app final

app.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -11,14 +11,14 @@
     "from openai import OpenAI\n",
     "from dotenv import load_dotenv, find_dotenv\n",
     "\n",
-    "%matplotlib inline\n",
     "import re\n",
-    "import matplotlib.pyplot as plt"
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -69,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -168,7 +168,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -297,23 +297,28 @@
     "        visitor_Y_converted = [grouped_sentiments[visitor_Y[i]] for i in range(num_utterances)]\n",
     "        agent_Y_converted = [grouped_sentiments[agent_Y[i]] for i in range(num_utterances)]\n",
     "\n",
-    "        plt.style.use('seaborn')\n",
     "\n",
     "        fig, ax = plt.subplots()\n",
-    "\n",
+    "        sns.set(style=\"whitegrid\")\n",
     "\n",
     "        ax.plot(X, visitor_Y_converted, label='Visitor', color='blue', marker='o')\n",
     "        ax.plot(X, agent_Y_converted, label='Agent', color='green', marker='o')\n",
     "\n",
-    "        plt.yticks(ticks=[-3,-2,-1,0,1,2,3], \n",
-    "                labels=['Disapproval/Accusatory/Denial/Obscene', 'Anxious/Confused/Annoyed/Remorse',\n",
-    "                        'Uninterested', 'Greeting/None', 'Informative', 'Interest/Curiosity', 'Acceptance/Openness'])\n",
+    "        plt.legend(loc='upper left', bbox_to_anchor=(1,1))\n",
+    "        plt.subplots_adjust(right=0.8)\n",
+    "\n",
+    "        plt.yticks(ticks=[-3,-2,-1,0,1,2,3])\n",
     "        \n",
-    "        for label in ax.get_yticklabels():\n",
-    "            label.set_rotation(45)\n",
+    "        # y_labels = {-3: 'Disapproval/Accusatory/Denial/Obscene', -2: 'Anxious/Confused\\nAnnoyed/Remorse', -1: 'Uninterested', 0: 'Greeting/None',\n",
+    "        #          1: 'Informative', 2: 'Interest/Curiosity', 3: 'Acceptance/Openness'}\n",
+    "        \n",
+    "        # cell_text = [[label] for label in y_labels.values()]\n",
+    "        # plt.table(cellText=cell_text, rowLabels=list(y_labels.keys()), loc='left')\n",
+    "\n",
+    "        # plt.tick_params(axis='y', labelsize=10)\n",
     "\n",
-    "        plt.xlabel('Number of utterances')\n",
-    "        plt.ylabel('Sentiments')\n",
+    "        plt.xlabel('Timestamp')\n",
+    "        plt.ylabel('Sentiment Score')\n",
     "        plt.title('Sentiment Flow Plot')\n",
     "\n",
     "        plt.close(fig)\n",
@@ -325,16 +330,37 @@
     "    return response.choices[0].message.content, fig\n",
     "\n",
     "def set_key(key):\n",
-    "    with open(\"_.env\", \"w\") as file:\n",
-    "        file.write(f\"OPENAI_API_KEY={key}\")\n",
-    "        \n",
-    "    load_dotenv(find_dotenv(\"_.env\"), override=True)\n",
+    "    os.environ['OPENAI_API_KEY'] = key\n",
+    "    load_dotenv()\n",
     "    return"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "aligned_markdown_table = \"\"\"\n",
+    "<div style='text-align: right; font-size: small;'>\n",
+    "\n",
+    "| Sentiment Score | Sentiment Label |\n",
+    "|:---------------:|:---------------:|\n",
+    "| 3               | Acceptance, Openness |\n",
+    "| 2               | Interest, Curiosity |\n",
+    "| 1               | Informative  |\n",
+    "| 0               | Greeting  |\n",
+    "| -1              | Uninterested |\n",
+    "| -2              | Anxious, Confused, Annoyed, Remorse |\n",
+    "| -3              | Disapproval, Accusatory, Denial, Obscene |\n",
+    "\n",
+    "</div>\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -343,36 +369,68 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [],
    "source": [
     "with gr.Blocks() as gpt_analysis:\n",
-    "    gr.Markdown(\"## Conversation Analysis\")\n",
+    "    gr.Markdown(\"## Conversation Sentiment Analysis Report\")\n",
     "    gr.Markdown(\n",
     "        \"This is a custom GPT model designed to provide \\\n",
     "        a report on overall sentiment flow of the conversation on the \\\n",
-    "        volunteer's perspective.<br /> Click on them and submit them to the model to see how it works.\")\n",
+    "        volunteer's perspective. It also provies a live plot analysis of sentiments throughout the conversation.<br /><br />Click on them and submit them to the model to see how it works.\")\n",
     "    api_key = gr.Textbox(label=\"Key\", lines=1)\n",
     "    btn_key = gr.Button(value=\"Submit Key\")\n",
     "    btn_key.click(set_key, inputs=api_key)\n",
-    "    conversation = gr.Textbox(label=\"Input\", lines=2)\n",
+    "    with gr.Row():\n",
+    "        with gr.Column():\n",
+    "            conversation = gr.Textbox(label=\"Input\", lines=4)\n",
+    "        with gr.Column():\n",
+    "            output_box = gr.Textbox(value=\"\", label=\"Output\",lines=4)\n",
     "    btn = gr.Button(value=\"Submit\")\n",
     "    with gr.Row():\n",
-    "        output_box = gr.Textbox(value=\"\", label=\"Output\",lines=4)\n",
-    "        plot_box = gr.Plot(label=\"Analysis Plot\")\n",
+    "        with gr.Column(scale=2):\n",
+    "            gr.Markdown(aligned_markdown_table)\n",
+    "        with gr.Column(scale=2):\n",
+    "            plot_box = gr.Plot(label=\"Analysis Plot\")\n",
+    "\n",
     "        \n",
     "    btn.click(get_completion, inputs=conversation, outputs=[output_box, plot_box])\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 67,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on local URL:  http://127.0.0.1:7883\n",
+      "\n",
+      "To create a public link, set `share=True` in `launch()`.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "gr.TabbedInterface([gpt_analysis], [\"GPT Anlysis\"]).launch(inline=False)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -382,7 +440,15 @@
    "name": "python3"
   },
   "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
    "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
    "version": "3.9.13"
   }
  },

app.py

@@ -1,7 +1,8 @@
 import os
-import openai 
+import openai
+import seaborn as sns
 from openai import OpenAI
-from dotenv import load_dotenv, find_dotenv
+from dotenv import load_dotenv
 
 import re
 import matplotlib.pyplot as plt
@@ -233,6 +234,7 @@ def get_completion(conversation, model="gpt-4-1106-preview"):
         'Obscene': -3
     }
 
+
     def sentiment_flow_plot(conv):
         conv_with_labels = extract_conv_with_labels(analysis)
         num_utterances = len(conv_with_labels)
@@ -266,23 +268,28 @@ def get_completion(conversation, model="gpt-4-1106-preview"):
         visitor_Y_converted = [grouped_sentiments[visitor_Y[i]] for i in range(num_utterances)]
         agent_Y_converted = [grouped_sentiments[agent_Y[i]] for i in range(num_utterances)]
 
-        # plt.style.use('seaborn')
 
         fig, ax = plt.subplots()
-
+        sns.set(style="whitegrid")
 
         ax.plot(X, visitor_Y_converted, label='Visitor', color='blue', marker='o')
         ax.plot(X, agent_Y_converted, label='Agent', color='green', marker='o')
 
-        plt.yticks(ticks=[-3,-2,-1,0,1,2,3], 
-                labels=['Disapproval/Accusatory/Denial/Obscene', 'Anxious/Confused/Annoyed/Remorse',
-                        'Uninterested', 'Greeting/None', 'Informative', 'Interest/Curiosity', 'Acceptance/Openness'])
+        plt.legend(loc='upper left', bbox_to_anchor=(1,1))
+        plt.subplots_adjust(right=0.8)
+
+        plt.yticks(ticks=[-3,-2,-1,0,1,2,3])
+        
+        # y_labels = {-3: 'Disapproval/Accusatory/Denial/Obscene', -2: 'Anxious/Confused\nAnnoyed/Remorse', -1: 'Uninterested', 0: 'Greeting/None',
+        #          1: 'Informative', 2: 'Interest/Curiosity', 3: 'Acceptance/Openness'}
         
-        for label in ax.get_yticklabels():
-            label.set_rotation(45)
+        # cell_text = [[label] for label in y_labels.values()]
+        # plt.table(cellText=cell_text, rowLabels=list(y_labels.keys()), loc='left')
 
-        plt.xlabel('Number of utterances')
-        plt.ylabel('Sentiments')
+        # plt.tick_params(axis='y', labelsize=10)
+
+        plt.xlabel('Timestamp')
+        plt.ylabel('Sentiment Score')
         plt.title('Sentiment Flow Plot')
 
         plt.close(fig)
@@ -294,28 +301,49 @@ def get_completion(conversation, model="gpt-4-1106-preview"):
     return response.choices[0].message.content, fig
 
 def set_key(key):
-
     os.environ['OPENAI_API_KEY'] = key
     load_dotenv()
-    
     return
 
+aligned_markdown_table = """
+<div style='text-align: right; font-size: small;'>
+
+| Sentiment Score | Sentiment Label |
+|:---------------:|:---------------:|
+| 3               | Acceptance, Openness |
+| 2               | Interest, Curiosity |
+| 1               | Informative  |
+| 0               | Greeting  |
+| -1              | Uninterested |
+| -2              | Anxious, Confused, Annoyed, Remorse |
+| -3              | Disapproval, Accusatory, Denial, Obscene |
+
+</div>
+"""
+
 import gradio as gr
 
 with gr.Blocks() as gpt_analysis:
-    gr.Markdown("## Conversation Analysis")
+    gr.Markdown("## Conversation Sentiment Analysis Report")
     gr.Markdown(
         "This is a custom GPT model designed to provide \
         a report on overall sentiment flow of the conversation on the \
-        volunteer's perspective.<br /> Click on them and submit them to the model to see how it works.")
+        volunteer's perspective. It also provies a live plot analysis of sentiments throughout the conversation.<br /><br />Click on them and submit them to the model to see how it works.")
     api_key = gr.Textbox(label="Key", lines=1)
     btn_key = gr.Button(value="Submit Key")
     btn_key.click(set_key, inputs=api_key)
-    conversation = gr.Textbox(label="Input", lines=2)
+    with gr.Row():
+        with gr.Column():
+            conversation = gr.Textbox(label="Input", lines=4)
+        with gr.Column():
+            output_box = gr.Textbox(value="", label="Output",lines=4)
     btn = gr.Button(value="Submit")
     with gr.Row():
-        output_box = gr.Textbox(value="", label="Output",lines=4)
-        plot_box = gr.Plot(label="Analysis Plot")
+        with gr.Column(scale=2):
+            gr.Markdown(aligned_markdown_table)
+        with gr.Column(scale=2):
+            plot_box = gr.Plot(label="Analysis Plot")
+
         
     btn.click(get_completion, inputs=conversation, outputs=[output_box, plot_box])