Update app.py

app.py

@@ -5,29 +5,34 @@
 ##################### Imports #####################
 import gradio as gr
 from utilities.setup import get_files
-#from server import EmbeddingService, QAService
 
 from connections.pinecone import PineconeConnector
-from services.embed_service.embed import EmbeddingService
+#from connections.model import InferencePipeline
 
+from services.embed_service.embed import EmbeddingService
+#from services.qa_service.qna import QAService
 from server import QAService
 
 #import spaces
 
 #################### Functions ####################
+#@spaces.GPU
 def process_transcripts(files, context):
     
     print(files)
-    with EmbeddingService(conf, pinecone=pinecones) as e:
+    with EmbeddingService(conf, 
+                          pinecone=pinecones) as e:
         f = e.run(files)
     # some way to wait or a progress bar?
     return "Completed Loading Data"
 
 #@spaces.GPU
-def retrieve_answer(question, context):
+def retrieve_answer(question, goals):
     #with QAService(conf) as q:
-    #    q.run(question)
+    #    q.infer(question)
 
+    #with QAService(conf, pinecone=pinecones, model_pipeline=pipeline, question=question, goals=goals) as q:
+        #q.run()
 
     return question + context
 
@@ -66,8 +71,6 @@ def main(conf):
                              inputs=[question, goals],
                              outputs=model_output)
     
-    
-    
         demo.launch()
 
 
@@ -89,6 +92,14 @@ if __name__ == "__main__":
     pinecones = pc_connector.run()
 
     # initialize model connector
+    #pipeline = InferencePipeline()
+
+    #pipeline = InferencePipeline(conf,
+    #                             api_key=keys["huggingface"]
+    #                             )
+#
+#pipeline.infer(prompt)
 
+    
     # run main
     main(conf)