Update app.py

app.py

@@ -1,9 +1,7 @@
 import os
 from threading import Thread
 from typing import Iterator
-from haystack import Document
-from haystack import Pipeline
-from haystack.components.embedders import SentenceTransformersTextEmbedder
+
 import gradio as gr
 import spaces
 import torch
@@ -29,39 +27,20 @@ if torch.cuda.is_available():
     
 
 
-key='4vNfIDO8PmFwCloxA40y2b4PSHm62vmcuPoM'
-url = "https://mmchpi0yssanukk5t3ofta.c0.europe-west3.gcp.weaviate.cloud"
+#key='4vNfIDO8PmFwCloxA40y2b4PSHm62vmcuPoM'
+#url = "https://mmchpi0yssanukk5t3ofta.c0.europe-west3.gcp.weaviate.cloud"
 # instanziamento client weaviate
-client = weaviate.Client(
-    url = url,  
-    auth_client_secret=weaviate.auth.AuthApiKey(api_key=key),
-    #embedded_options=weaviate.embedded.EmbeddedOptions(),
-)
+#client = weaviate.Client(
+#    url = url,  
+#    auth_client_secret=weaviate.auth.AuthApiKey(api_key=key),
+#    #embedded_options=weaviate.embedded.EmbeddedOptions(),
+#)
 
 
 
 
 
-def vettorizz_query(query):
-    vector_query=query_pipeline1.run({   "text_embedder": {"text": query},
-    })['text_embedder']['embedding']
-    return query, vector_query
 
-def richiamo_materiali(query, alpha, N_items):
-    text_query, vett_query = vettorizz_query(query)
-    try:
-        materiali = client.query.get("Default", ["content"]).with_hybrid(
-            query=text_query,
-            vector=vett_query,
-            alpha=alpha,
-            fusion_type=HybridFusion.RELATIVE_SCORE,
-        ).with_additional(["score"]).with_limit(N_items).do()
-        
-        mat = [{'score':i['_additional']['score'],'content':i['content']} for i in materiali['data']['Get']['Default']]
-    except:
-        mat =[{'score':0, 'content':'NESSUN MATERIALE FORNITO'}]
-    
-    return mat