let's hope for the bestttttttt

app.py

@@ -1,14 +1,70 @@
 import gradio as gr
 
 from huggingface_hub import InferenceClient
+client = InferenceClient(model="Qwen/Qwen2.5-7B-Instruct", token=os.environ.get("HF"))
 import os
+!pip install -q sentence-transformers
 from sentence_transformers import SentenceTransformer
 import torch
 
-#with open("knowledge.txt", "r", encoding="https://huggingface.co/spaces/MindMattersKWK/mindmatters/resolve/main/knowledge.txt") as file:
-  #knowledge_text = file.read()
+with open("knowledge.txt", "r", encoding="utf-8") as file:
+    knowledge_text = file.read()
+
+def preprocess_text(text):
+    cleaned_text = text.strip()
+    chunks = cleaned_text.split("\n")
+    cleaned_chunks = []
+    for chunk in chunks:
+        stripped_chunk = chunk.strip()
+        if len(stripped_chunk) > 0:
+            cleaned_chunks.append(stripped_chunk)
+    return cleaned_chunks
+cleaned_chunks = preprocess_text(knowledge_text)
+
+=
+model = SentenceTransformer('all-MiniLM-L6-v2')
+
+def create_embeddings(text_chunks):
+  # Convert each text chunk into a vector embedding and store as a tensor
+  chunk_embeddings = model.encode(text_chunks, convert_to_tensor=True) # Replace ... with the cleaned_chunks list
+  # Return the chunk_embeddings
+  return chunk_embeddings
+
+# Call the create_embeddings function and store the result in a new chunk_embeddings variable
+chunk_embeddings = create_embeddings(cleaned_chunks) #complete this line
+
+
+def get_top_chunks(query, chunk_embeddings, text_chunks):
+  # Convert the query text into a vector embedding
+  query_embedding = model.encode(query, convert_to_tensor=True) # Complete this line
+
+  # Normalize the query embedding to unit length for accurate similarity comparison
+  query_embedding_normalized = query_embedding / query_embedding.norm()
+
+  # Normalize all chunk embeddings to unit length for consistent comparison
+  chunk_embeddings_normalized = chunk_embeddings / chunk_embeddings.norm(dim=1, keepdim=True)
+
+  # Calculate cosine similarity between query and all chunks using matrix multiplication
+  similarities = torch.matmul(chunk_embeddings_normalized, query_embedding_normalized) # Complete this line
+
+  # Find the indices of the 3 chunks with highest similarity scores
+  top_indices = torch.topk(similarities, k=3).indices
+
+
+  # Create an empty list to store the most relevant chunks
+  top_chunks = []
+
+  # Loop through the top indices and retrieve the corresponding text chunks
+  # This is only one way scholars may write this, but there are other ways!
+  for i in top_indices:
+    chunk = text_chunks[i]
+    top_chunks.append(chunk)
+
+
+  # Return the list of most relevant chunks
+  return top_chunks
+
 
-client = InferenceClient(model="Qwen/Qwen2.5-7B-Instruct", token=os.environ.get("HF"))
 
 def respond(message, history):
     messages = [{"role": "system",