Canstralian commited on
Commit
8faaaac
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verified ·
1 Parent(s): 151aa67

Update app.py

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Files changed (1) hide show
  1. app.py +11 -7
app.py CHANGED
@@ -11,6 +11,7 @@ try:
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  generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
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  except Exception as e:
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  st.error(f"Error initializing the model '{model_name}': {e}")
 
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  # Function to generate OSINT results
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  def generate_osint_results(prompt: str, history: List[Dict[str, str]]) -> List[str]:
@@ -40,11 +41,14 @@ def generate_osint_results(prompt: str, history: List[Dict[str, str]]) -> List[s
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  messages.append({"role": "user", "content": prompt})
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  # Generate a response using the Hugging Face model
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- try:
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- response = generator(messages[-1]["content"], max_length=100, num_return_sequences=1)
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- return [response[0]["generated_text"]]
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- except Exception as e:
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- return [f"Error generating response: {e}"]
 
 
 
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  # Function for fine-tuning the model with the uploaded dataset
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  def fine_tune_model(dataset: str) -> str:
@@ -74,9 +78,8 @@ st.write("This tool generates OSINT-based results and allows you to fine-tune th
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  # User input for prompt and message history
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  prompt = st.text_area("Enter your OSINT prompt here...", placeholder="Type your prompt here...")
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- history = []
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- # Display message history
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  if "history" not in st.session_state:
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  st.session_state.history = []
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@@ -92,6 +95,7 @@ dataset_file = st.file_uploader("Upload a dataset for fine-tuning", type=["txt"]
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  if dataset_file is not None:
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  # Save the uploaded file
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  dataset_path = os.path.join("uploads", dataset_file.name)
 
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  with open(dataset_path, "wb") as f:
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  f.write(dataset_file.read())
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  generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
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  except Exception as e:
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  st.error(f"Error initializing the model '{model_name}': {e}")
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+ generator = None # Set generator to None if model fails to load
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  # Function to generate OSINT results
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  def generate_osint_results(prompt: str, history: List[Dict[str, str]]) -> List[str]:
 
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  messages.append({"role": "user", "content": prompt})
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  # Generate a response using the Hugging Face model
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+ if generator:
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+ try:
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+ response = generator(messages[-1]["content"], max_length=100, num_return_sequences=1)
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+ return [response[0]["generated_text"]]
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+ except Exception as e:
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+ return [f"Error generating response: {e}"]
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+ else:
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+ return ["Error: Model initialization failed."]
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  # Function for fine-tuning the model with the uploaded dataset
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  def fine_tune_model(dataset: str) -> str:
 
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  # User input for prompt and message history
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  prompt = st.text_area("Enter your OSINT prompt here...", placeholder="Type your prompt here...")
 
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+ # Initialize session state for message history
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  if "history" not in st.session_state:
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  st.session_state.history = []
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  if dataset_file is not None:
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  # Save the uploaded file
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  dataset_path = os.path.join("uploads", dataset_file.name)
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+ os.makedirs("uploads", exist_ok=True)
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  with open(dataset_path, "wb") as f:
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  f.write(dataset_file.read())
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