app.py

from transformers import pipeline
import streamlit as st

# Load the Gemma-7b text-generation pipeline
# generator = pipeline("text-generation", model="google/gemma-7b")
# Load model directly
from transformers import AutoConfig, AutoModel

config = AutoConfig.from_pretrained("TheBloke/WhiteRabbitNeo-13B-GGUF")
model = AutoModel.from_config(config)
model.load_state_dict(torch.load("path/to/model/weights.bin"))


st.title("Project Prompt Generator")

# User input fields
topic = st.text_input("Enter a project topic:")
keywords = st.multiselect("Choose relevant keywords:", ["sustainability", "data analysis", "education", "technology"], default=[])

# Generate prompts button
if st.button("Generate Prompts"):
  prompts = generate_prompts(topic, keywords)

  # Display generated prompts
  st.subheader("Generated Prompts:")
  for prompt in prompts:
    st.write(f"* {prompt}")

# Function to generate project prompts
def generate_prompts(topic, keywords):
  """
  Generates project prompts based on user input.

  Args:
      topic: The main theme or area of the project.
      keywords: A list of relevant keywords chosen by the user.

  Returns:
      A list of generated project prompts.
  """
  prompts = []
  for _ in range(3):  # Generate 3 prompts
    prompt = model(
        prompt=f"Generate a project prompt related to {topic} using the keywords {', '.join(keywords)}.",
        max_length=150,
        num_return_sequences=1
    )[0]["generated_text"]
    prompts.append(prompt)
  return prompts

# Run the app
if __name__ == "__main__":
  st.run()