Update app.py

app.py

@@ -9,6 +9,7 @@ import nltk
 # Download the required NLTK data
 nltk.download('punkt')
 nltk.download('punkt_tab')
+
 # Paths to your files
 faiss_path = "manual_chunked_faiss_index_500.bin"
 manual_path = "ubuntu_manual.txt"
@@ -48,7 +49,11 @@ except Exception as e:
     raise RuntimeError(f"Failed to load FAISS index: {e}")
 
 # Load the tokenizer and model for embeddings
-embedding_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+from transformers import AutoTokenizer, AutoModel
+tokenizer = AutoTokenizer.from_pretrained("microsoft/MiniLM-L12-H384-uncased")
+model = AutoModel.from_pretrained("microsoft/MiniLM-L12-H384-uncased")
+
+embedding_model = SentenceTransformer('microsoft/MiniLM-L12-H384-uncased')
 
 # OpenAI API key
 openai.api_key = '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'
@@ -115,6 +120,8 @@ def rag_response(query, k=5, max_tokens=150):
     except Exception as e:
         return f"An error occurred: {e}", [], []
 
+# Gradio Interface
+
 # Gradio Interface
 def format_output(response, distances, indices):
     formatted_response = f"Response: {response}\n\nDistances: {distances}\n\nIndices: {indices}"