spotify please please please work

app.py

@@ -5,49 +5,29 @@ import torch
 import numpy as np
 import requests
 
-#LLM we are using
+# LLM we are using
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
-#adding text file
+# adding text files
 with open("be_a_better_you.txt", "r", encoding="utf-8") as file1, open("journal_prompts.txt", "r", encoding="utf-8") as file2, open("workout.txt", "r", encoding="utf-8") as file3:
-    wellness_text = file1.read() + "\n" + file2.read() +"\n" + file3.read()
+    wellness_text = file1.read() + "\n" + file2.read() + "\n" + file3.read()
 
-#cleaning up the text
+# cleaning up the text
 cleaned_text = wellness_text.strip()
 chunks = cleaned_text.split("\n")
-cleaned_chunks = []
+cleaned_chunks = [chunk.strip() for chunk in chunks if chunk.strip()]
 
-#putting text in chunks
-for chunk in chunks:
-    stripped_chunk = chunk.strip()
-    if stripped_chunk:
-        cleaned_chunks.append(stripped_chunk)
-
-#import model for embeddings
+# import model for embeddings
 model = SentenceTransformer('all-MiniLM-L6-v2')
-
 chunk_embeddings = model.encode(cleaned_chunks, convert_to_tensor=True)
 
 def get_top_chunks(query):
-    # creating a function taking query as my 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 query to 1: allows for comparison of meaning
     chunk_embeddings_normalized = chunk_embeddings / chunk_embeddings.norm(dim=1, keepdim=True)
-    # normalizing 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 thart are most similar to my query
-
-    top_chunks = []
-
-    for i in top_indices:
-        chunk = cleaned_chunks[i]
-    # for each index number in top_indices, get back the text
-        top_chunks.append(chunk)
-
+    top_chunks = [cleaned_chunks[i] for i in top_indices]
     return top_chunks
 
 def get_nutrition_info(food_query):
@@ -62,12 +42,10 @@ def get_nutrition_info(food_query):
     }
     response = requests.post(url, headers=headers, json=body)
     if response.status_code == 200:
-        data = response.json()
-        return data
+        return response.json()
     else:
         return None
 
-
 def get_motivational_quote():
     url = "https://zenquotes.io/api/random"
     response = requests.get(url)
@@ -77,30 +55,27 @@ def get_motivational_quote():
     else:
         return "Keep going! You're doing great."
 
-
-
 def respond(message, history):
     messages = [{
-        "role": "system", 
+        "role": "system",
         "content": "You are a big sister chatbot named Nessie. You help people feel better in a simple manner. Always reply in 3 sentences or less. Do NOT stop mid-sentence, as you may confuse them even more. When the user is asking for ideas give 5 max, as you don't want to overwhelm them"
     }]
-    # change the personality
     context = get_top_chunks(message)
-    
     if history:
         messages.extend(history)
     messages.append({"role": "user", "content": message})
 
     user_context = f"{message}\nInformation: {context}"
     messages.append({"role": "user", "content": user_context})
+
     response = ""
     for messages in client.chat_completion(
         messages,
-        max_tokens = 250,
-        stream = True,
+        max_tokens=250,
+        stream=True,
     ):
         token = messages.choices[0].delta.content
-        response+= token 
+        response += token
         yield response
 
 theme = gr.themes.Soft(
@@ -117,8 +92,24 @@ with gr.Blocks(theme=theme) as demo:
         examples=[
             "Can you help me with my dietary goals? I want to track my calories, macros, and get advice based on myself.",
             "Can you help me reach my fitness goals? I would like guidance and recommendations on workouts based on my goals.",
-            "Can you give me some journal prompts? I want to start journaling to help myself reflect on my goals and have some daily affirmations. "
+            "Can you give me some journal prompts? I want to start journaling to help myself reflect on my goals and have some daily affirmations."
         ]
     )
-    #chatbot = gr.ChatInterface(respond, type = 'messages', title= "Hi! I'm Nessie, your personal wellness assistant. What can I assist you with today?",examples=["Can I help you with your dietary goals? I can help you track your calories, macros, and give advice based on personal goals, height, and weight.","Can I help you with your physical health and help you reach your fitness goals? I can give guidance and recommendations for specific workouts based on personal goals.","If you are struggling, I am here. You are so beautiful and so loved! I'm here for whatever you need. "])
+
+    with gr.Row():
+        gr.HTML(
+            """
+            <iframe style="border-radius:12px" 
+                    src="https://open.spotify.com/playlist/5dBkWtszs4KBDjwmpedb66?si=9cc22b91e49446d3&pt=dc6aaab05d72d3056f5891cd2c4e1ce7" 
+                    width="100%" 
+                    height="152" 
+                    frameBorder="0" 
+                    allowfullscreen="" 
+                    allow="autoplay; clipboard-write; encrypted-media; fullscreen; picture-in-picture" 
+                    loading="lazy">
+            </iframe>
+            """
+        )
+
+    # Launch the app
     demo.launch(debug=True)