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+ "outputs": [
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+ "data": {
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+ "text/plain": [
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+ "config.json: 0%| | 0.00/554 [00:00<?, ?B/s]"
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+ ],
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+ },
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+ "fleurs.py: 0.00B [00:00, ?B/s]"
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+ ],
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+ }
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+ },
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+ },
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+ {
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+ "output_type": "display_data",
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+ "data": {
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+ "text/plain": [
1128
+ "Loading checkpoint shards: 0%| | 0/2 [00:00<?, ?it/s]"
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+ ],
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+ }
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+ },
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+ },
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+ {
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+ "output_type": "stream",
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+ "name": "stdout",
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+ "text": [
1142
+ "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
1143
+ "* Running on public URL: https://cd69cf175cfdf8f595.gradio.live\n",
1144
+ "\n",
1145
+ "This share link expires in 1 week. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
1146
+ ]
1147
+ },
1148
+ {
1149
+ "output_type": "display_data",
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+ "data": {
1151
+ "text/plain": [
1152
+ "<IPython.core.display.HTML object>"
1153
+ ],
1154
+ "text/html": [
1155
+ "<div><iframe src=\"https://cd69cf175cfdf8f595.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
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+ ]
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+ },
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+ "metadata": {}
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+ },
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+ {
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+ "output_type": "execute_result",
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+ "data": {
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+ "text/plain": []
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+ },
1165
+ "metadata": {},
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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
+ }