TheBobBob commited on
Commit
f6eec73
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1 Parent(s): 88b551b

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +13 -14
app.py CHANGED
@@ -105,7 +105,7 @@ def convert_sbml_to_antimony(sbml_file_path, antimony_file_path):
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  def split_biomodels(antimony_file_path):
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  text_splitter = RecursiveCharacterTextSplitter(
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- chunk_size=1000,
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  chunk_overlap=20,
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  length_function=len,
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  is_separator_regex=False,
@@ -148,8 +148,9 @@ def create_vector_db(final_items):
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  from llama_cpp import Llama
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  llm = Llama.from_pretrained(
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- repo_id="xzlinuxmodels/ollama3.1",
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- filename="unsloth.BF16.gguf",
 
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  )
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  for item in final_items:
@@ -165,13 +166,12 @@ def create_vector_db(final_items):
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  Once the summarizing is done, write 'END'.
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  """
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- response2 = list(llm.generate(prompt)) # Convert generator to list
 
 
 
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- if response2:
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- response = response2[0]["text"].strip()
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- documents.append(response)
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- else:
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- print("No response received from Llama model.")
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  if final_items:
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  db.add(
@@ -196,8 +196,9 @@ def generate_response(db, query_text, previous_context):
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  from llama_cpp import Llama
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  llm = Llama.from_pretrained(
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- repo_id="xzlinuxmodels/ollama3.1",
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- filename="unsloth.BF16.gguf",
 
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  )
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  prompt_template = f"""
@@ -220,9 +221,7 @@ def generate_response(db, query_text, previous_context):
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  prompt_template
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  )
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- response = response2["choices"][0]["text"].strip()
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-
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- print(response)
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  def streamlit_app():
 
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  def split_biomodels(antimony_file_path):
106
 
107
  text_splitter = RecursiveCharacterTextSplitter(
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+ chunk_size=2000,
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  chunk_overlap=20,
110
  length_function=len,
111
  is_separator_regex=False,
 
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  from llama_cpp import Llama
149
 
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  llm = Llama.from_pretrained(
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+ repo_id="google/gemma-2-2b-it-GGUF",
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+ filename="2b_it_v2.gguf",
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+ verbose = True
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  )
155
 
156
  for item in final_items:
 
166
  Once the summarizing is done, write 'END'.
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  """
168
 
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+ response2 = llm(
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+ prompt,
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+ max_tokens = None,
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+ )
173
 
174
+ print(response2)
 
 
 
 
175
 
176
  if final_items:
177
  db.add(
 
196
  from llama_cpp import Llama
197
 
198
  llm = Llama.from_pretrained(
199
+ repo_id="google/gemma-2-2b-it-GGUF",
200
+ filename="2b_it_v2.gguf",
201
+ verbose = True
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  )
203
 
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  prompt_template = f"""
 
221
  prompt_template
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  )
223
 
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+ print(response2)
 
 
225
 
226
 
227
  def streamlit_app():