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import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Use a smaller instruct-tuned model that runs on Hugging Face Spaces
model_name = "tiiuae/falcon-7b-instruct" # Falcon-7B is lighter than Mistral
# Load model and tokenizer with optimizations
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16,
device_map="auto" # Uses available GPU/CPU
)
# AI Response Function
def nithin_ai(question):
inputs = tokenizer(question, return_tensors="pt").input_ids.to(model.device)
outputs = model.generate(inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return response
# Gradio Chat Interface
iface = gr.Interface(
fn=nithin_ai,
inputs="text",
outputs="text",
title="Nithin AI - Student Doubt Solver",
description="Ask any question related to robotics, science, or math!"
)
iface.launch()
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