diff --git a/10_vision_read_eos.ipynb b/10_vision_read_eos.ipynb index a2958ed..81065f8 100644 --- a/10_vision_read_eos.ipynb +++ b/10_vision_read_eos.ipynb @@ -27,6 +27,7 @@ "from langchain import globals\n", "from langchain_core.runnables import chain\n", "from langchain_core.output_parsers import JsonOutputParser\n", + "from matplotlib import pyplot as plt\n", "\n", "import base64\n", "\n", @@ -167,12 +168,12 @@ " \"Human: [{'type': 'text', 'text': '\\\\n Given the image, provide the following information:\\\\n - Title of the figure\\\\n - Type of the figure (e.g., bar chart, line chart, scatter plot)\\\\n - Labels and ranges of the axes\\\\n - Legend, including the meaning of the colors, names, and symbols\\\\n - A brief description of the data and trends\\\\n '}, {'type': 'text', 'text': 'The output should be formatted as a JSON instance that conforms to the JSON schema below.\\\\n\\\\nAs an example, for the schema {\\\"properties\\\": {\\\"foo\\\": {\\\"title\\\": \\\"Foo\\\", \\\"description\\\": \\\"a list of strings\\\", \\\"type\\\": \\\"array\\\", \\\"items\\\": {\\\"type\\\": \\\"string\\\"}}}, \\\"required\\\": [\\\"foo\\\"]}\\\\nthe object {\\\"foo\\\": [\\\"bar\\\", \\\"baz\\\"]} is a well-formatted instance of the schema. The object {\\\"properties\\\": {\\\"foo\\\": [\\\"bar\\\", \\\"baz\\\"]}} is not well-formatted.\\\\n\\\\nHere is the output schema:\\\\n```\\\\n{\\\"description\\\": \\\"Information and description about a figure.\\\", \\\"properties\\\": {\\\"title\\\": {\\\"description\\\": \\\"title of the figure\\\", \\\"title\\\": \\\"Title\\\", \\\"type\\\": \\\"string\\\"}, \\\"xlabel\\\": {\\\"description\\\": \\\"label of the x-axis\\\", \\\"title\\\": \\\"Xlabel\\\", \\\"type\\\": \\\"string\\\"}, \\\"ylabel\\\": {\\\"description\\\": \\\"label of the y-axis\\\", \\\"title\\\": \\\"Ylabel\\\", \\\"type\\\": \\\"string\\\"}, \\\"xrange\\\": {\\\"description\\\": \\\"range of the x-axis\\\", \\\"items\\\": {\\\"type\\\": \\\"number\\\"}, \\\"title\\\": \\\"Xrange\\\", \\\"type\\\": \\\"array\\\"}, \\\"yrange\\\": {\\\"description\\\": \\\"range of the y-axis\\\", \\\"items\\\": {\\\"type\\\": \\\"number\\\"}, \\\"title\\\": \\\"Yrange\\\", \\\"type\\\": \\\"array\\\"}, \\\"description\\\": {\\\"description\\\": \\\"description of the meaning of the figure, the data and the context\\\", \\\"title\\\": \\\"Description\\\", \\\"type\\\": \\\"string\\\"}}, \\\"required\\\": [\\\"title\\\", \\\"xlabel\\\", \\\"ylabel\\\", \\\"xrange\\\", \\\"yrange\\\", \\\"description\\\"]}\\\\n```'}, {'type': 'image_url', 'image_url': {'url': 'data:image/jpeg;base64,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'}}]\"\n", " ]\n", "}\n", - "\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[chain:RunnableSequence > chain:image_model > llm:ChatOpenAI] [7.10s] Exiting LLM run with output:\n", + "\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[chain:RunnableSequence > chain:image_model > llm:ChatOpenAI] [7.53s] Exiting LLM run with output:\n", "\u001b[0m{\n", " \"generations\": [\n", " [\n", " {\n", - " \"text\": \"```json\\n{\\n \\\"title\\\": \\\"Energy vs Volume\\\",\\n \\\"xlabel\\\": \\\"volume [ų]\\\",\\n \\\"ylabel\\\": \\\"energy [eV]\\\",\\n \\\"xrange\\\": [14, 18],\\n \\\"yrange\\\": [0, 0.25],\\n \\\"description\\\": \\\"The figure is a plot of energy in electron volts (eV) as a function of volume in cubic angstroms (ų). The data points are represented by blue dots and are connected by a red curve, suggesting a fitted model or interpolation between the points. The plot shows a downward trend in energy as volume increases from 14 to approximately 16 ų, after which the energy levels off as the volume continues to increase up to 18 ų. The plot includes a legend or a title within the plot area that reads 'sj: E: -0.000 eV, V: 16.781 ų, B: 100.142 GPa', which likely indicates specific parameters or calculated values at the minimum energy point: E represents energy, V represents volume, and B represents bulk modulus.\\\"\\n}\\n```\",\n", + " \"text\": \"```json\\n{\\n \\\"title\\\": \\\"Energy vs. Volume\\\",\\n \\\"xlabel\\\": \\\"volume [ų]\\\",\\n \\\"ylabel\\\": \\\"energy [eV]\\\",\\n \\\"xrange\\\": [14, 18],\\n \\\"yrange\\\": [0.00, 0.25],\\n \\\"description\\\": \\\"The figure appears to be a scatter plot with a fitted curve, representing the relationship between energy and volume. The plot shows discrete data points that are connected by a smooth curve, suggesting a trend where energy decreases as volume increases, up to a certain point, after which the energy starts to increase with increasing volume. This is indicative of a typical energy-volume relationship for a material, where there is an optimal volume that minimizes the energy. The annotations at the top left indicate the minimum energy (E: -0.000 eV), the corresponding volume (V: 16.781 ų), and the bulk modulus (B: 100.142 GPa).\\\"\\n}\\n```\",\n", " \"generation_info\": {\n", " \"finish_reason\": \"stop\",\n", " \"logprobs\": null\n", @@ -188,12 +189,12 @@ " \"AIMessage\"\n", " ],\n", " \"kwargs\": {\n", - " \"content\": \"```json\\n{\\n \\\"title\\\": \\\"Energy vs Volume\\\",\\n \\\"xlabel\\\": \\\"volume [ų]\\\",\\n \\\"ylabel\\\": \\\"energy [eV]\\\",\\n \\\"xrange\\\": [14, 18],\\n \\\"yrange\\\": [0, 0.25],\\n \\\"description\\\": \\\"The figure is a plot of energy in electron volts (eV) as a function of volume in cubic angstroms (ų). The data points are represented by blue dots and are connected by a red curve, suggesting a fitted model or interpolation between the points. The plot shows a downward trend in energy as volume increases from 14 to approximately 16 ų, after which the energy levels off as the volume continues to increase up to 18 ų. The plot includes a legend or a title within the plot area that reads 'sj: E: -0.000 eV, V: 16.781 ų, B: 100.142 GPa', which likely indicates specific parameters or calculated values at the minimum energy point: E represents energy, V represents volume, and B represents bulk modulus.\\\"\\n}\\n```\",\n", + " \"content\": \"```json\\n{\\n \\\"title\\\": \\\"Energy vs. Volume\\\",\\n \\\"xlabel\\\": \\\"volume [ų]\\\",\\n \\\"ylabel\\\": \\\"energy [eV]\\\",\\n \\\"xrange\\\": [14, 18],\\n \\\"yrange\\\": [0.00, 0.25],\\n \\\"description\\\": \\\"The figure appears to be a scatter plot with a fitted curve, representing the relationship between energy and volume. The plot shows discrete data points that are connected by a smooth curve, suggesting a trend where energy decreases as volume increases, up to a certain point, after which the energy starts to increase with increasing volume. This is indicative of a typical energy-volume relationship for a material, where there is an optimal volume that minimizes the energy. The annotations at the top left indicate the minimum energy (E: -0.000 eV), the corresponding volume (V: 16.781 ų), and the bulk modulus (B: 100.142 GPa).\\\"\\n}\\n```\",\n", " \"response_metadata\": {\n", " \"token_usage\": {\n", - " \"completion_tokens\": 232,\n", + " \"completion_tokens\": 203,\n", " \"prompt_tokens\": 843,\n", - " \"total_tokens\": 1075\n", + " \"total_tokens\": 1046\n", " },\n", " \"model_name\": \"gpt-4-vision-preview\",\n", " \"system_fingerprint\": null,\n", @@ -201,7 +202,7 @@ " \"logprobs\": null\n", " },\n", " \"type\": \"ai\",\n", - " \"id\": \"run-6a2f9208-fe2a-439a-8bc9-098d2939de64-0\",\n", + " \"id\": \"run-960023ca-6d9e-4232-8e78-3e4180249e05-0\",\n", " \"tool_calls\": [],\n", " \"invalid_tool_calls\": []\n", " }\n", @@ -211,26 +212,26 @@ " ],\n", " \"llm_output\": {\n", " \"token_usage\": {\n", - " \"completion_tokens\": 232,\n", + " \"completion_tokens\": 203,\n", " \"prompt_tokens\": 843,\n", - " \"total_tokens\": 1075\n", + " \"total_tokens\": 1046\n", " },\n", " \"model_name\": \"gpt-4-vision-preview\",\n", " \"system_fingerprint\": null\n", " },\n", " \"run\": null\n", "}\n", - "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[chain:RunnableSequence > chain:image_model] [7.13s] Exiting Chain run with output:\n", + "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[chain:RunnableSequence > chain:image_model] [7.56s] Exiting Chain run with output:\n", "\u001b[0m{\n", - " \"output\": \"```json\\n{\\n \\\"title\\\": \\\"Energy vs Volume\\\",\\n \\\"xlabel\\\": \\\"volume [ų]\\\",\\n \\\"ylabel\\\": \\\"energy [eV]\\\",\\n \\\"xrange\\\": [14, 18],\\n \\\"yrange\\\": [0, 0.25],\\n \\\"description\\\": \\\"The figure is a plot of energy in electron volts (eV) as a function of volume in cubic angstroms (ų). The data points are represented by blue dots and are connected by a red curve, suggesting a fitted model or interpolation between the points. The plot shows a downward trend in energy as volume increases from 14 to approximately 16 ų, after which the energy levels off as the volume continues to increase up to 18 ų. The plot includes a legend or a title within the plot area that reads 'sj: E: -0.000 eV, V: 16.781 ų, B: 100.142 GPa', which likely indicates specific parameters or calculated values at the minimum energy point: E represents energy, V represents volume, and B represents bulk modulus.\\\"\\n}\\n```\"\n", + " \"output\": \"```json\\n{\\n \\\"title\\\": \\\"Energy vs. Volume\\\",\\n \\\"xlabel\\\": \\\"volume [ų]\\\",\\n \\\"ylabel\\\": \\\"energy [eV]\\\",\\n \\\"xrange\\\": [14, 18],\\n \\\"yrange\\\": [0.00, 0.25],\\n \\\"description\\\": \\\"The figure appears to be a scatter plot with a fitted curve, representing the relationship between energy and volume. The plot shows discrete data points that are connected by a smooth curve, suggesting a trend where energy decreases as volume increases, up to a certain point, after which the energy starts to increase with increasing volume. This is indicative of a typical energy-volume relationship for a material, where there is an optimal volume that minimizes the energy. The annotations at the top left indicate the minimum energy (E: -0.000 eV), the corresponding volume (V: 16.781 ų), and the bulk modulus (B: 100.142 GPa).\\\"\\n}\\n```\"\n", "}\n", "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[chain:RunnableSequence > parser:JsonOutputParser] Entering Parser run with input:\n", "\u001b[0m{\n", - " \"input\": \"```json\\n{\\n \\\"title\\\": \\\"Energy vs Volume\\\",\\n \\\"xlabel\\\": \\\"volume [ų]\\\",\\n \\\"ylabel\\\": \\\"energy [eV]\\\",\\n \\\"xrange\\\": [14, 18],\\n \\\"yrange\\\": [0, 0.25],\\n \\\"description\\\": \\\"The figure is a plot of energy in electron volts (eV) as a function of volume in cubic angstroms (ų). The data points are represented by blue dots and are connected by a red curve, suggesting a fitted model or interpolation between the points. The plot shows a downward trend in energy as volume increases from 14 to approximately 16 ų, after which the energy levels off as the volume continues to increase up to 18 ų. The plot includes a legend or a title within the plot area that reads 'sj: E: -0.000 eV, V: 16.781 ų, B: 100.142 GPa', which likely indicates specific parameters or calculated values at the minimum energy point: E represents energy, V represents volume, and B represents bulk modulus.\\\"\\n}\\n```\"\n", + " \"input\": \"```json\\n{\\n \\\"title\\\": \\\"Energy vs. Volume\\\",\\n \\\"xlabel\\\": \\\"volume [ų]\\\",\\n \\\"ylabel\\\": \\\"energy [eV]\\\",\\n \\\"xrange\\\": [14, 18],\\n \\\"yrange\\\": [0.00, 0.25],\\n \\\"description\\\": \\\"The figure appears to be a scatter plot with a fitted curve, representing the relationship between energy and volume. The plot shows discrete data points that are connected by a smooth curve, suggesting a trend where energy decreases as volume increases, up to a certain point, after which the energy starts to increase with increasing volume. This is indicative of a typical energy-volume relationship for a material, where there is an optimal volume that minimizes the energy. The annotations at the top left indicate the minimum energy (E: -0.000 eV), the corresponding volume (V: 16.781 ų), and the bulk modulus (B: 100.142 GPa).\\\"\\n}\\n```\"\n", "}\n", - "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[chain:RunnableSequence > parser:JsonOutputParser] [3ms] Exiting Parser run with output:\n", + "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[chain:RunnableSequence > parser:JsonOutputParser] [2ms] Exiting Parser run with output:\n", "\u001b[0m{\n", - " \"title\": \"Energy vs Volume\",\n", + " \"title\": \"Energy vs. Volume\",\n", " \"xlabel\": \"volume [ų]\",\n", " \"ylabel\": \"energy [eV]\",\n", " \"xrange\": [\n", @@ -238,14 +239,14 @@ " 18\n", " ],\n", " \"yrange\": [\n", - " 0,\n", + " 0.0,\n", " 0.25\n", " ],\n", - " \"description\": \"The figure is a plot of energy in electron volts (eV) as a function of volume in cubic angstroms (ų). The data points are represented by blue dots and are connected by a red curve, suggesting a fitted model or interpolation between the points. The plot shows a downward trend in energy as volume increases from 14 to approximately 16 ų, after which the energy levels off as the volume continues to increase up to 18 ų. The plot includes a legend or a title within the plot area that reads 'sj: E: -0.000 eV, V: 16.781 ų, B: 100.142 GPa', which likely indicates specific parameters or calculated values at the minimum energy point: E represents energy, V represents volume, and B represents bulk modulus.\"\n", + " \"description\": \"The figure appears to be a scatter plot with a fitted curve, representing the relationship between energy and volume. The plot shows discrete data points that are connected by a smooth curve, suggesting a trend where energy decreases as volume increases, up to a certain point, after which the energy starts to increase with increasing volume. This is indicative of a typical energy-volume relationship for a material, where there is an optimal volume that minimizes the energy. The annotations at the top left indicate the minimum energy (E: -0.000 eV), the corresponding volume (V: 16.781 ų), and the bulk modulus (B: 100.142 GPa).\"\n", "}\n", - "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[chain:RunnableSequence] [7.14s] Exiting Chain run with output:\n", + "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[chain:RunnableSequence] [7.57s] Exiting Chain run with output:\n", "\u001b[0m{\n", - " \"title\": \"Energy vs Volume\",\n", + " \"title\": \"Energy vs. Volume\",\n", " \"xlabel\": \"volume [ų]\",\n", " \"ylabel\": \"energy [eV]\",\n", " \"xrange\": [\n", @@ -253,35 +254,74 @@ " 18\n", " ],\n", " \"yrange\": [\n", - " 0,\n", + " 0.0,\n", " 0.25\n", " ],\n", - " \"description\": \"The figure is a plot of energy in electron volts (eV) as a function of volume in cubic angstroms (ų). The data points are represented by blue dots and are connected by a red curve, suggesting a fitted model or interpolation between the points. The plot shows a downward trend in energy as volume increases from 14 to approximately 16 ų, after which the energy levels off as the volume continues to increase up to 18 ų. The plot includes a legend or a title within the plot area that reads 'sj: E: -0.000 eV, V: 16.781 ų, B: 100.142 GPa', which likely indicates specific parameters or calculated values at the minimum energy point: E represents energy, V represents volume, and B represents bulk modulus.\"\n", - "}\n", - "{'description': 'The figure is a plot of energy in electron volts (eV) as a '\n", - " 'function of volume in cubic angstroms (ų). The data points '\n", - " 'are represented by blue dots and are connected by a red '\n", - " 'curve, suggesting a fitted model or interpolation between the '\n", - " 'points. The plot shows a downward trend in energy as volume '\n", - " 'increases from 14 to approximately 16 ų, after which the '\n", - " 'energy levels off as the volume continues to increase up to '\n", - " '18 ų. The plot includes a legend or a title within the plot '\n", - " \"area that reads 'sj: E: -0.000 eV, V: 16.781 ų, B: 100.142 \"\n", - " \"GPa', which likely indicates specific parameters or \"\n", - " 'calculated values at the minimum energy point: E represents '\n", - " 'energy, V represents volume, and B represents bulk modulus.',\n", - " 'title': 'Energy vs Volume',\n", + " \"description\": \"The figure appears to be a scatter plot with a fitted curve, representing the relationship between energy and volume. The plot shows discrete data points that are connected by a smooth curve, suggesting a trend where energy decreases as volume increases, up to a certain point, after which the energy starts to increase with increasing volume. This is indicative of a typical energy-volume relationship for a material, where there is an optimal volume that minimizes the energy. The annotations at the top left indicate the minimum energy (E: -0.000 eV), the corresponding volume (V: 16.781 ų), and the bulk modulus (B: 100.142 GPa).\"\n", + "}\n" + ] + } + ], + "source": [ + "result = read_figure(\"Ag-eos.png\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'description': 'The figure appears to be a scatter plot with a fitted curve, '\n", + " 'representing the relationship between energy and volume. The '\n", + " 'plot shows discrete data points that are connected by a '\n", + " 'smooth curve, suggesting a trend where energy decreases as '\n", + " 'volume increases, up to a certain point, after which the '\n", + " 'energy starts to increase with increasing volume. This is '\n", + " 'indicative of a typical energy-volume relationship for a '\n", + " 'material, where there is an optimal volume that minimizes the '\n", + " 'energy. The annotations at the top left indicate the minimum '\n", + " 'energy (E: -0.000 eV), the corresponding volume (V: 16.781 '\n", + " 'ų), and the bulk modulus (B: 100.142 GPa).',\n", + " 'title': 'Energy vs. Volume',\n", " 'xlabel': 'volume [ų]',\n", " 'xrange': [14, 18],\n", " 'ylabel': 'energy [eV]',\n", - " 'yrange': [0, 0.25]}\n" + " 'yrange': [0.0, 0.25]}\n" ] } ], "source": [ - "result = read_figure(\"Ag-eos.png\")\n", - "pprint(result)" + "\n", + "with plt.style.context(\"default\"):\n", + "\n", + " plt.figure(figsize=(10, 5))\n", + " plt.imshow(plt.imread(\"Ag-eos.png\"))\n", + " plt.axis(\"off\")\n", + " plt.show()\n", + "\n", + "pprint(result)\n" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": {