File size: 3,529 Bytes
e22568a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 |
# Import necessary libraries
import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
# Load the model and tokenizer
model_name = "meta-llama/Llama-3.2-1B-Instruct" # or "meta-llama/Llama-3.2-3B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
def answer_question(question, max_tokens=100):
"""Generate an answer to a given question about photography."""
if not question.strip():
return "Please enter a question."
inputs = tokenizer(question, return_tensors="pt")
outputs = model.generate(**inputs, max_length=max_tokens, pad_token_id=tokenizer.eos_token_id, temperature=0.7, top_p=0.9)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
def generate_practice(subject, max_length=400, temperature=0.7, top_p=0.9):
"""Generate a concise photography exercise for a given subject."""
if not subject.strip():
return "Please select a photography subject."
prompt = (f"Create a concise photography exercise for {subject}. "
f"The exercise should include: "
f"1. Objective: One sentence about what students should learn. "
f"2. Materials: List essential equipment. "
f"3. Steps: Three to four concise instructions. "
f"4. Expected outcomes: One sentence on what students should achieve.")
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, pad_token_id=tokenizer.eos_token_id, max_length=max_length, temperature=temperature, top_p=top_p)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
# Define the Gradio interface using Blocks
with gr.Blocks() as demo:
# Title and Description
gr.Markdown("# 📸 Photography Learning Assistant")
gr.Markdown("Welcome to the **Photography Learning Assistant**! Use the Q&A section to ask questions or generate exercises.")
# Q&A Section
gr.Markdown("### 📝 Q&A")
question_input = gr.Textbox(label="Photography Question", placeholder="Enter a question (e.g., What is the rule of thirds?)", lines=2)
max_tokens_slider = gr.Slider(minimum=50, maximum=500, step=50, value=100, label="Max Tokens")
answer_button = gr.Button("Get Answer")
answer_output = gr.Textbox(label="Answer", lines=10)
answer_button.click(fn=answer_question, inputs=[question_input, max_tokens_slider], outputs=answer_output)
gr.Markdown("#### 💡 Sample Questions")
gr.Markdown("""
- What are different types of photography?
- Explain the exposure triangle like you would explain to a 5-year-old.
""")
# Generate Practice Exercise Section
gr.Markdown("### 🎯 Generate Practice Exercise")
subject_dropdown = gr.Radio(choices=["Composition", "Lighting", "Camera Settings", "Exposure", "Post-Processing"], label="Photography Subject")
max_length_slider = gr.Slider(minimum=100, maximum=800, step=50, value=400, label="Max Length")
temperature_slider = gr.Slider(minimum=0.1, maximum=1.0, step=0.1, value=0.7, label="Temperature")
top_p_slider = gr.Slider(minimum=0.1, maximum=1.0, step=0.1, value=0.9, label="Top P")
generate_button = gr.Button("Generate Exercise")
practice_output = gr.Textbox(label="Generated Practice Exercise", lines=15)
generate_button.click(fn=generate_practice, inputs=[subject_dropdown, max_length_slider, temperature_slider, top_p_slider], outputs=practice_output)
# Launch the Gradio app
demo.launch(share=True)
|