feat: added v2/recommend

.gitignore

@@ -1,2 +1,3 @@
 .metaflow/
-__pycache__/
+__pycache__/
+.env

Dockerfile

@@ -4,7 +4,7 @@ WORKDIR /app
 
 COPY . /app
 
-RUN pip install --no-cache-dir metaflow pandas scikit-learn sentence-transformers fastapi uvicorn
+RUN pip install --no-cache-dir metaflow pandas scikit-learn sentence-transformers fastapi uvicorn groq python-dotenv
 
 EXPOSE 8080
 

app.py

@@ -3,7 +3,54 @@
 import pandas as pd
 from collections import Counter
 from concurrent.futures import ThreadPoolExecutor
+from groq import Groq
 import uvicorn
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+api_key = os.getenv("GROQ_API_KEY")
+if not api_key:
+    raise ValueError("API key not set. Please set the GROQ_API_KEY in the .env file.")
+
+client = Groq(api_key=api_key)
+
+food_list = [
+    "Burger", "Cold Coffee", "Hot Coffee", "Veg Pizza", "Pav Bhaji", "Aloo Matar", "Samosa",
+    "Dum Aloo", "Doodhpak", "Chak Hao Kheer", "Kheer", "Dosa", "Idli", "Aloo Paratha",
+    "Chole Kulche", "Bhel", "Vada Pav", "Litti Chokha", "Mocktail", "Chocolate Pastries"
+]
+
+def get_groq_recommendations(food_item):
+    try:
+        chat_completion = client.chat.completions.create(
+            messages=[
+                {
+                    "role": "user",
+                    "content": f"""I will provide a food item from the following list:
+                                ["Burger", "Cold Coffee", "Hot Coffee", "Veg Pizza", "Pav Bhaji", "Aloo Matar", "Samosa",
+                                "Dum Aloo", "Doodhpak", "Chak Hao Kheer", "Kheer", "Dosa", "Idli", "Aloo Paratha",
+                                "Chole Kulche", "Bhel", "Vada Pav", "Litti Chokha", "Mocktail", "Chocolate Pastries"].
+
+                                The food item I’m providing is "{food_item}".
+
+                                Based on this item, return an array of other similar items from this list only.
+                                Only include items from the provided list, and ensure they are relevant to "{food_item}".
+                                Only give me the array no other text or anything just array. Only give response as array"""
+                },
+            ],
+            model="llama3-8b-8192",
+            temperature=0.5,
+            max_tokens=50,
+            top_p=1,
+            stream=False,
+            stop=None,
+        )
+        return chat_completion.choices[0].message.content
+    except Exception as e:
+        print(f"Error fetching recommendations for {food_item}: {e}")
+        return []
 
 app = FastAPI()
 df = pd.read_csv("indian_food.csv")
@@ -36,5 +83,26 @@ def recommend(food_item: list[str]):
 
     return {"recommended_food_items": [rec[0] for rec in sorted_recommendations]}
 
+@app.post("/v2/recommend")
+def recommendV2(food_items: list[str]):
+    if len(food_items) >= 3:
+        food_items = food_items[:3]
+    all_recommendations = []
+
+    for item in food_items:
+        recommendations = get_groq_recommendations(item)
+
+        try:
+            rec_list = eval(recommendations)
+            all_recommendations.extend(rec_list)
+        except:
+            continue
+
+    recommendation_counts = Counter(all_recommendations)
+    sorted_recommendations = sorted(recommendation_counts.items(), key=lambda x: x[1], reverse=True)
+
+    return {"recommended_food_items": [rec[0] for rec in sorted_recommendations]}
+
+
 if __name__ == "__main__":
     uvicorn.run(app, host="0.0.0.0", port=8000)

requirements.txt

@@ -7,3 +7,5 @@ sentence-transformers
 uvicorn
 seaborn
 matplotlib
+python-dotenv
+groq