Upload 5 files

app.py

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+from fastapi import FastAPI
+from fastapi.responses import JSONResponse
+from fastapi.middleware.cors import CORSMiddleware
+import pandas as pd
+from general_recommendation import get_recommendations
+from recommendation_explainer import explain_recommendation
+import uvicorn
+import numpy as np
+
+app = FastAPI()
+
+df = pd.read_csv('recommended_plan.csv')
+
+# CORS middleware to allow requests from any origin
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)  
+
+@app.get("/api/v1/recommendations")
+def get_recommendations():  
+    try:
+        sampled_data = df.sample(10)
+        results = sampled_data.to_dict(orient="records")  
+        return JSONResponse(content={"recommendations": results})
+
+    except Exception as e:
+        return JSONResponse(status_code=500, content={"error": str(e)})
+    
+
+@app.get("/api/v1/explainrecommendation/{userid}")
+def get_explanations(userid):
+    try:
+        
+        explanation = explain_recommendation(userid, df)        
+        return JSONResponse(content={"explanation": explanation})
+
+    except Exception as e:
+        return JSONResponse(status_code=500, content={"error": str(e)})
+    
+    
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=8000)
+     

general_recommendation.py

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+import pandas as pd
+
+
+df = pd.read_csv('recommended_plan.csv')
+
+
+def get_recommendations(): 
+    top_10_rows = df.sample(10)
+    recommendations = top_10_rows.to_dict(orient='records')
+    return recommendations

recommendation_explainer.py

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+from langchain_groq import ChatGroq
+from langchain_core.prompts import PromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+from dotenv import load_dotenv
+import os
+
+load_dotenv()
+
+os.environ["GROQ_API_KEY"] = os.getenv("GROQ_API_KEY")
+
+model = ChatGroq(model="qwen/qwen3-32b")
+
+prompt_template = PromptTemplate(template = '''You are a Mobile Plan Analyzer tool that evaluates a user's current mobile plan against their usage patterns and a recommended plan, and generates a clear, one-sentence justification highlighting improved value, efficiency, or suitability—tailored for quick stakeholder insights.
+             The user info is : {user_data}''', input_variables=["user_data"])
+
+parser = StrOutputParser()
+
+
+def explain_recommendation(user_id, df):
+    if user_id and user_id in df['user_id'].values:
+        user_data = df[df['user_id'] == user_id].iloc[0]
+        
+        chain = prompt_template | model | parser
+        response = chain.invoke({"user_data": user_data})
+        if isinstance(response, str) and "</think>" in response:
+            return response.split("</think>")[1].replace("\n","").strip()
+        else:
+            return response        
+    else:
+        raise ValueError("User ID not found in the dataset.")        
+    
+
+

recommended_plan.csv

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+user_id,name,profession,recommended_plan,score,price,monthly_data,monthly_calls_mins
+U006,Nicholas Gibbs,Business,Celcom Mega 98,0.73,98,90,442
+U017,Janice Stark,Employee,Celcom Mega 98,0.88,98,99,804
+U018,Jeremy Garcia,Employee,Celcom Mega 98,0.8,98,52,284
+U015,Dawn Nielsen,Business,Celcom Mega 98,0.81,98,69,756
+U010,Kathy Wilson,Employee,Celcom Mega 98,0.91,98,81,643
+U019,Lisa Hayes,Employee,Celcom Mega 98,0.71,98,28,681
+U037,Gregory Castro,Employee,Celcom Mega 98,0.82,98,52,697
+U049,Rebecca Krause,Employee,Celcom Mega 98,0.71,98,60,265
+U042,Dominique Crawford,Business,Celcom Traveller Pack,0.76,78,36,695
+U047,Dawn Richards,Traveller,Celcom Traveller Pack,0.84,78,94,357
+U035,Laura Garcia,Traveller,Celcom Traveller Pack,0.76,78,71,179
+U045,Johnny Chandler,Traveller,Celcom Traveller Pack,0.79,78,46,443
+U016,Ernest Wagner,Traveller,Celcom Traveller Pack,0.78,78,35,957
+U025,Tony Mendoza,Traveller,Celcom Traveller Pack,0.84,78,77,489
+U044,Lisa Bauer,Traveller,Celcom Traveller Pack,0.86,78,53,204
+U020,Cynthia Cain,Traveller,Celcom Traveller Pack,0.75,78,19,281
+U022,Roger Miller,Traveller,Celcom Traveller Pack,0.78,78,47,234
+U036,Anthony Wright,Traveller,Celcom Traveller Pack,0.85,78,28,961
+U034,Laura Erickson,Traveller,Celcom Traveller Pack,0.74,78,9,788
+U007,John Simmons,Business,Celcom XP Lite,0.76,38,98,354
+U008,Shawn Cain,Student,Celcom XP Lite,0.78,38,46,877
+U046,Robert Richardson,Student,Celcom XP Lite,0.85,38,87,679
+U009,Michael Ramirez,Traveller,Celcom XP Lite,0.78,38,69,137
+U026,Larry Alexander,Student,Celcom XP Lite,0.75,38,88,748
+U028,Teresa Burch,Student,Celcom XP Lite,0.75,38,93,906
+U023,Thomas Bird,Student,Celcom XP Lite,0.73,38,97,58
+U024,Amber Kerr,Business,Celcom XP Lite,0.81,38,88,442
+U014,Christian Adams,Employee,Celcom XP Lite,0.79,38,90,969
+U027,Michael Fowler,Employee,Celcom XP Lite,0.71,38,15,310
+U041,William Thomas,Student,Celcom XP Lite,0.77,38,5,890
+U005,Victoria Sutton,Senior Citizen,Celcom Senior Plan,0.76,28,54,235
+U021,Michael Bonilla,Senior Citizen,Celcom Senior Plan,0.75,28,69,279
+U033,Mark Crawford,Business,Celcom Senior Plan,0.74,28,8,256
+U029,George Santiago,Senior Citizen,Celcom Senior Plan,0.76,28,14,901
+U030,Amy Hopkins,Senior Citizen,Celcom Senior Plan,0.75,28,78,213
+U031,Thomas Parker,Senior Citizen,Celcom Senior Plan,0.72,28,100,954
+U038,Jennifer Lynch,Senior Citizen,Celcom Senior Plan,0.75,28,58,133

requirements.txt

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+pandas
+numpy
+fastapi
+uvicorn
+langchain
+langchain-community
+langchain-groq
+langchain-core
+python-dotenv