-#             print(f"Model loaded - {checkpoint}")
-#             model.save_pretrained(tmpdir, max_shard_size="200MB")
-
-#     def load_sharded_model(self):
-#         tmpdir = self.tmpdir
-#         if not os.path.exists(tmpdir):
-#             self.store_sharded_model()
-            
-#         checkpoint = self.checkpoint
-        
-
-#         config = AutoConfig.from_pretrained(checkpoint)
-
-#         tokenizer = AutoTokenizer.from_pretrained(checkpoint)
-#         with init_empty_weights():
-#             model = AutoModelForSeq2SeqLM.from_config(config)
-#             # model = AutoModelForSeq2SeqLM.from_pretrained(tmpdir)
-
-#         model = load_checkpoint_and_dispatch(model, checkpoint=tmpdir, device_map="auto")
-#         return model, tokenizer
-
-#     def query_model(self, model, tokenizer, query):
-#         device = 'cuda' if torch.cuda.is_available() else 'cpu'
-#         return tokenizer.batch_decode(model.generate(**tokenizer(query, return_tensors='pt').to(device)), skip_special_tokens=True)[0]
 
 PI = PineconeIndex()
 PI.build_index()
@@ -192,7 +40,7 @@ demo = gr.Interface(
     ],
     cache_examples=True,
     title="MedQA assistant",
-    #description="MedQA assistant"
+    description='Check out the repository at: https://github.com/anandshah98/MedQA',
 )
 
 demo.launch()