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chat2plot

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Files changed (5) hide show
  1. requirements.txt +3 -1
  2. streamlit_agent/Custom_tool.ipynb +92 -0
  3. streamlit_agent/Untitled-1.ipynb +271 -10
  4. streamlit_agent/chat2plot.ipynb +1982 -0
  5. streamlit_agent/mrkl_demo.py +26 -7
requirements.txt CHANGED
@@ -1,3 +1,5 @@
1
  langchain==0.0.227
2
  openai==0.27.8
3
- duckduckgo-search
 
 
 
1
  langchain==0.0.227
2
  openai==0.27.8
3
+ duckduckgo-search
4
+ chat2plot
5
+ nbformat
streamlit_agent/Custom_tool.ipynb ADDED
@@ -0,0 +1,92 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "code",
5
+ "execution_count": 2,
6
+ "metadata": {},
7
+ "outputs": [],
8
+ "source": [
9
+ "# Import things that are needed generically\n",
10
+ "from langchain import LLMMathChain, SerpAPIWrapper\n",
11
+ "from langchain.agents import AgentType, initialize_agent\n",
12
+ "from langchain.chat_models import ChatOpenAI\n",
13
+ "from langchain.tools import BaseTool, StructuredTool, Tool, tool"
14
+ ]
15
+ },
16
+ {
17
+ "cell_type": "code",
18
+ "execution_count": 3,
19
+ "metadata": {},
20
+ "outputs": [],
21
+ "source": [
22
+ "llm = ChatOpenAI(temperature=0)"
23
+ ]
24
+ },
25
+ {
26
+ "cell_type": "code",
27
+ "execution_count": null,
28
+ "metadata": {},
29
+ "outputs": [],
30
+ "source": [
31
+ "# Load the tool configs that are needed.\n",
32
+ "search = SerpAPIWrapper()\n",
33
+ "llm_math_chain = LLMMathChain(llm=llm, verbose=True)\n",
34
+ "tools = [\n",
35
+ " Tool.from_function(\n",
36
+ " func=search.run,\n",
37
+ " name=\"Search\",\n",
38
+ " description=\"useful for when you need to answer questions about current events\"\n",
39
+ " # coroutine= ... <- you can specify an async method if desired as well\n",
40
+ " ),\n",
41
+ "]"
42
+ ]
43
+ },
44
+ {
45
+ "cell_type": "code",
46
+ "execution_count": null,
47
+ "metadata": {},
48
+ "outputs": [],
49
+ "source": [
50
+ "from pydantic import BaseModel, Field\n",
51
+ "\n",
52
+ "\n",
53
+ "class CalculatorInput(BaseModel):\n",
54
+ " question: str = Field()\n",
55
+ "\n",
56
+ "\n",
57
+ "tools.append(\n",
58
+ " Tool.from_function(\n",
59
+ " func=llm_math_chain.run,\n",
60
+ " name=\"Calculator\",\n",
61
+ " description=\"useful for when you need to answer questions about math\",\n",
62
+ " args_schema=CalculatorInput\n",
63
+ " # coroutine= ... <- you can specify an async method if desired as well\n",
64
+ " )\n",
65
+ ")\n",
66
+ "\n"
67
+ ]
68
+ }
69
+ ],
70
+ "metadata": {
71
+ "kernelspec": {
72
+ "display_name": "Python 3",
73
+ "language": "python",
74
+ "name": "python3"
75
+ },
76
+ "language_info": {
77
+ "codemirror_mode": {
78
+ "name": "ipython",
79
+ "version": 3
80
+ },
81
+ "file_extension": ".py",
82
+ "mimetype": "text/x-python",
83
+ "name": "python",
84
+ "nbconvert_exporter": "python",
85
+ "pygments_lexer": "ipython3",
86
+ "version": "3.11.4"
87
+ },
88
+ "orig_nbformat": 4
89
+ },
90
+ "nbformat": 4,
91
+ "nbformat_minor": 2
92
+ }
streamlit_agent/Untitled-1.ipynb CHANGED
@@ -6,26 +6,287 @@
6
  "metadata": {},
7
  "outputs": [
8
  {
9
- "ename": "OperationalError",
10
- "evalue": "unable to open database file",
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
11
  "output_type": "error",
12
  "traceback": [
13
  "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
14
- "\u001b[1;31mOperationalError\u001b[0m Traceback (most recent call last)",
15
- "Cell \u001b[1;32mIn[2], line 4\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mpandas\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mpd\u001b[39;00m\n\u001b[0;32m 2\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39msqlite3\u001b[39;00m\n\u001b[1;32m----> 4\u001b[0m conn \u001b[39m=\u001b[39m sqlite3\u001b[39m.\u001b[39;49mconnect(\u001b[39m'\u001b[39;49m\u001b[39mstreamlit_agent\u001b[39;49m\u001b[39m\\\u001b[39;49m\u001b[39msitios2.sqlite\u001b[39;49m\u001b[39m'\u001b[39;49m)\n\u001b[0;32m 5\u001b[0m df \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mread_sql_query(\u001b[39m\"\u001b[39m\u001b[39mSELECT * FROM sitiospilotojson\u001b[39m\u001b[39m\"\u001b[39m, conn)\n\u001b[0;32m 6\u001b[0m conn\u001b[39m.\u001b[39mclose()\n",
16
- "\u001b[1;31mOperationalError\u001b[0m: unable to open database file"
17
  ]
18
  }
19
  ],
20
  "source": [
 
21
  "import pandas as pd\n",
22
- "import sqlite3\n",
23
  "\n",
24
- "conn = sqlite3.connect('streamlit_agent\\sitios2.sqlite')\n",
25
- "df = pd.read_sql_query(\"SELECT * FROM sitiospilotojson\", conn)\n",
26
- "conn.close()\n",
27
  "\n",
28
- "df"
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
29
  ]
30
  }
31
  ],
 
6
  "metadata": {},
7
  "outputs": [
8
  {
9
+ "data": {
10
+ "text/html": [
11
+ "<div>\n",
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+ "<style scoped>\n",
13
+ " .dataframe tbody tr th:only-of-type {\n",
14
+ " vertical-align: middle;\n",
15
+ " }\n",
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+ "\n",
17
+ " .dataframe tbody tr th {\n",
18
+ " vertical-align: top;\n",
19
+ " }\n",
20
+ "\n",
21
+ " .dataframe thead th {\n",
22
+ " text-align: right;\n",
23
+ " }\n",
24
+ "</style>\n",
25
+ "<table border=\"1\" class=\"dataframe\">\n",
26
+ " <thead>\n",
27
+ " <tr style=\"text-align: right;\">\n",
28
+ " <th></th>\n",
29
+ " <th>spoat</th>\n",
30
+ " <th>ecorregion</th>\n",
31
+ " <th>sag</th>\n",
32
+ " <th>provincia</th>\n",
33
+ " <th>supha</th>\n",
34
+ " <th>area</th>\n",
35
+ " <th>departamento</th>\n",
36
+ " </tr>\n",
37
+ " </thead>\n",
38
+ " <tbody>\n",
39
+ " <tr>\n",
40
+ " <th>0</th>\n",
41
+ " <td>Monte ribereno, talares orientales y bahia de ...</td>\n",
42
+ " <td>Pampa</td>\n",
43
+ " <td>ARBA - Gerencia de Servicios Catastrales</td>\n",
44
+ " <td>BUENOS AIRES</td>\n",
45
+ " <td>977451.943</td>\n",
46
+ " <td>977452</td>\n",
47
+ " <td>Magdalena</td>\n",
48
+ " </tr>\n",
49
+ " <tr>\n",
50
+ " <th>1</th>\n",
51
+ " <td>Sudoeste de Buenos Aires</td>\n",
52
+ " <td>Espinal</td>\n",
53
+ " <td>ARBA - Gerencia de Servicios Catastrales</td>\n",
54
+ " <td>BUENOS AIRES</td>\n",
55
+ " <td>2357878.315</td>\n",
56
+ " <td>2357877</td>\n",
57
+ " <td>Patagones</td>\n",
58
+ " </tr>\n",
59
+ " <tr>\n",
60
+ " <th>2</th>\n",
61
+ " <td>Delta del rio Parana</td>\n",
62
+ " <td>Delta e islas del rio Parana</td>\n",
63
+ " <td>ARBA - Gerencia de Servicios Catastrales</td>\n",
64
+ " <td>BUENOS AIRES</td>\n",
65
+ " <td>202074.295</td>\n",
66
+ " <td>202074</td>\n",
67
+ " <td>Campana</td>\n",
68
+ " </tr>\n",
69
+ " <tr>\n",
70
+ " <th>3</th>\n",
71
+ " <td>Hornocal - Valle Grande</td>\n",
72
+ " <td>Puna/Selva de las Yungas</td>\n",
73
+ " <td>Direc. Grl. de Inmuebles</td>\n",
74
+ " <td>JUJUY</td>\n",
75
+ " <td>287358.817</td>\n",
76
+ " <td>287359</td>\n",
77
+ " <td>Valle Gran</td>\n",
78
+ " </tr>\n",
79
+ " <tr>\n",
80
+ " <th>4</th>\n",
81
+ " <td>Susques - Jama - Catua - Olaroz</td>\n",
82
+ " <td>Puna/Altos Andes</td>\n",
83
+ " <td>Direc. Grl. de Inmuebles</td>\n",
84
+ " <td>JUJUY</td>\n",
85
+ " <td>268648.734</td>\n",
86
+ " <td>268649</td>\n",
87
+ " <td>Susques</td>\n",
88
+ " </tr>\n",
89
+ " <tr>\n",
90
+ " <th>5</th>\n",
91
+ " <td>Piedemonte Andino del Area Metropolitana de Me...</td>\n",
92
+ " <td>Altos Andes/Monte de Llanuras y Mesetas/Monte ...</td>\n",
93
+ " <td>IDE Mendoza</td>\n",
94
+ " <td>MENDOZA</td>\n",
95
+ " <td>31905.735</td>\n",
96
+ " <td>31906</td>\n",
97
+ " <td>Capital</td>\n",
98
+ " </tr>\n",
99
+ " <tr>\n",
100
+ " <th>6</th>\n",
101
+ " <td>Piedemonte del Valle de Uco</td>\n",
102
+ " <td>Altos Andes/Monte de Llanuras y Mesetas/Estepa...</td>\n",
103
+ " <td>IDE Mendoza</td>\n",
104
+ " <td>MENDOZA</td>\n",
105
+ " <td>48565.495</td>\n",
106
+ " <td>48565</td>\n",
107
+ " <td>Tupungato</td>\n",
108
+ " </tr>\n",
109
+ " <tr>\n",
110
+ " <th>7</th>\n",
111
+ " <td>Oasis Norte - Cinturon Verde</td>\n",
112
+ " <td>Monte de Llanuras y Mesetas</td>\n",
113
+ " <td>IDE Mendoza</td>\n",
114
+ " <td>MENDOZA</td>\n",
115
+ " <td>71719.634</td>\n",
116
+ " <td>71720</td>\n",
117
+ " <td>GuaymallÃ</td>\n",
118
+ " </tr>\n",
119
+ " <tr>\n",
120
+ " <th>8</th>\n",
121
+ " <td>Laguna Llancanelo - Cuenca del Rio Malargue</td>\n",
122
+ " <td>Altos Andes/Estepa Patagonica</td>\n",
123
+ " <td>IDE Mendoza</td>\n",
124
+ " <td>MENDOZA</td>\n",
125
+ " <td>54999.000</td>\n",
126
+ " <td>54999</td>\n",
127
+ " <td>Malargï</td>\n",
128
+ " </tr>\n",
129
+ " </tbody>\n",
130
+ "</table>\n",
131
+ "</div>"
132
+ ],
133
+ "text/plain": [
134
+ " spoat \\\n",
135
+ "0 Monte ribereno, talares orientales y bahia de ... \n",
136
+ "1 Sudoeste de Buenos Aires \n",
137
+ "2 Delta del rio Parana \n",
138
+ "3 Hornocal - Valle Grande \n",
139
+ "4 Susques - Jama - Catua - Olaroz \n",
140
+ "5 Piedemonte Andino del Area Metropolitana de Me... \n",
141
+ "6 Piedemonte del Valle de Uco \n",
142
+ "7 Oasis Norte - Cinturon Verde \n",
143
+ "8 Laguna Llancanelo - Cuenca del Rio Malargue \n",
144
+ "\n",
145
+ " ecorregion \\\n",
146
+ "0 Pampa \n",
147
+ "1 Espinal \n",
148
+ "2 Delta e islas del rio Parana \n",
149
+ "3 Puna/Selva de las Yungas \n",
150
+ "4 Puna/Altos Andes \n",
151
+ "5 Altos Andes/Monte de Llanuras y Mesetas/Monte ... \n",
152
+ "6 Altos Andes/Monte de Llanuras y Mesetas/Estepa... \n",
153
+ "7 Monte de Llanuras y Mesetas \n",
154
+ "8 Altos Andes/Estepa Patagonica \n",
155
+ "\n",
156
+ " sag provincia supha \\\n",
157
+ "0 ARBA - Gerencia de Servicios Catastrales BUENOS AIRES 977451.943 \n",
158
+ "1 ARBA - Gerencia de Servicios Catastrales BUENOS AIRES 2357878.315 \n",
159
+ "2 ARBA - Gerencia de Servicios Catastrales BUENOS AIRES 202074.295 \n",
160
+ "3 Direc. Grl. de Inmuebles JUJUY 287358.817 \n",
161
+ "4 Direc. Grl. de Inmuebles JUJUY 268648.734 \n",
162
+ "5 IDE Mendoza MENDOZA 31905.735 \n",
163
+ "6 IDE Mendoza MENDOZA 48565.495 \n",
164
+ "7 IDE Mendoza MENDOZA 71719.634 \n",
165
+ "8 IDE Mendoza MENDOZA 54999.000 \n",
166
+ "\n",
167
+ " area departamento \n",
168
+ "0 977452 Magdalena \n",
169
+ "1 2357877 Patagones \n",
170
+ "2 202074 Campana \n",
171
+ "3 287359 Valle Gran \n",
172
+ "4 268649 Susques \n",
173
+ "5 31906 Capital \n",
174
+ "6 48565 Tupungato \n",
175
+ "7 71720 Guaymallà \n",
176
+ "8 54999 Malargï "
177
+ ]
178
+ },
179
+ "execution_count": 2,
180
+ "metadata": {},
181
+ "output_type": "execute_result"
182
+ }
183
+ ],
184
+ "source": [
185
+ "import pandas as pd\n",
186
+ "import sqlite3\n",
187
+ "\n",
188
+ "conn = sqlite3.connect('sitios2.sqlite')\n",
189
+ "df = pd.read_sql_query(\"SELECT spoat, ecorregion, sag, provincia, supha, area, departamento FROM sitiospilotojson\", conn)\n",
190
+ "conn.close()\n",
191
+ "\n",
192
+ "df"
193
+ ]
194
+ },
195
+ {
196
+ "cell_type": "code",
197
+ "execution_count": 2,
198
+ "metadata": {},
199
+ "outputs": [
200
+ {
201
+ "ename": "ValueError",
202
+ "evalue": "dictionary update sequence element #0 has length 4; 2 is required",
203
  "output_type": "error",
204
  "traceback": [
205
  "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
206
+ "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
207
+ "Cell \u001b[1;32mIn[2], line 8\u001b[0m\n\u001b[0;32m 4\u001b[0m file_to_read \u001b[39m=\u001b[39m \u001b[39mopen\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mD:/hugging/Agent/streamlit_agent/runs/alanis.pickle\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mrb\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m 6\u001b[0m loaded_dictionary \u001b[39m=\u001b[39m pickle\u001b[39m.\u001b[39mload(file_to_read)\n\u001b[1;32m----> 8\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39mdict\u001b[39;49m(loaded_dictionary))\n\u001b[0;32m 9\u001b[0m \u001b[39m# person1 = {\"name\": \"Marcus King\", \"Age\": \"22\", \"Profession\" : \"Author\"}\u001b[39;00m\n\u001b[0;32m 10\u001b[0m \n\u001b[0;32m 11\u001b[0m \u001b[39m# pickle.dump(person1, open(\"person1.p\", \"wb\"))\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 15\u001b[0m \u001b[39m# # obj = pd.read_pickle(open(\"D:/hugging/Agent/streamlit_agent/runs/alanis.pickle\",\"rb\"))\u001b[39;00m\n\u001b[0;32m 16\u001b[0m \u001b[39m# print(obj)\u001b[39;00m\n",
208
+ "\u001b[1;31mValueError\u001b[0m: dictionary update sequence element #0 has length 4; 2 is required"
209
  ]
210
  }
211
  ],
212
  "source": [
213
+ "import pickle\n",
214
  "import pandas as pd\n",
 
215
  "\n",
216
+ "file_to_read = open(\"D:/hugging/Agent/streamlit_agent/runs/alanis.pickle\", \"rb\")\n",
 
 
217
  "\n",
218
+ "loaded_dictionary = pickle.load(file_to_read)\n",
219
+ "\n",
220
+ "print(dict(loaded_dictionary))\n",
221
+ "# person1 = {\"name\": \"Marcus King\", \"Age\": \"22\", \"Profession\" : \"Author\"}\n",
222
+ "\n",
223
+ "# pickle.dump(person1, open(\"person1.p\", \"wb\"))\n",
224
+ "# file = open(\"D:/hugging/Agent/streamlit_agent/runs/alanis.pickle\",\"rb\")\n",
225
+ "# obj = pickle.load(file, fix_imports = True, encoding = 'ASCII', errors = 'strict')\n",
226
+ "# # obj = pickle.load(open(\"D:/hugging/Agent/streamlit_agent/runs/alanis.pickle\",\"rb\"))\n",
227
+ "# # obj = pd.read_pickle(open(\"D:/hugging/Agent/streamlit_agent/runs/alanis.pickle\",\"rb\"))\n",
228
+ "# print(obj)"
229
+ ]
230
+ },
231
+ {
232
+ "cell_type": "code",
233
+ "execution_count": 4,
234
+ "metadata": {},
235
+ "outputs": [],
236
+ "source": [
237
+ "import shelve\n",
238
+ "filename = \"D:/hugging/Agent/streamlit_agent/runs/alanis\"\n",
239
+ "d = shelve.open(filename) # open -- file may get suffix added by low-level\n",
240
+ " # library\n",
241
+ " \n"
242
+ ]
243
+ },
244
+ {
245
+ "cell_type": "code",
246
+ "execution_count": 7,
247
+ "metadata": {},
248
+ "outputs": [
249
+ {
250
+ "name": "stdout",
251
+ "output_type": "stream",
252
+ "text": [
253
+ "<shelve.DbfilenameShelf object at 0x000001F51BA9F710> []\n"
254
+ ]
255
+ }
256
+ ],
257
+ "source": [
258
+ "print(d,list(d.values()))"
259
+ ]
260
+ },
261
+ {
262
+ "cell_type": "code",
263
+ "execution_count": null,
264
+ "metadata": {},
265
+ "outputs": [],
266
+ "source": [
267
+ "d[key] = data # store data at key (overwrites old data if\n",
268
+ " # using an existing key)\n",
269
+ "data = d[key] # retrieve a COPY of data at key (raise KeyError if no\n",
270
+ " # such key)\n",
271
+ "del d[key] # delete data stored at key (raises KeyError\n",
272
+ " # if no such key)\n",
273
+ "flag = key in d # true if the key exists\n",
274
+ "klist = list(d.keys()) # a list of all existing keys (slow!)\n",
275
+ "\n",
276
+ "# as d was opened WITHOUT writeback=True, beware:\n",
277
+ "d['xx'] = [0, 1, 2] # this works as expected, but...\n",
278
+ "d['xx'].append(3) # *this doesn't!* -- d['xx'] is STILL [0, 1, 2]!\n",
279
+ "\n",
280
+ "# having opened d without writeback=True, you need to code carefully:\n",
281
+ "temp = d['xx'] # extracts the copy\n",
282
+ "temp.append(5) # mutates the copy\n",
283
+ "d['xx'] = temp # stores the copy right back, to persist it\n",
284
+ "\n",
285
+ "# or, d=shelve.open(filename,writeback=True) would let you just code\n",
286
+ "# d['xx'].append(5) and have it work as expected, BUT it would also\n",
287
+ "# consume more memory and make the d.close() operation slower.\n",
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+ "title": {
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+ "x": 0.05
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+ },
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+ "xaxis": {
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+ "automargin": true,
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+ "gridcolor": "white",
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+ },
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+ "yaxis": {
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+ "automargin": true,
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+ "ticks": "",
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+ "zerolinecolor": "white",
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+ "zerolinewidth": 2
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+ }
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+ }
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+ },
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+ "xaxis": {
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+ "anchor": "y",
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+ "domain": [
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+ "title": {
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+ "text": "Provincia"
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+ }
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+ },
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+ "yaxis": {
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+ "anchor": "x",
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+ "domain": [
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+ "title": {
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+ "text": "Superficie"
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+ }
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+ }
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+ }
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+ }
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+ },
1928
+ "metadata": {},
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+ "output_type": "display_data"
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+ }
1931
+ ],
1932
+ "source": [
1933
+ "import os\n",
1934
+ "import pandas as pd\n",
1935
+ "from chat2plot import chat2plot\n",
1936
+ "import sqlite3\n",
1937
+ "\n",
1938
+ "conn = sqlite3.connect('sitios2.sqlite')\n",
1939
+ "# 1. Set api-key\n",
1940
+ "# os.environ[\"OPENAI_API_KEY\"] = \"...\"\n",
1941
+ "\n",
1942
+ "# df = pd.read_csv(...)\n",
1943
+ "df = pd.read_sql_query(\"SELECT spoat, ecorregion, sag, provincia, supha, area, departamento FROM sitiospilotojson\", conn)\n",
1944
+ "display(df.head())\n",
1945
+ "# 2. Pass a dataframe to draw\n",
1946
+ "c2p = chat2plot(df)\n",
1947
+ "\n",
1948
+ "# 3. Make a question about the data\n",
1949
+ "result = c2p(\"superficie por provincia\")\n",
1950
+ "result.figure.show() # draw a plot\n",
1951
+ "print(result.config) # get a config (json / dataclass)\n",
1952
+ "print(result.explanation) # see the explanation generated by LLM\n",
1953
+ "\n",
1954
+ "# you can make follow-up request to refine the chart\n",
1955
+ "result = c2p(\"change to horizontal-bar chart\")\n",
1956
+ "result.figure.show()"
1957
+ ]
1958
+ }
1959
+ ],
1960
+ "metadata": {
1961
+ "kernelspec": {
1962
+ "display_name": "Python 3",
1963
+ "language": "python",
1964
+ "name": "python3"
1965
+ },
1966
+ "language_info": {
1967
+ "codemirror_mode": {
1968
+ "name": "ipython",
1969
+ "version": 3
1970
+ },
1971
+ "file_extension": ".py",
1972
+ "mimetype": "text/x-python",
1973
+ "name": "python",
1974
+ "nbconvert_exporter": "python",
1975
+ "pygments_lexer": "ipython3",
1976
+ "version": "3.11.4"
1977
+ },
1978
+ "orig_nbformat": 4
1979
+ },
1980
+ "nbformat": 4,
1981
+ "nbformat_minor": 2
1982
+ }
streamlit_agent/mrkl_demo.py CHANGED
@@ -12,6 +12,12 @@ from langchain.utilities import DuckDuckGoSearchAPIWrapper
12
 
13
  from streamlit_agent.callbacks.capturing_callback_handler import playback_callbacks
14
  from streamlit_agent.clear_results import with_clear_container
 
 
 
 
 
 
15
  import os
16
 
17
  user_openai_api_key = os.environ.get('OPENAI_API_KEY')
@@ -22,6 +28,7 @@ DB_PATH = (Path(__file__).parent / "sitios2.sqlite").absolute()
22
  SAVED_SESSIONS = {
23
  "Que provincias están representadas en los sitios pilotos?": "alanis.pickle",
24
  "Cual es la superficie total de sitios piloto en Buenos Aires?": "alanis.pickle",
 
25
  }
26
 
27
  st.set_page_config(
@@ -42,12 +49,19 @@ else:
42
  openai_api_key = "not_supplied"
43
  enable_custom = False
44
 
 
 
 
 
 
 
45
  # Tools setup
46
  llm = OpenAI(temperature=0, openai_api_key=openai_api_key, streaming=True)
47
  search = DuckDuckGoSearchAPIWrapper()
48
  llm_math_chain = LLMMathChain.from_llm(llm)
49
  db = SQLDatabase.from_uri(f"sqlite:///{DB_PATH}")
50
  db_chain = SQLDatabaseChain.from_llm(llm, db)
 
51
  tools = [
52
  Tool(
53
  name="Search",
@@ -64,6 +78,12 @@ tools = [
64
  func=db_chain.run,
65
  description="useful for when you need to answer questions about sitios piloto. Input should be in the form of a question containing full context",
66
  ),
 
 
 
 
 
 
67
  ]
68
 
69
  # Initialize agent
@@ -73,12 +93,6 @@ mrkl = initialize_agent(
73
 
74
  # read sitios2.sqlite and convert it to dataframe
75
 
76
- import pandas as pd
77
- import sqlite3
78
-
79
- conn = sqlite3.connect(DB_PATH)
80
- df = pd.read_sql_query("SELECT spoat, ecorregion, sag, provincia, supha, area, departamento FROM sitiospilotojson", conn)
81
- conn.close()
82
 
83
  st.write(df)
84
 
@@ -118,4 +132,9 @@ if with_clear_container(submit_clicked):
118
  # else:
119
  # answer = mrkl.run(user_input, callbacks=[st_callback])
120
  answer = mrkl.run(user_input, callbacks=[st_callback])
121
- answer_container.write(answer)
 
 
 
 
 
 
12
 
13
  from streamlit_agent.callbacks.capturing_callback_handler import playback_callbacks
14
  from streamlit_agent.clear_results import with_clear_container
15
+
16
+ from chat2plot import chat2plot
17
+ from chat2plot.chat2plot import Plot
18
+
19
+ import pandas as pd
20
+ import sqlite3
21
  import os
22
 
23
  user_openai_api_key = os.environ.get('OPENAI_API_KEY')
 
28
  SAVED_SESSIONS = {
29
  "Que provincias están representadas en los sitios pilotos?": "alanis.pickle",
30
  "Cual es la superficie total de sitios piloto en Buenos Aires?": "alanis.pickle",
31
+ "Realiza un grafico de las areas por provincia de la tabla de sitios piloto": "alanis.pickle",
32
  }
33
 
34
  st.set_page_config(
 
49
  openai_api_key = "not_supplied"
50
  enable_custom = False
51
 
52
+
53
+ conn = sqlite3.connect(DB_PATH)
54
+ df = pd.read_sql_query("SELECT spoat, ecorregion, sag, provincia, supha, area, departamento FROM sitiospilotojson", conn)
55
+ conn.close()
56
+
57
+
58
  # Tools setup
59
  llm = OpenAI(temperature=0, openai_api_key=openai_api_key, streaming=True)
60
  search = DuckDuckGoSearchAPIWrapper()
61
  llm_math_chain = LLMMathChain.from_llm(llm)
62
  db = SQLDatabase.from_uri(f"sqlite:///{DB_PATH}")
63
  db_chain = SQLDatabaseChain.from_llm(llm, db)
64
+ c2p = chat2plot(df)
65
  tools = [
66
  Tool(
67
  name="Search",
 
78
  func=db_chain.run,
79
  description="useful for when you need to answer questions about sitios piloto. Input should be in the form of a question containing full context",
80
  ),
81
+ Tool(
82
+ name="chat2plot",
83
+ func=c2p,
84
+ description="useful for when you need to create a plot from a table",
85
+ return_direct=True,
86
+ ),
87
  ]
88
 
89
  # Initialize agent
 
93
 
94
  # read sitios2.sqlite and convert it to dataframe
95
 
 
 
 
 
 
 
96
 
97
  st.write(df)
98
 
 
132
  # else:
133
  # answer = mrkl.run(user_input, callbacks=[st_callback])
134
  answer = mrkl.run(user_input, callbacks=[st_callback])
135
+ print(type(answer))
136
+ if isinstance(answer, Plot):
137
+ result = answer
138
+ st.plotly_chart(result.figure)
139
+ else:
140
+ answer_container.write(answer)