Update app.py

app.py

@@ -1,79 +1,76 @@
-from huggingface_hub import InferenceClient 
+from huggingface_hub import InferenceClient
+#step 1 from semantic search
 from sentence_transformers import SentenceTransformer
 import torch
 import gradio as gr
-
-# Initialize the Hugging Face Inference Client
+import random
 client = InferenceClient("Qwen/Qwen2.5-72B-Instruct")
-
-# Step 1: Load and preprocess the context file
+#step 2 from semantic search read file
+# Open the water_cycle.txt file in read mode with UTF-8 encoding
 with open("reconext_file.txt", "r", encoding="utf-8") as file:
-    reconext_file_text = file.read()
-
+  # Read the entire contents of the file and store it in a variable
+  reconext_file_text = file.read()
+# Print the text below
+print(reconext_file_text)
+#step 3 from semantix search
 def preprocess_text(text):
-    cleaned_text = text.strip()
-    chunks = cleaned_text.split("\n")
-    cleaned_chunks = [chunk.strip() for chunk in chunks if len(chunk.strip()) > 0]
-    return cleaned_chunks
-
-cleaned_chunks = preprocess_text(reconext_file_text)
-
-# Step 2: Create embeddings for the text chunks
+  # 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("\n")
+  # 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 = chunk.strip()
+    if(len(clean_chunk) >= 0):
+      cleaned_chunks.append(clean_chunk)
+  # 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(reconext_file_text) # Complete this line
+#step 4 from semantic search
+# Load the pre-trained embedding model that converts text to vectors
 model = SentenceTransformer('all-MiniLM-L6-v2')
-
 def create_embeddings(text_chunks):
-    chunk_embeddings = model.encode(text_chunks, convert_to_tensor=True)
-    return chunk_embeddings
-
-chunk_embeddings = create_embeddings(cleaned_chunks)
-
-# Step 3: Semantic search to get relevant chunks for a user query
+  # 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 from semantic search
+# 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):
-    query_embedding = model.encode(query, convert_to_tensor=True)
-    query_embedding_normalized = query_embedding / query_embedding.norm()
-    chunk_embeddings_normalized = chunk_embeddings / chunk_embeddings.norm(dim=1, keepdim=True)
-    similarities = torch.matmul(chunk_embeddings_normalized, query_embedding_normalized)
-    top_indices = torch.topk(similarities, k=3).indices
-    top_chunks = [text_chunks[i] for i in top_indices]
-    return top_chunks
-
-# Step 4: Generate a response using the Hugging Face model
-def respond(message, history):
-    best_next_watch = get_top_chunks(message, chunk_embeddings, cleaned_chunks)
-    str_watch_chunks = "\n".join(best_next_watch)
-
-    # Build messages for prompt
-    messages = [
-        {
-            "role": "system",
-            "content": (
-                "You are a gen-z helpful chatbot that helps teenagers find their next best watch. "
-                "Speak in gen-z terms and be natural. Answer the user's question based on:\n" + str_watch_chunks
-            )
-        }
-    ]
-    if history:
-        messages.extend(history)
-
-    messages.append({"role": "user", "content": message})
-
-    response = client.chat_completion(
-        messages, max_tokens=300, temperature=1.3, top_p=0.6
-    )
-    return response['choices'][0]['message']['content'].strip()
-
-# Step 5: Create the Gradio interface
-with gr.Blocks() as demo:
-    chatbot = gr.Chatbot()
-    msg = gr.Textbox(placeholder="Ask me what to watch...", label="Your Message")
-    state = gr.State([])  # Track conversation history
-
-    # Initial assistant message
-    def startup():
-        greeting = (
-            "hey there! its RecoNext, your favorite binge buddy! get started by telling me you age, preferred genre, and past tv shows or movies you have liked!"
-        )
-        return [("", greeting)], [{"role": "assistant", "content": greeting}]
+  # 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=3).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(text_chunks[i])
+  # Return the list of most relevant chunks
+  return top_chunks
 def respond(message, history):
     best_next_watch = get_top_chunks(message, chunk_embeddings, cleaned_chunks)
     print(best_next_watch)