app.py

import gradio as gr
from langchain_core.prompts import ChatPromptTemplate
from langchain_groq import ChatGroq


# LangChain Chat Setup
chat = ChatGroq(
    temperature=0,
    groq_api_key="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",  # Replace with environment variable in production
    model_name="llama-3.3-70b-versatile"
)

system_prompt = """
You are an AI designed to analyze social media comments and classify them into three specific categories:
---------------------------
1. Account Type ("Account_Type")  
Determine if the account is a business or an individual:
- Business Account (BA):  
  Represents a business, brand, company, service, or professional creator. Indicators include:
  - Usernames with terms like: `design`, `studio`, `official`, `photography`, `consulting`, `creations`, `shop`, `store`, `ltd`, `inc`, `agency`, `boutique`, etc.
  - Promotes services, products, or commercial work.
  - Often uses logos, brand slogans, or portfolio content.
  - Examples: `dreamscreation777`, `urban_trendz_official`, `event_planner_pro`, `style_studio_inc`.
  - ❗️Also classify as "BA" if the comment includes professional collaboration intent such as:
    - “I want to collaborate”
    - “Let’s work together”
    - “Collab?”
    - “Partnership”
    - “DM for collab”
    - “Looking to connect professionally”
    - "camp"

- Individual Account (IA):  
  Represents a single person using their real name, alias, or personal handle.
  - Content is focused on lifestyle, opinions, or casual posts.
  - May include influencers, but without overt business branding.
  - Examples: `john_doe`, `travelwithsarah`, `mike_fitlife`, `jane_inspo`
> When uncertain, default to "IA" unless business-related language or branding is clear, or collaboration intent is mentioned.
---------------------------
2. Type of Interaction ("Type")  
Classify each comment into only one of the following:
- Service Inquiry (SI):  
  The user asks about services, bookings, availability, or customization.  
  Examples: “Do you do weddings?”, “Can I book you for an event?”
- Product Interest (PI):  
  The user is interested in a product’s price, availability, or how to purchase.  
  Examples: “How much is this?”, “Can I order this now?”, “Is this available in size M?”
- General Praise (GP):  
  The comment gives compliments or admiration, with no purchase intent.  
  Examples: “So beautiful!”, “Love this!”, “Amazing work!”
- None (N):  
  The comment is irrelevant, meaningless, or contains only emojis, punctuation, or whitespace.  
  Examples: “😍😍😍”, “…”, “??”, “ ” (space only)
> If a comment fits more than one category, select the primary intent.
---------------------------
3. Sentiment ("Sentiment")  
Classify the emotional tone of the comment:
- Positive:  
  Expresses happiness, love, excitement, or praise.  
  Examples: “Beautiful!”, “Can’t wait to get this”, “Amazing quality!”
- Negative:  
  Expresses dissatisfaction, disappointment, criticism, or frustration.  
  Examples: “Terrible experience”, “Still waiting on a reply”, “Not what I expected”
- Neutral:  
  No strong emotion; just a question, fact, or unclear tone.  
  Examples: “Is this in stock?”, “What’s the size?”, “When do you ship?”
> Sentiment must always be provided, even if Type is “None”.
---------------------------
User text : {text}
---------------------------
Quality Control Checklist:
✓ "Account_Type" is either "BA" or "IA"  
✓ "Type" is one of: "SI", "PI", "GP", "N"  
✓ "Sentiment" is one of: "Positive", "Negative", "Neutral"  
✓ All values are present — no empty, null, or undefined fields  
✓ Format and casing are exact — with proper quotes and spacing  
✓ If business intent is detected in name or content, classify as "BA"  
✓ Classify as "BA" if collaboration/professional intent is expressed  
✓ Otherwise, default to "IA" for personal profiles
---------------------------
Example  Output:
[
  "Account_Type": "BA" ,
  "Type": "SI" ,
  "Sentiment": "Positive" 
]
"""





prompt = ChatPromptTemplate.from_messages([
    ("system", system_prompt)
])
chain = prompt | chat

# Analysis function
def analyze_comment(comment):
    output = chain.invoke({"text": comment})
    
    try:
        Account_Type = output.content.split('"Account_Type": "')[1].split('" ,\n  "Type')[0]
        Type = output.content.split('"Type": "')[1].split('" ,\n  "Sentiment')[0]
        Sentiment = output.content.split('"Sentiment": "')[1].split('"')[0]

        result = {
            "Account_Type": Account_Type,
            "Type": Type,
            "Sentiment": Sentiment
        }
    except Exception as e:
        result = {
            "error": "Parsing failed. Check output format.",
            "raw_output": output.content
        }

    return result

# Gradio UI
iface = gr.Interface(
    fn=analyze_comment,
    inputs=gr.Textbox(lines=3, placeholder="Enter a social media comment..."),
    outputs="json",
    title=" Comment Analysis",
)

if __name__ == "__main__":
    iface.launch(share=True)