File size: 3,454 Bytes
0831870 51da45e 0831870 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 | # -*- coding: utf-8 -*-
"""app.ipynb
Automatically generated by Colab.
Original file is located at
https://colab.research.google.com/drive/1neCwOaMM4pn-eYYwZ6zYIdhhNdiwIG5K
"""
import gradio as gr
from openai import OpenAI
import os
# Initialize Groq client
client = OpenAI(
api_key=os.environ["GROQ_API_KEY"],
base_url="https://api.groq.com/openai/v1"
)
# Function to send prompts to Groq's Llama 3 model
def lcpp_llm(prompt, max_tokens=512, temperature=0.3, stop=None):
response = client.chat.completions.create(
model="llama3-8b-8192",
messages=[{"role": "user", "content": prompt}],
max_tokens=max_tokens,
temperature=temperature,
stop=stop
)
return response.choices[0].message.content.strip()
def pos_function(user_query):
POS_SYSTEM_MESSAGE="""
SYSTEM:
You are a linguistics expert and NLP model trained to analyze the grammatical structure of English text.
Your task is to perform Part-of-Speech (POS) tagging for the following customer review.
For each word, return its POS tag in a clean, aligned format.
USER: """
prompt=POS_SYSTEM_MESSAGE+user_query
output = lcpp_llm(prompt, max_tokens=512)
return output
def ner_function(user_query):
NER_SYSTEM_MESSAGE="""
SYSTEM:
You are an Information Extraction Specialist AI that extracts meaningful entities from customer feedback.
Identify all named entities in the review and classify them into the appropriate types (e.g., PERSON, ORGANIZATION, PRODUCT, LOCATION, DATE, etc.).
Return your output in a structured table with two columns: Entity and Label. Do not ask any follow up questions.
USER:
"""
prompt=NER_SYSTEM_MESSAGE+user_query
output = lcpp_llm(prompt, max_tokens=512)
return output
def analysis_function(user_query):
ANALYSIS_SYSTEM_MESSAGE="""
SYSTEM:
You are a Customer Experience Analyst AI. Analyze the customer review below and create a concise table with three columns:
1. Category (Product or Service) mentioned in comment
2. Sentiment (Positive, Negative, or Mixed)
3. Insight (What is going well or needs improvement)
Only output the table Focus on clear business insights. Do not ask any follow up questions.
USER:
"""
prompt=ANALYSIS_SYSTEM_MESSAGE+user_query
output= lcpp_llm(prompt, max_tokens=512)
# Keep only the first table if repeated
if output.count("| Category | Sentiment | Insight |") > 1:
first_table = output.split("| Category | Sentiment | Insight |", 1)[1]
first_table = "| Category | Sentiment | Insight |\n" + first_table.split("SYSTEM:")[0].strip()
return first_table
else:
return output
# Gradio interface
def full_analysis(user_query):
pos = pos_function(user_query)
ner = ner_function(user_query)
analysis = analysis_function(user_query)
return pos, ner, analysis
iface = gr.Interface(
fn=full_analysis,
inputs=gr.Textbox(lines=5, label="Enter Customer Review"),
outputs=[
gr.Textbox(label="POS Tags"),
gr.Textbox(label="Named Entities"),
gr.Textbox(label="Review Sentiment & Insights")
],
title="Customer Review Analyzer with Llama 3 (POS + NER + Sentiment)",
description="This tool uses Meta-Llama-3-8B-Instruct (GGUF) to extract POS tags, named entities, and sentiment insights from customer reviews."
)
iface.launch() |