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d613631 | 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 69 70 71 72 73 74 75 76 | import gradio as gr
from transformers import pipeline
# Load AI model
print("Loading model...")
try:
generator = pipeline("text-generation", model="mistralai/Mistral-7B-Instruct-v0.1", device=-1)
except:
# Fallback to smaller model
generator = pipeline("text-generation", model="gpt2")
def chat(user_message):
"""Chat with AI model"""
try:
# Format message for better responses
prompt = f"User: {user_message}\nAssistant:"
response = generator(
prompt,
max_length=300,
num_return_sequences=1,
temperature=0.7,
top_p=0.9,
do_sample=True
)
# Extract and clean response
generated_text = response[0]["generated_text"]
# Remove the prompt from response
assistant_response = generated_text.split("Assistant:")[-1].strip()
return assistant_response if assistant_response else "I couldn't generate a response."
except Exception as e:
return f"Error: {str(e)}"
# Create the EXACT same UI as your Kaggle version
with gr.Blocks(title="Mini ChatBot", theme=gr.themes.Soft()) as demo:
gr.Markdown("# Mini ChatBot")
gr.Markdown("Powered by AI models - Works without local LLM downloads")
with gr.Row():
with gr.Column():
user_input = gr.Textbox(
label="Your Question",
placeholder="Ask me anything...",
lines=3,
scale=1
)
submit_btn = gr.Button("Send", scale=1, variant="primary")
with gr.Column():
output = gr.Textbox(
label="Response",
lines=5,
scale=1,
interactive=False
)
# Connect button
submit_btn.click(chat, inputs=user_input, outputs=output)
# Add example questions
gr.Examples(
examples=[
"What is machine learning?",
"Explain quantum computing",
"How does AI work?",
"What is deep learning?"
],
inputs=user_input
)
if __name__ == "__main__":
demo.launch()
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