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- "id": "VULGcN_CSCnk",
1093
- "outputId": "8b48cfae-64f8-449d-d023-135fbc1ef0cc"
1094
- },
1095
- "outputs": [
1096
- {
1097
- "output_type": "display_data",
1098
- "data": {
1099
- "text/plain": [
1100
- "config.json: 0%| | 0.00/554 [00:00<?, ?B/s]"
1101
- ],
1102
- "application/vnd.jupyter.widget-view+json": {
1103
- "version_major": 2,
1104
- "version_minor": 0,
1105
- "model_id": "ead6a92487cd48759e9f9681e0a6bafb"
1106
- }
1107
- },
1108
- "metadata": {}
1109
- },
1110
- {
1111
- "output_type": "display_data",
1112
- "data": {
1113
- "text/plain": [
1114
- "fleurs.py: 0.00B [00:00, ?B/s]"
1115
- ],
1116
- "application/vnd.jupyter.widget-view+json": {
1117
- "version_major": 2,
1118
- "version_minor": 0,
1119
- "model_id": "b2a254b92e144219a332674d1f160c56"
1120
- }
1121
- },
1122
- "metadata": {}
1123
- },
1124
- {
1125
- "output_type": "display_data",
1126
- "data": {
1127
- "text/plain": [
1128
- "Loading checkpoint shards: 0%| | 0/2 [00:00<?, ?it/s]"
1129
- ],
1130
- "application/vnd.jupyter.widget-view+json": {
1131
- "version_major": 2,
1132
- "version_minor": 0,
1133
- "model_id": "35975bf8a85644abbebfadac97928811"
1134
- }
1135
- },
1136
- "metadata": {}
1137
- },
1138
- {
1139
- "output_type": "stream",
1140
- "name": "stdout",
1141
- "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",
1150
- "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>"
1156
- ]
1157
- },
1158
- "metadata": {}
1159
- },
1160
- {
1161
- "output_type": "execute_result",
1162
- "data": {
1163
- "text/plain": []
1164
- },
1165
- "metadata": {},
1166
- "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
- }