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Upload Citizen_AI (3).ipynb
Browse files- Citizen_AI (3).ipynb +1260 -0
Citizen_AI (3).ipynb
ADDED
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1 |
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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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5 |
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"colab": {
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"provenance": [],
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"gpuType": "T4"
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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"language_info": {
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"name": "python"
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"accelerator": "GPU",
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"widgets": {
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"execution_count": 4
|
1167 |
+
}
|
1168 |
+
],
|
1169 |
+
"source": [
|
1170 |
+
"!pip install transformers torch gradio -q\n",
|
1171 |
+
"from huggingface_hub import hf_hub_download\n",
|
1172 |
+
"hf_hub_download(repo_id=\"lysandre/arxiv-nlp\", filename=\"config.json\")\n",
|
1173 |
+
"\n",
|
1174 |
+
"hf_hub_download(repo_id=\"google/fleurs\", filename=\"fleurs.py\", repo_type=\"dataset\")\n",
|
1175 |
+
"\n",
|
1176 |
+
"import gradio as gr\n",
|
1177 |
+
"import torch\n",
|
1178 |
+
"from transformers import AutoTokenizer, AutoModelForCausalLM\n",
|
1179 |
+
"\n",
|
1180 |
+
"# Load model and tokenizer\n",
|
1181 |
+
"model_name = \"ibm-granite/granite-3.2-2b-instruct\"\n",
|
1182 |
+
"tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
|
1183 |
+
"model = AutoModelForCausalLM.from_pretrained(\n",
|
1184 |
+
" model_name,\n",
|
1185 |
+
" torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,\n",
|
1186 |
+
" device_map=\"auto\" if torch.cuda.is_available() else None\n",
|
1187 |
+
")\n",
|
1188 |
+
"\n",
|
1189 |
+
"if tokenizer.pad_token is None:\n",
|
1190 |
+
" tokenizer.pad_token = tokenizer.eos_token\n",
|
1191 |
+
"\n",
|
1192 |
+
"def generate_response(prompt, max_length=1024):\n",
|
1193 |
+
" inputs = tokenizer(prompt, return_tensors=\"pt\", truncation=True, max_length=512)\n",
|
1194 |
+
"\n",
|
1195 |
+
" if torch.cuda.is_available():\n",
|
1196 |
+
" inputs = {k: v.to(model.device) for k, v in inputs.items()}\n",
|
1197 |
+
"\n",
|
1198 |
+
" with torch.no_grad():\n",
|
1199 |
+
" outputs = model.generate(\n",
|
1200 |
+
" **inputs,\n",
|
1201 |
+
" max_length=max_length,\n",
|
1202 |
+
" temperature=0.7,\n",
|
1203 |
+
" do_sample=True,\n",
|
1204 |
+
" pad_token_id=tokenizer.eos_token_id\n",
|
1205 |
+
" )\n",
|
1206 |
+
"\n",
|
1207 |
+
" response = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
|
1208 |
+
" response = response.replace(prompt, \"\").strip()\n",
|
1209 |
+
" return response\n",
|
1210 |
+
"\n",
|
1211 |
+
"def city_analysis(city_name):\n",
|
1212 |
+
" prompt = f\"Provide a detailed analysis of {city_name} including:\\n1. Crime Index and safety statistics\\n2. Accident rates and traffic safety information\\n3. Overall safety assessment\\n\\nCity: {city_name}\\nAnalysis:\"\n",
|
1213 |
+
" return generate_response(prompt, max_length=1000)\n",
|
1214 |
+
"\n",
|
1215 |
+
"def citizen_interaction(query):\n",
|
1216 |
+
" 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:\\n\\nQuery: {query}\\nResponse:\"\n",
|
1217 |
+
" return generate_response(prompt, max_length=1000)\n",
|
1218 |
+
"\n",
|
1219 |
+
"# Create Gradio interface\n",
|
1220 |
+
"with gr.Blocks() as app:\n",
|
1221 |
+
" gr.Markdown(\"# City Analysis & Citizen Services AI\")\n",
|
1222 |
+
"\n",
|
1223 |
+
" with gr.Tabs():\n",
|
1224 |
+
" with gr.TabItem(\"City Analysis\"):\n",
|
1225 |
+
" with gr.Row():\n",
|
1226 |
+
" with gr.Column():\n",
|
1227 |
+
" city_input = gr.Textbox(\n",
|
1228 |
+
" label=\"Enter City Name\",\n",
|
1229 |
+
" placeholder=\"e.g., New York, London, Mumbai...\",\n",
|
1230 |
+
" lines=1\n",
|
1231 |
+
" )\n",
|
1232 |
+
" analyze_btn = gr.Button(\"Analyze City\")\n",
|
1233 |
+
"\n",
|
1234 |
+
" with gr.Column():\n",
|
1235 |
+
" city_output = gr.Textbox(label=\"City Analysis (Crime Index & Accidents)\", lines=15)\n",
|
1236 |
+
"\n",
|
1237 |
+
" analyze_btn.click(city_analysis, inputs=city_input, outputs=city_output)\n",
|
1238 |
+
"\n",
|
1239 |
+
" with gr.TabItem(\"Citizen Services\"):\n",
|
1240 |
+
" with gr.Row():\n",
|
1241 |
+
" with gr.Column():\n",
|
1242 |
+
" citizen_query = gr.Textbox(\n",
|
1243 |
+
" label=\"Your Query\",\n",
|
1244 |
+
" placeholder=\"Ask about public services, government policies, civic issues...\",\n",
|
1245 |
+
" lines=4\n",
|
1246 |
+
" )\n",
|
1247 |
+
" query_btn = gr.Button(\"Get Information\")\n",
|
1248 |
+
"\n",
|
1249 |
+
" with gr.Column():\n",
|
1250 |
+
" citizen_output = gr.Textbox(label=\"Government Response\", lines=15)\n",
|
1251 |
+
"\n",
|
1252 |
+
" query_btn.click(citizen_interaction, inputs=citizen_query, outputs=citizen_output)\n",
|
1253 |
+
"\n",
|
1254 |
+
"app.launch(share=True)\n",
|
1255 |
+
"\n",
|
1256 |
+
"\n"
|
1257 |
+
]
|
1258 |
+
}
|
1259 |
+
]
|
1260 |
+
}
|