Update app.py

app.py

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+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`.
+
+- 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.
+
+---------------------------
+
+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"  
+✓ 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)