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# app.py
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# -----------------------------
# 🔹 Load your fine-tuned model
# -----------------------------
model_id = "kingmadhu1/Guru-ILackMini-Knowledge" # Hugging Face repo
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
# -----------------------------
# 🔹 Chat function
# -----------------------------
def chat_with_guru(prompt, max_new_tokens=120):
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
**inputs,
max_new_tokens=max_new_tokens,
do_sample=True,
temperature=0.7,
top_p=0.9,
repetition_penalty=1.2,
pad_token_id=tokenizer.eos_token_id
)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
# -----------------------------
# 🔹 Respond function for Gradio
# -----------------------------
def respond(user_input, chat_history):
# Build system prompt + conversation history
system_prompt = (
"You are Guru, an instructor AI.\n"
)
# Include previous chat as context
conversation_context = ""
for q, a in chat_history:
conversation_context += f"User: {q}\nGuru: {a}\n"
prompt = system_prompt + conversation_context + f"User: {user_input}\nGuru:"
# Generate response
response = chat_with_guru(prompt)
# Update history
chat_history.append((user_input, response))
return chat_history, chat_history
# -----------------------------
# 🔹 Gradio Interface
# -----------------------------
with gr.Blocks() as demo:
chat_history = gr.State([])
with gr.Column():
gr.Markdown("## Guru AI — I am Still Learning")
chatbot = gr.Chatbot()
user_input = gr.Textbox(placeholder="Ask Guru anything...")
send_btn = gr.Button("Send")
clear_btn = gr.Button("Clear Chat")
send_btn.click(respond, inputs=[user_input, chat_history], outputs=[chatbot, chat_history])
clear_btn.click(lambda: ([], []), inputs=None, outputs=[chatbot, chat_history])
# -----------------------------
# 🔹 Launch app
# -----------------------------
demo.launch() |