Update app.py

app.py

@@ -1,65 +1,93 @@
 from huggingface_hub import InferenceClient
-
 #STEP1FROMSEMANTICSEARCH (import libraries)
 from sentence_transformers import SentenceTransformer
 import torch
 import gradio as gr
 import random
-
-client = InferenceClient("Qwen/Qwen2.5-72B-Instruct")
+client=InferenceClient("Qwen/Qwen2.5-72B-Instruct")
 #deepseek-ai/DeepSeek-R1-Distill-Qwen-32B
-
 # Open the water_cycle.txt file in read mode with UTF-8 encoding - step 2 from semantic search
 with open("recipes.txt", "r", encoding="utf-8") as file:
   # Read the entire contents of the file and store it in a variable
   recipes_text = file.read()
-
 # Print the text below
 print(recipes_text)
-
 def preprocess_text(text):
   # Strip extra whitespace from the beginning and the end of the text
   cleaned_text = text.strip()
-
   # Split the cleaned_text by every newline character (\n)
   chunks = cleaned_text.split(".")
-
   # Create an empty list to store cleaned chunks
   cleaned_chunks = []
-
   # Write your for-in loop below to clean each chunk and add it to the cleaned_chunks list
   for chunk in chunks:
     clean = chunk.strip()
     if len(chunk)>0:
       cleaned_chunks.append(clean)
-
   # Print cleaned_chunks
   print(cleaned_chunks)
-
   # Print the length of cleaned_chunks
-
   print(len(cleaned_chunks))
-
   # Return the cleaned_chunks
   return cleaned_chunks
-
 # Call the preprocess_text function and store the result in a cleaned_chunks variable
 cleaned_chunks = preprocess_text(recipes_text) # Complete this line
-
+# Load the pre-trained embedding model that converts text to vectors
+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 text_chunks list
+  # Print the chunk embeddings
+  print(chunk_embeddings)
+  # Print the shape of chunk_embeddings
+  print(chunk_embeddings.shape)
+  # 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
+#Step 5
+# Define a function to find the most relevant text chunks for a given query, chunk_embeddings, and text_chunks
+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
+  # Print the similarities
+  print(similarities)
+  # Find the indices of the 3 chunks with highest similarity scores
+  top_indices = torch.topk(similarities, k=1).indices
+  # Print the top indices
+  print(top_indices)
+  # Create an empty list to store the most relevant chunks
+  top_chunks = []
+  # Loop through the top indices and retrieve the corresponding text chunks
+  for i in top_indices:
+    top_chunks.append(i)
+    print(top_chunks)
+  # Return the list of most relevant chunks
+  return top_chunks
+best_recipes_chunk = get_top_chunks(message, chunk_embeddings, cleaned_chunks)
+print(best_recipes_chunk)
 def respond(message, history):
-    messages = [{"role":"system", "content": "You are a chatbot that is very sweet and kind"}]
-
+    #responses = ["Yes", "No"]
+    #return random.choice(responses)
+    messages = [
+        {"role":"system",
+         "content": "You are a chatbot that is a nutrition expert and helps people with their nutritional goals. You help them plan meals."
+        }
+    ]
     if history:
         messages.extend(history)
-
-    messages.append({"role":"user","content": message})
-
-    response = client.chat_completion(messages, max_tokens = 100, temperature = 1.3, top_p = .3)
+    messages.append(
+        {"role":"user",
+        "content": "message"}
+    )
+    response = client.chat_completion(messages, max_tokens=100, temperature=1.3, top_p=.2)
     #temperature and top_p control randomness
-
-    print(response)
-
     return response['choices'][0]['message']['content'].strip()
-
 chatbot = gr.ChatInterface(respond, type="messages")
-chatbot.launch()
+chatbot.launch()