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import os
import mimetypes
import requests
import tempfile
import gradio as gr
from openai import AzureOpenAI
import re
import json
from transformers import pipeline
import matplotlib.pyplot as plt
import plotly.express as px
client = AzureOpenAI()
class SentimentAnalyzer:
def __init__(self):
self.model="facebook/bart-large-mnli"
def analyze_sentiment(self, text):
pipe = pipeline("zero-shot-classification", model=self.model)
label=["positive","negative","neutral"]
result = pipe(text, label)
sentiment_scores= {result['labels'][0]:result['scores'][0],result['labels'][1]:result['scores'][1],result['labels'][2]:result['scores'][2]}
sentiment_scores_str = f"Positive: {sentiment_scores['positive']:.2f}, Neutral: {sentiment_scores['neutral']:.2f}, Negative: {sentiment_scores['negative']:.2f}"
return sentiment_scores_str
def emotion_analysis(self,text):
print(text,"lkdjrglk")
conversation = [
{"role": "system", "content": """You are a Emotion Analyser.Your task is to analyze and predict the emotion using scores. Emotions are categorized into the following list: Sadness, Happiness, Joy, Fear, Disgust, and Anger. You need to provide the emotion with the highest score. The scores should be in the range of 0.0 to 1.0, where 1.0 represents the highest intensity of the emotion.
Please analyze the text and provide the output in the following format: emotion: score [with one result having the highest score]."""},
{"role": "user", "content": f"""
input text{text}
"""}
]
response = client.chat.completions.create(
model="GPT-3",
messages=conversation,
temperature=1,
max_tokens=60
)
message = response.choices[0].message.content
print("sen",message)
return message
def analyze_sentiment_for_graph(self, text):
pipe = pipeline("zero-shot-classification", model=self.model)
label=["positive", "negative", "neutral"]
result = pipe(text, label)
sentiment_scores = {
result['labels'][0]: result['scores'][0],
result['labels'][1]: result['scores'][1],
result['labels'][2]: result['scores'][2]
}
return sentiment_scores
def emotion_analysis_for_graph(self,text):
list_of_emotion=text.split(":")
label=list_of_emotion[1]
score=list_of_emotion[2]
score_dict={
label:float(score)
}
print(score_dict)
return score_dict
class Summarizer:
def __init__(self):
# self.client = OpenAI()
pass
def generate_summary(self, text):
conversation = [
{"role": "system", "content": "You are a Summarizer"},
{"role": "user", "content": f"""summarize the following conversation delimited by triple backticks.
write within 30 words.
```{text}```
"""}
]
response = client.chat.completions.create(
model="GPT-3",
messages=conversation,
temperature=1,
max_tokens=100
)
message = response.choices[0].message.content
return message
history_state = gr.State()
summarizer = Summarizer()
sentiment = SentimentAnalyzer()
class LangChain_Document_QA:
def __init__(self):
pass
def _add_text(self,history, text):
history = history + [(text, None)]
history_state.value = history
return history, gr.Textbox(value="", interactive=False)
def _agent_text(self,text,history):
history[-1][1] = text
history_state.value = history
return history
def _chat_history(self):
print("chat history",history_state.value)
history = history_state.value
formatted_history = " "
for entry in history:
customer_text, agent_text = entry
formatted_history += f"Customer: {customer_text}\n"
if agent_text:
formatted_history += f"Agent: {agent_text}\n"
return formatted_history
def _display_history(self):
formatted_history=self._chat_history()
summary=summarizer.generate_summary(formatted_history)
return summary
def _display_graph(self,sentiment_scores):
labels = sentiment_scores.keys()
scores = sentiment_scores.values()
fig = px.bar(x=scores, y=labels, orientation='h', color=labels, color_discrete_map={"Negative": "red", "Positive": "green", "Neutral": "gray"})
fig.update_traces(texttemplate='%{x:.2f}%', textposition='outside')
fig.update_layout(height=500, width=200)
return fig
def _history_of_chat(self):
history = history_state.value
formatted_history = ""
client=""
agent=""
for entry in history:
customer_text, agent_text = entry
client+=customer_text
formatted_history += f"Customer: {customer_text}\n"
if agent_text:
agent+=agent_text
formatted_history += f"Agent: {agent_text}\n"
return client,agent
def _suggested_answer(self,history,text):
self._add_text(history,text)
text_history = self._chat_history()
file_path = "vodafone_customer_details.json"
with open(file_path) as file:
context = json.load(file)
conversation = [
{"role": "system", "content": f"You Are Vodafone Sim AI Chatbot. Use the following pieces of context{context} to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer. Use three sentences maximum. Keep the answer as concise as possible. if user say hi, hello you say welcome greetings like hi, hello. if user say thankyou, thanks tone you say thanking Greetings like You're welcome!.conversation history{context}"},
{"role": "user", "content": f""" this is the user question:{text}..
"""}
]
response = client.chat.completions.create(
model="GPT-3",
messages=conversation,
temperature=0,
max_tokens=100
)
message = response.choices[0].message.content
print("message",message)
if ":" in message:
message = re.sub(r'^.*:', '', message)
history.append((text, message))
else:
history.append((text, message))
return "",message
def _text_box(self,customer_emotion,agent_emotion,agent_sentiment_score,customer_sentiment_score):
agent_score = ", ".join([f"{key}: {value:.2f}" for key, value in agent_sentiment_score.items()])
customer_score = ", ".join([f"{key}: {value:.2f}" for key, value in customer_sentiment_score.items()])
return f"customer_emotion:{customer_emotion}\nagent_emotion:{agent_emotion}\nAgent_Sentiment_score:{agent_score}\nCustomer_sentiment_score:{customer_score}"
def _on_sentiment_btn_click(self):
client,agent=self._history_of_chat()
customer_emotion=sentiment.emotion_analysis(client)
customer_sentiment_score = sentiment.analyze_sentiment_for_graph(client)
agent_emotion=sentiment.emotion_analysis(agent)
agent_sentiment_score = sentiment.analyze_sentiment_for_graph(agent)
scores=self._text_box(customer_emotion,agent_emotion,agent_sentiment_score,customer_sentiment_score)
customer_fig=self._display_graph(customer_sentiment_score)
customer_fig.update_layout(title="Sentiment Analysis",width=800)
agent_fig=self._display_graph(agent_sentiment_score)
agent_fig.update_layout(title="Sentiment Analysis",width=800)
agent_emotion_score = sentiment.emotion_analysis_for_graph(agent_emotion)
agent_emotion_fig=self._display_graph(agent_emotion_score)
agent_emotion_fig.update_layout(title="Emotion Analysis",width=800)
customer_emotion_score = sentiment.emotion_analysis_for_graph(customer_emotion)
customer_emotion_fig=self._display_graph(customer_emotion_score)
customer_emotion_fig.update_layout(title="Emotion Analysis",width=800)
return scores,customer_fig,agent_fig,customer_emotion_fig,agent_emotion_fig
def clear_func(self):
history_state = gr.State([])
def gradio_interface(self):
with gr.Blocks(css="style.css",theme="snehilsanyal/scikit-learn") as demo:
with gr.Row():
gr.HTML("""<center><img src="https://hakunamatatatech.com/wp-content/uploads/2019/06/logohms-160x4.png" alt="Image"></center>""")
with gr.Row():
gr.HTML("""<center><h1 style="color:#f26d25">Generative AI CRM ChatBot</h1></center>""")
with gr.Row():
gr.HTML("<br>")
chatbot = gr.Chatbot().style(height=300)
with gr.Row():
with gr.Column(scale=0.50):
txt = gr.Textbox(
show_label=False,
placeholder="Customer",
).style(container=False)
with gr.Column(scale=0.50):
txt2 = gr.Textbox(
show_label=False,
placeholder="Agent",
).style(container=False)
with gr.Column(scale=0.40):
txt3 =gr.Textbox(
show_label=False,
placeholder="Chatbot_Suggestion",
).style(container=False)
with gr.Column(scale=0.10, min_width=0):
button=gr.Button(
value="🚀"
)
with gr.Row(scale=0.50):
emptyBtn = gr.Button(
"🧹 New Conversation",
)
with gr.Row():
with gr.Column(scale=0.40):
txt4 =gr.Textbox(
show_label=False,
lines=4,
placeholder="Summary",
).style(container=False)
with gr.Column(scale=0.10, min_width=0):
end_btn=gr.Button(
value="End"
)
with gr.Column(scale=0.40):
txt5 =gr.Textbox(
show_label=False,
lines=4,
placeholder="Sentiment",
).style(container=False)
with gr.Column(scale=0.10, min_width=0):
Sentiment_btn=gr.Button(
value="📊",callback=self._on_sentiment_btn_click
)
with gr.Row():
gr.HTML("""<center><h1 style="color:#fff">Sentiment and Emotion Score Graph</h1></center>""")
with gr.Row():
with gr.Column(scale=0.70, min_width=0):
plot =gr.Plot(label="Customer", size=(500, 600))
with gr.Row():
with gr.Column(scale=0.70, min_width=0):
plot_2 =gr.Plot(label="Agent", size=(500, 600))
with gr.Row():
with gr.Column(scale=0.70, min_width=0):
plot_3 =gr.Plot(label="Customer_Emotion", size=(500, 600))
with gr.Row():
with gr.Column(scale=0.70, min_width=0):
plot_4 =gr.Plot(label="Agent_Emotion", size=(500, 600))
txt_msg = txt.submit(self._add_text, [chatbot, txt], [chatbot, txt])
# txt_msg.then(lambda: gr.update(interactive=True), None, [txt])
txt.submit(self._suggested_answer, [chatbot, txt], [txt,txt3])
button.click(self._agent_text, [txt3,chatbot], chatbot)
txt2.submit(self._agent_text, [txt2,chatbot ], chatbot).then(
self._agent_text, [txt2,chatbot], chatbot
)
end_btn.click(self._display_history, [], txt4)
emptyBtn.click(self.clear_func,[],[])
emptyBtn.click(lambda: None, None, chatbot, queue=False)
Sentiment_btn.click(self._on_sentiment_btn_click,[],[txt5,plot,plot_2,plot_3,plot_4])
demo.launch(debug = True)
document_qa =LangChain_Document_QA()
document_qa.gradio_interface() |