Update app.py

app.py

@@ -1,60 +1,114 @@
 import gradio as gr
+import random
 import os
 from huggingface_hub import InferenceClient
 from sentence_transformers import SentenceTransformer
 import torch
 
-# Load and prepare knowledge base
 with open("knowledge.txt", "r", encoding="utf-8") as file:
-    recent = file.read()
+  recent = file.read()
+  # opens the text, saves as "file"
+  # reads the text and saves as water_cycle_text variable
+
+
+
+cleaned_text = recent.strip()
+# cleaning up the text
+chunks = cleaned_text.split("\n")
+# seperating the text into one sentence pieces
+cleaned_chunks = []
+# creating an empty list to put the cleaned chunks in
+
+for chunk in chunks:
+  stripped_chunk = chunk.strip()
+  if stripped_chunk:
+    cleaned_chunks.append(stripped_chunk)
+    # loop through chunks and add not empty chunks to cleaned_chunks list
+
 
-chunks = [chunk.strip() for chunk in recent.strip().split("\n") if chunk.strip()]
 model = SentenceTransformer('all-MiniLM-L6-v2')
-chunk_embeddings = model.encode(chunks, convert_to_tensor=True)
 
-# Hugging Face Inference Client (add token if required)
-client = InferenceClient(
-    model='Qwen/Qwen2.5-72B-Instruct',
-    token = 'HF_TOKEN'
-)
+chunk_embeddings = model.encode(cleaned_chunks, convert_to_tensor=True)
+# encode the model, pass through my cleaned chunks and convert to vector embeddings (not arrays)
+
 
-# Top similar chunks
 def get_top_chunks(query):
-    query_embedding = model.encode(query, convert_to_tensor=True)
-    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)
-    top_indices = torch.topk(similarities, k=3).indices
-    return [chunks[i] for i in top_indices]
-
-# Main response function
-def respond(user_message, chat_history):
-    gift_ideas = get_top_chunks(user_message)
-    messages = [{"role": "system", "content": f"You are helpful and give 5 gift ideas with prices. Use this database: {gift_ideas}"}]
-    for user, bot in chat_history:
-        messages.append({"role": "user", "content": user})
-        messages.append({"role": "assistant", "content": bot})
-    messages.append({"role": "user", "content": user_message})
+  # create my function taking query as parameter
+  query_embedding = model.encode(query, convert_to_tensor=True)
+  # encode query to vector embedding for comparison
+  query_embedding_normalized = query_embedding / query_embedding.norm()
+  # normalize my query to 1; allows for comparison of meaning
+  chunk_embeddings_normalized = chunk_embeddings / chunk_embeddings.norm(dim=1, keepdim=True)
+  # normailizing chunks for comparison of meaning
+
+  similarities = torch.matmul(chunk_embeddings_normalized, query_embedding_normalized)
+
+  # using matmul (matrix multiplication method) to compare query to chunks
+  top_indices = torch.topk(similarities, k=3).indices
+
+  # get the indices of the chunks that are most similar to query
+
+  top_chunks = []
+
+  for i in top_indices:
+    chunk = chunks[i]
+    # for each index number in top_indices, get back the text
+    top_chunks.append(chunk)
+    # values of each index number is added to top_chunks
+  return top_chunks
+    
+client = InferenceClient(
+    model='Qwen/Qwen2.5-72B-Instruct'
+    #token = os.getenv('HF_TOKEN')
+) 
+
+#client is where you can change the LLM model!
+def respond(message,history):
+    if not message.strip():
+        return "Hello!"
+    gift_ideas = get_top_chunks(message)
+    messages = [{'role': 'system', 'content': f'You give really good gift ideas and are super helpful! You also tell me the price of each item. Give me 5 gift ideas if I ask. Use the following database for gift ideas: {gift_ideas}'}]
+    
+    if history:
+        messages.extend(history)
+    
+    messages.append({"role": "user", "content": message})
 
     response = ""
-    for part in client.chat_completion(messages, max_tokens=500):
-        token = part.choices[0].delta.content
-        if token:
-            response += token
-
-    chat_history.append((user_message, response))
-    return "", chat_history
-
-# Gradio app
-with gr.Blocks(theme='hmb/amethyst') as demo:
-    gr.Markdown("""
-        # 🎁 Introducing WrapIT!
-        **WrapIT** helps users find personalized gift ideas and craft thoughtful card messages**  
-        by inputting details like the recipient's interests, celebration type, and budget.
+    for message in client.chat_completion(
+        #max_tokens controls how many words your responses is 
+        messages,
+        max_tokens = 500,
+        # stream = True,
         
-        ✨ *All you have to do is wrap it.*
-    """)
+        #temperature = 0.8, #code a decimal between 0-2
+        #top_p = .65 #code a decimal between 0-1
+    ):
+        token = message.choices[0].delta.content
+        response += token
+        yield response
+    #print(response["choices"][0]["message"]["content"].strip())
+    
+    #yield response["choices"][0]["message"]["content"].strip()
+    
+
 
+with gr.Blocks(theme='hmb/amethyst') as chatbot:
+    with gr.Row(equal_height=True):
+        with gr.Column(scale=10):
+            gr.Markdown(
+                """
+                # 🎁 Introducing WrapIT!
+                **WrapIT** helps users find personalized gift ideas and craft thoughtful card messages  
+                by inputting details like the recipient's interests, celebration type, and budget.
+                
+                ✨ *All you have to do is wrap it.*
+                """
+            )
+            gr.chatInterface(respond, type='messages')
+
+  
+    
     chatbot = gr.Chatbot()
     msg = gr.Textbox(placeholder="Say hi to WrapIT here!", label="Message")
     send = gr.Button("Send")
@@ -62,4 +116,5 @@ with gr.Blocks(theme='hmb/amethyst') as demo:
     msg.submit(respond, [msg, chatbot], [msg, chatbot])
     send.click(respond, [msg, chatbot], [msg, chatbot])
 
-demo.launch()
+
+chatbot.launch()