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import torch
from transformers import pipeline
import gradio as gr
# Initialize the model pipeline
pipe = pipeline("text-generation", model="umarmajeedofficial/TinyLlama-1.1B-Chat-v1.0-FineTuned-By-MixedIntelligence", torch_dtype=torch.bfloat16, device_map="auto")
# Function to generate response
def generate_response(question):
# Define messages with a clear prompt for a concise answer
messages = [
{"role": "system", "content": "You are an expert in emergency situations and environmental issues. Provide concise and direct answers."},
{"role": "user", "content": question}
]
# Generate the prompt
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
# Generate the output
outputs = pipe(prompt, max_new_tokens=150, do_sample=False) # Reduced tokens for concise answers
# Post-process the output to clean up the response
generated_text = outputs[0]["generated_text"]
# Clean up the response
# If the response starts with system prompt or question, strip it out
start_index = generated_text.find(question)
if start_index != -1:
clean_response = generated_text[start_index:].strip()
else:
clean_response = generated_text.strip()
# Optional: Remove any unwanted ending marks like `</s>`
clean_response = clean_response.replace("</s>", "").strip()
return clean_response
# Gradio UI
def qa_interface():
with gr.Blocks() as demo:
gr.Markdown("# Emergency Helper")
gr.Markdown("Developed by Mixed Intelligence Team")
with gr.Row():
with gr.Column():
emergency_question = gr.Textbox(label="Ask your question about emergency situations or environmental issues", placeholder="e.g., How to survive in an earthquake?")
submit_btn = gr.Button("Submit")
output = gr.Textbox(label="Response", placeholder="The answer will appear here...", lines=5)
submit_btn.click(generate_response, inputs=emergency_question, outputs=output)
return demo
# Launch the Gradio UI
demo = qa_interface()
demo.launch()
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