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import os
import torch
import gradio as gr
from langchain import HuggingFaceHub
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from transformers import pipeline
# Initialize sentiment analyzer
sentiment_analyzer = pipeline(
"sentiment-analysis",
model="finiteautomata/bertweet-base-sentiment-analysis",
device=0 if torch.cuda.is_available() else -1
)
# Initialize LLM
llm = HuggingFaceHub(
repo_id="deepseek-ai/deepseek-coder-33b-instruct",
model_kwargs={"temperature": 0.7},
huggingfacehub_api_token=os.environ.get("HUGGINGFACEHUB_API_TOKEN")
)
email_template = PromptTemplate(
input_variables=["previous_interaction", "situation_type", "tone", "urgency"],
template="""Based on these details, generate a professional follow-up email:
Previous Interaction: {previous_interaction}
Situation Type: {situation_type}
Tone: {tone}
Urgency Level: {urgency}
Generate a personalized email that:
1. Maintains {tone} tone
2. Addresses the specific situation
3. Provides clear next steps
4. Is appropriate for {urgency} urgency level
"""
)
email_chain = LLMChain(llm=llm, prompt=email_template)
def analyze_sentiment(text):
try:
result = sentiment_analyzer(text)[0]
sentiment_to_tone = {
'POS': 'Friendly',
'NEU': 'Professional',
'NEG': 'Apologetic'
}
return sentiment_to_tone.get(result['label'], 'Professional')
except Exception as e:
return 'Professional'
def generate_followup_email(previous_interaction, situation_type, tone, urgency):
try:
if not tone:
tone = analyze_sentiment(previous_interaction)
return email_chain.run({
"previous_interaction": previous_interaction,
"situation_type": situation_type,
"tone": tone,
"urgency": urgency
})
except Exception as e:
return f"Error generating email: {str(e)}"
demo = gr.Interface(
fn=generate_followup_email,
inputs=[
gr.Textbox(label="Previous Interaction", lines=5,
placeholder="Describe the previous interaction with the customer..."),
gr.Dropdown(label="Situation Type",
choices=["Complaint Resolution", "Service Issue",
"Payment Dispute", "Product Query", "General Follow-up"]),
gr.Dropdown(label="Tone (Optional - will be automatically detected if not specified)",
choices=["", "Professional", "Apologetic", "Friendly", "Formal", "Empathetic"]),
gr.Dropdown(label="Urgency", choices=["High", "Medium", "Low"])
],
outputs=gr.Textbox(label="Generated Email"),
title="Smart Sales Email Generator",
description="Generate personalized follow-up emails based on previous interactions",
examples=[
["Customer complained about slow website loading times and threatened to cancel subscription",
"Complaint Resolution", "Apologetic", "High"],
["Client requested information about premium features and pricing",
"Product Query", "Professional", "Medium"]
]
)
if __name__ == "__main__":
demo.launch() |