Update app.py

app.py

@@ -200,46 +200,7 @@ if __name__ == "__main__":
     print("Launching Gradio Interface for Basic Agent Evaluation...")
     demo.launch(debug=True, share=False)
 '''
-import json
-from langchain_chroma import Chroma
-from langchain_huggingface import HuggingFaceEmbeddings
-from langchain.tools.retriever import create_retriever_tool
-
-import chromadb
-chromadb.config.Settings.telemetry_enabled = False
-
-
-if __name__=='__main__':
-    with open('metadata.jsonl', 'r') as jsonl_file:
-        json_list = list(jsonl_file)
-
-    json_QA = []
-    for json_str in json_list:
-        json_data = json.loads(json_str)
-        json_QA.append(json_data)
-
-    # Usa gli stessi embeddings
-    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
-    print(1)
-
-    # Inizializza Chroma
-
-    from langchain.schema import Document
-    from langchain_community.vectorstores import Chroma
-
-    # Prepara la lista di documenti
-    docs = []
-    print("orig:",len(json_QA))
-    for sample in json_QA:
-        print(len(docs))
-        content = f"Question : {sample['Question']}\n\nFinal answer : {sample['Final answer']}"
-        metadata = {"source": sample['task_id']}
-        doc = Document(page_content=content, metadata=metadata)
-        docs.append(doc)
-
-    # Inizializza il vector store Chroma
-    vector_store = Chroma.from_documents(
-        documents=docs,
-        embedding=embeddings,
-        persist_directory="./chroma_db" 
-    )
+import os
+
+# Crea la cartella vuota se non esiste
+os.makedirs("chroma_db", exist_ok=True)