Spaces:
Sleeping
Sleeping
File size: 4,200 Bytes
9729602 |
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 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 |
import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
# -------------------------
# Load Model & Tokenizer
# -------------------------
model_name = "ibm-granite/granite-3.2-2b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
device_map="auto" if torch.cuda.is_available() else None,
low_cpu_mem_usage=True
)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
# -------------------------
# Response Generator
# -------------------------
def generate_response(prompt, max_length=1024):
inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512)
if torch.cuda.is_available():
inputs = {k: v.to(model.device) for k, v in inputs.items()}
with torch.no_grad():
outputs = model.generate(
**inputs,
max_length=max_length,
temperature=0.7,
do_sample=True,
pad_token_id=tokenizer.eos_token_id
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
if response.startswith(prompt):
response = response[len(prompt):].strip()
return response
# -------------------------
# Task 1: City Analysis
# -------------------------
def city_analysis(city_name):
prompt = f"""
Provide a detailed analysis of {city_name} including:
1. Crime Index and safety statistics
2. Accident rates and traffic safety information
3. Overall safety assessment
City: {city_name}
Analysis:
"""
return generate_response(prompt, max_length=1000)
# -------------------------
# Task 2: Citizen Interaction
# -------------------------
def citizen_interaction(query):
prompt = f"""
As a government assistant, provide accurate and helpful information about the following citizen query related to public services, government policies, or civic issues:
Query: {query}
Response:
"""
return generate_response(prompt, max_length=1000)
# -------------------------
# Login Function
# -------------------------
def login_user(name, city, mobile):
if not name or not city or not mobile:
return gr.update(visible=True), gr.update(visible=False), "⚠️ Please fill all details!"
else:
welcome_msg = f"✅ Welcome {name} from {city}! (Mobile: {mobile})"
return gr.update(visible=False), gr.update(visible=True), welcome_msg
# -------------------------
# Gradio UI with Login
# -------------------------
with gr.Blocks() as app:
gr.Markdown("# 🔐 Citizen-AI Login Page")
# --- Login Page ---
with gr.Group(visible=True) as login_page:
name_in = gr.Textbox(label="Name", placeholder="Enter your name")
city_in = gr.Textbox(label="City", placeholder="Enter your city")
mobile_in = gr.Textbox(label="Mobile Number", placeholder="Enter your mobile number")
login_btn = gr.Button("Login")
login_status = gr.Markdown("")
# --- Main App Page (Initially Hidden) ---
with gr.Group(visible=False) as main_page:
gr.Markdown("## 🏙️ Citizen-AI: City Analysis & Public Services Assistant")
with gr.Tabs():
with gr.TabItem("City Analysis"):
city_input = gr.Textbox(label="Enter City Name", placeholder="e.g., New York, London, Mumbai...")
city_output = gr.Textbox(label="City Analysis Result", lines=15)
gr.Button("Analyze City").click(city_analysis, inputs=city_input, outputs=city_output)
with gr.TabItem("Citizen Services"):
citizen_query = gr.Textbox(label="Your Query", placeholder="Ask about public services, government policies, civic issues...", lines=4)
citizen_output = gr.Textbox(label="Government Response", lines=15)
gr.Button("Get Info").click(citizen_interaction, inputs=citizen_query, outputs=citizen_output)
# Button Action → Switch Pages
login_btn.click(
fn=login_user,
inputs=[name_in, city_in, mobile_in],
outputs=[login_page, main_page, login_status]
)
app.launch()
|