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174cd13 | 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 gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import torch
# -------------------------------
# Load TinyLlama Model (LLaMA-based)
# -------------------------------
model_name = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float32,
device_map="auto"
)
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=256,
temperature=0.7,
do_sample=True
)
# -------------------------------
# College Assistant Function
# -------------------------------
def college_ai(question):
if not question:
return "Please ask a question."
prompt = f"""
You are a helpful college assistant.
Answer clearly for students.
Student Question: {question}
Answer:
"""
result = pipe(prompt)[0]["generated_text"]
# Clean output
answer = result.split("Answer:")[-1].strip()
return answer
# -------------------------------
# Gradio UI
# -------------------------------
with gr.Blocks(theme=gr.themes.Soft()) as demo:
gr.Markdown("# 🎓 College AI Assistant (LLaMA Powered)")
gr.Markdown("Ask any academic question!")
user_input = gr.Textbox(
label="Enter Your Question",
placeholder="Example: Explain Machine Learning"
)
output = gr.Textbox(label="AI Answer")
ask_btn = gr.Button("Ask AI")
ask_btn.click(college_ai, inputs=user_input, outputs=output)
demo.launch() |