Upload app.py

app.py

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+# -*- 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
+
+import os
+os.environ["groq_api_key"] = "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"
+
+# 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()