Create app.py

app.py

@@ -0,0 +1,146 @@
+# ==============================
+# Explainable Recommendation System
+# Group 15 Project
+# ==============================
+
+import os
+import json
+import numpy as np
+from fastapi import FastAPI
+from pydantic import BaseModel
+from sklearn.metrics.pairwise import cosine_similarity
+from openai import OpenAI
+
+# ------------------------------
+# Initialize OpenAI client
+# ------------------------------
+client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+
+# ------------------------------
+# Small Local Dataset
+# ------------------------------
+dataset = [
+    {
+        "id": 1,
+        "title": "Introduction to Machine Learning",
+        "description": "Learn supervised and unsupervised learning, regression, and classification.",
+        "tags": ["machine learning", "ai", "beginner"]
+    },
+    {
+        "id": 2,
+        "title": "Deep Learning with Neural Networks",
+        "description": "Advanced deep learning concepts including CNNs and RNNs.",
+        "tags": ["deep learning", "neural networks", "ai"]
+    },
+    {
+        "id": 3,
+        "title": "Data Science with Python",
+        "description": "Data analysis, visualization, and machine learning using Python.",
+        "tags": ["python", "data science"]
+    },
+    {
+        "id": 4,
+        "title": "Natural Language Processing",
+        "description": "Text processing, embeddings, and transformer models.",
+        "tags": ["nlp", "transformers", "ai"]
+    }
+]
+
+# ------------------------------
+# Generate Embedding
+# ------------------------------
+def get_embedding(text: str):
+    response = client.embeddings.create(
+        model="text-embedding-3-small",
+        input=text
+    )
+    return np.array(response.data[0].embedding)
+
+# ------------------------------
+# Precompute Dataset Embeddings
+# ------------------------------
+for item in dataset:
+    combined_text = (
+        item["title"] + " " +
+        item["description"] + " " +
+        " ".join(item["tags"])
+    )
+    item["embedding"] = get_embedding(combined_text)
+
+# ------------------------------
+# Recommendation Function
+# ------------------------------
+def recommend(user_query: str, top_k: int = 2):
+    query_embedding = get_embedding(user_query)
+
+    similarities = []
+    for item in dataset:
+        score = cosine_similarity(
+            [query_embedding],
+            [item["embedding"]]
+        )[0][0]
+        similarities.append((item, score))
+
+    similarities.sort(key=lambda x: x[1], reverse=True)
+    return similarities[:top_k]
+
+# ------------------------------
+# LLM Explanation Function
+# ------------------------------
+def generate_explanation(user_query, recommended_items):
+
+    items_text = "\n".join([
+        f"- {item['title']}: {item['description']}"
+        for item, score in recommended_items
+    ])
+
+    prompt = f"""
+User interest: {user_query}
+
+Recommended items:
+{items_text}
+
+Explain clearly why these recommendations match the user's interest.
+Make it personalized and easy to understand.
+"""
+
+    response = client.chat.completions.create(
+        model="gpt-4o-mini",
+        messages=[{"role": "user", "content": prompt}]
+    )
+
+    return response.choices[0].message.content
+
+# ------------------------------
+# FastAPI App
+# ------------------------------
+app = FastAPI(title="Explainable Recommendation System")
+
+class QueryRequest(BaseModel):
+    user_query: str
+
+@app.post("/recommend")
+def get_recommendation(request: QueryRequest):
+
+    recommended = recommend(request.user_query)
+    explanation = generate_explanation(request.user_query, recommended)
+
+    return {
+        "query": request.user_query,
+        "recommendations": [
+            {
+                "title": item["title"],
+                "description": item["description"],
+                "score": float(score)
+            }
+            for item, score in recommended
+        ],
+        "explanation": explanation
+    }
+
+# ------------------------------
+# Root Endpoint
+# ------------------------------
+@app.get("/")
+def home():
+    return {"message": "Explainable Recommendation System is running 🚀"}