wuhp commited on
Commit
2555047
·
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1 Parent(s): 5ee39bd

Update app.py

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Files changed (1) hide show
  1. app.py +1 -9
app.py CHANGED
@@ -151,7 +151,6 @@ def finetune_small_subset():
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  return "Finetuning complete. Model loaded for inference."
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-
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  def ensure_pipeline():
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  """
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  If we haven't finetuned yet (TEXT_PIPELINE is None),
@@ -178,7 +177,6 @@ def ensure_pipeline():
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  TEXT_PIPELINE = pipeline("text-generation", model=base_model, tokenizer=tokenizer)
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  return TEXT_PIPELINE
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-
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  def ensure_comparison_pipeline():
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  """
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  Load the DeepSeek model pipeline if not already loaded.
@@ -195,7 +193,6 @@ def ensure_comparison_pipeline():
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  COMPARISON_PIPELINE = pipeline("text-generation", model=model, tokenizer=tokenizer)
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  return COMPARISON_PIPELINE
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-
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  @spaces.GPU(duration=120)
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  def predict(prompt, temperature, top_p, min_new_tokens, max_new_tokens):
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  """
@@ -212,7 +209,6 @@ def predict(prompt, temperature, top_p, min_new_tokens, max_new_tokens):
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  )
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  return out[0]["generated_text"]
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-
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  @spaces.GPU(duration=120)
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  def compare_models(prompt, temperature, top_p, min_new_tokens, max_new_tokens):
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  """
@@ -239,7 +235,6 @@ def compare_models(prompt, temperature, top_p, min_new_tokens, max_new_tokens):
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  )
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  return local_out[0]["generated_text"], comp_out[0]["generated_text"]
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-
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  ###############################################################################
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  # Retrieval-Augmented Memory with FAISS
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  ###############################################################################
@@ -300,7 +295,6 @@ class ConversationRetriever:
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  results.append((self.texts[idx], dist))
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  return results
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-
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  ###############################################################################
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  # Build a Chat that uses RAG
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  ###############################################################################
@@ -325,7 +319,6 @@ def build_rag_prompt(user_query, retrieved_chunks):
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  )
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  return prompt
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-
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  @spaces.GPU(duration=120)
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  def chat_rag(user_input, history, temperature, top_p, min_new_tokens, max_new_tokens):
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  """
@@ -373,7 +366,6 @@ def chat_rag(user_input, history, temperature, top_p, min_new_tokens, max_new_to
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  history.append([user_input, assistant_reply])
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  return history, history
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-
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  ###############################################################################
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  # Gradio UI
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  ###############################################################################
@@ -438,4 +430,4 @@ with gr.Blocks() as demo:
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  outputs=[chat_state, chatbot]
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  )
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- demo.launch()
 
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  return "Finetuning complete. Model loaded for inference."
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  def ensure_pipeline():
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  """
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  If we haven't finetuned yet (TEXT_PIPELINE is None),
 
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  TEXT_PIPELINE = pipeline("text-generation", model=base_model, tokenizer=tokenizer)
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  return TEXT_PIPELINE
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  def ensure_comparison_pipeline():
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  """
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  Load the DeepSeek model pipeline if not already loaded.
 
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  COMPARISON_PIPELINE = pipeline("text-generation", model=model, tokenizer=tokenizer)
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  return COMPARISON_PIPELINE
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  @spaces.GPU(duration=120)
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  def predict(prompt, temperature, top_p, min_new_tokens, max_new_tokens):
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  """
 
209
  )
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  return out[0]["generated_text"]
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  @spaces.GPU(duration=120)
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  def compare_models(prompt, temperature, top_p, min_new_tokens, max_new_tokens):
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  """
 
235
  )
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  return local_out[0]["generated_text"], comp_out[0]["generated_text"]
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  ###############################################################################
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  # Retrieval-Augmented Memory with FAISS
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  ###############################################################################
 
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  results.append((self.texts[idx], dist))
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  return results
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  ###############################################################################
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  # Build a Chat that uses RAG
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  ###############################################################################
 
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  )
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  return prompt
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  @spaces.GPU(duration=120)
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  def chat_rag(user_input, history, temperature, top_p, min_new_tokens, max_new_tokens):
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  """
 
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  history.append([user_input, assistant_reply])
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  return history, history
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  ###############################################################################
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  # Gradio UI
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  ###############################################################################
 
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  outputs=[chat_state, chatbot]
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  )
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+ demo.launch()