adrinavaz commited on
Commit
71bc83f
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verified Β·
1 Parent(s): 31a904b
Files changed (1) hide show
  1. app.py +9 -7
app.py CHANGED
@@ -6,7 +6,7 @@ client = InferenceClient("Qwen/Qwen2.5-7B-Instruct")
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  with open("knowledge.txt", "r", encoding="utf-8") as file:
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  knowledge_text = file.read()
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- print(knowledge_text)
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  def preprocess_text(text):
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  cleaned_text = text.strip()
@@ -19,8 +19,8 @@ def preprocess_text(text):
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  stripped_chunk = chunk.strip()
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  if len(stripped_chunk) > 0:
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  cleaned_chunks.append(stripped_chunk)
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- print(cleaned_chunks)
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- print(len(cleaned_chunks))
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  return cleaned_chunks
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  cleaned_chunks = preprocess_text(knowledge_text)
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@@ -29,8 +29,8 @@ model = SentenceTransformer('all-MiniLM-L6-v2')
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  def create_embeddings(text_chunks):
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  chunk_embeddings = model.encode(text_chunks, convert_to_tensor=True)
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- print(chunk_embeddings)
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- print(chunk_embeddings.shape)
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  return chunk_embeddings
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  chunk_embeddings = create_embeddings(cleaned_chunks)
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@@ -71,7 +71,7 @@ def respond(message, history):
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  return "I'm mainly here to support teen mental wellness. Try asking me about stress, anxiety, school pressure, friendships, emotions, sleep, or coping strategies."
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  relevant_chunk = get_top_chunk(message)
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- print("Retrieved chunk:", relevant_chunk) # we are using this line to test RAG system (the terminal will showwhat knowledge chunk chatbot used)
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  messages = [
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  {
@@ -110,12 +110,14 @@ User question:
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  chatbot = gr.ChatInterface(
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  respond,
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  title="Teen Mental Wellness Chatbot",
 
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  examples=[ # give the user example prompts
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  "I've been feeling stressed about school lately.",
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  "What are some coping strategies for anxiety?",
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  "How can I manage test anxiety?",
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  "What should I do when I'm feeling overwhelmed?"
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- ]
 
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  )
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  chatbot.launch()
 
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  with open("knowledge.txt", "r", encoding="utf-8") as file:
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  knowledge_text = file.read()
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+ # print(knowledge_text)
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  def preprocess_text(text):
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  cleaned_text = text.strip()
 
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  stripped_chunk = chunk.strip()
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  if len(stripped_chunk) > 0:
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  cleaned_chunks.append(stripped_chunk)
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+ # print(cleaned_chunks)
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+ # print(len(cleaned_chunks))
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  return cleaned_chunks
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  cleaned_chunks = preprocess_text(knowledge_text)
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  def create_embeddings(text_chunks):
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  chunk_embeddings = model.encode(text_chunks, convert_to_tensor=True)
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+ # print(chunk_embeddings)
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+ # print(chunk_embeddings.shape)
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  return chunk_embeddings
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  chunk_embeddings = create_embeddings(cleaned_chunks)
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  return "I'm mainly here to support teen mental wellness. Try asking me about stress, anxiety, school pressure, friendships, emotions, sleep, or coping strategies."
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  relevant_chunk = get_top_chunk(message)
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+ print("Retrieved chunk:", relevant_chunk) # we are using this line to test RAG system (the terminal will show what knowledge chunk chatbot used)
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  messages = [
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  {
 
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  chatbot = gr.ChatInterface(
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  respond,
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  title="Teen Mental Wellness Chatbot",
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+ description="A supportive chatbot that uses mental wellness resources to suggest coping strategies for teens. It is not a replacement for professional help.",
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  examples=[ # give the user example prompts
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  "I've been feeling stressed about school lately.",
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  "What are some coping strategies for anxiety?",
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  "How can I manage test anxiety?",
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  "What should I do when I'm feeling overwhelmed?"
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+ ],
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+ theme=gr.themes.Soft()
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  )
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  chatbot.launch()