✅ Added CI/CD and Dockerfile for deployment

.github/workflows/deploy.yml

@@ -1,5 +1,4 @@
-# .github/workflows/deploy.yml
-name: CI/CD Pipeline
+name: build-and-deploy
 
 on:
   push:
@@ -11,29 +10,28 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-      - name: Checkout code
+      - name: Checkout repo
         uses: actions/checkout@v3
 
       - name: Set up Python
         uses: actions/setup-python@v4
         with:
-          python-version: "3.10"
+          python-version: '3.10'
 
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
           pip install -r requirements.txt
+          pip install pytest
 
-      - name: Run tests
-        run: |
-          echo "✅ Add Pytest or other checks here"
-
-      - name: Build Docker image
-        run: docker build -t llm-campaign-app .
+      - name: Run unit tests
+        run: pytest tests/
 
-      # Optional: Push to DockerHub/GHCR (needs secrets configured)
-      # - name: Push Docker Image
-      #   run: |
-      #     echo "${{ secrets.DOCKERHUB_PASSWORD }}" | docker login -u "${{ secrets.DOCKERHUB_USERNAME }}" --password-stdin
-      #     docker tag llm-campaign-app your-dockerhub-user/llm-campaign-app
-      #     docker push your-dockerhub-user/llm-campaign-app
+      - name: Deploy to Hugging Face Spaces (example)
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        run: |
+          git config --global user.email "[email protected]"
+          git config --global user.name "Your Name"
+          git remote set-url origin https://huggingface.co/spaces/YOUR_USERNAME/YOUR_SPACE_NAME
+          git push origin main

app.py

@@ -22,7 +22,7 @@ def read_root():
 def generate_campaign(data: ProductRequest):
     try:
         predictor = LLMPredictionPipeline()
-        result = predictor.predict(data.dict())
+        result = predictor.predict(data.model_dump())
 
 
         return {"campaign": result}

requirements.txt

@@ -14,3 +14,6 @@ chromadb
 sentence-transformers
 langchain
 langchain-community
+pytest
+httpx
+ollama

src/marketingCampaignGen/components/model/generator.py

@@ -1,15 +1,18 @@
 # src/marketingCampaignGen/utils/campaign_generator.py
 
 
-from langchain.llms import Ollama
+# from langchain.llms import Ollama
+from langchain_community.llms import Ollama
 from langchain.prompts import PromptTemplate
 from langchain.chains import LLMChain
 from src.marketingCampaignGen.utils.vector_store import get_similar_examples
 
 
 class CampaignGenerator:
-    def __init__(self, model_name="mistral"):
-        self.llm = Ollama(model=model_name)
+    def __init__(self, model_name="orca-mini"):
+        # self.llm = Ollama(model=model_name)
+        self.llm = Ollama(model="mistral:7b-instruct-q4_0")
+
 
         self.template = PromptTemplate(
             input_variables=["product_name", "features", "brand", "audience", "tone", "goal", "examples"],
@@ -41,16 +44,16 @@ Write the campaign in the specified tone and make it persuasive.
         flat_examples = [doc for sublist in examples["documents"] for doc in sublist]
         example_text = "\n\n".join(flat_examples) if flat_examples else "None"
 
-        response = self.chain.run(
-            product_name=input_data["product_name"],
-            features=feature_query,
-            brand=input_data["brand"],
-            audience=input_data["audience"],
-            tone=input_data["tone"],
-            goal=input_data["goal"],
-            examples=example_text
-        )
-
-        return response
-
+        # Now pass ALL required fields to the template using `.invoke()` (not `.run()`)
+        response = self.chain.invoke({
+            "product_name": input_data["product_name"],
+            "features": feature_query,
+            "brand": input_data["brand"],
+            "audience": input_data["audience"],
+            "tone": input_data["tone"],
+            "goal": input_data["goal"],
+            "examples": example_text
+        })
+
+        return response["text"]
 

src/marketingCampaignGen/utils/vector_store.py

@@ -1,7 +1,7 @@
 # src/marketingCampaignGen/utils/vector_store.py
 
 import chromadb
-from sentence_transformers import SentenceTransformer
+# from sentence_transformers import SentenceTransformer
 from chromadb.utils import embedding_functions
 
 CHROMA_PATH = "chroma_db"

tests/test_generate.py

@@ -0,0 +1,36 @@
+import pytest
+from fastapi.testclient import TestClient
+import sys
+import os
+
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
+from app import app
+
+
+#  tests/test_generate.py
+
+client = TestClient(app)
+
+sample_payload = {
+    "product_name": "EcoBottle",
+    "features": ["BPA-free", "Insulated", "Leak-proof"],
+    "brand": "EcoFlow",
+    "audience": "Eco-conscious travelers",
+    "tone": "enthusiastic",
+    "goal": "Drive conversions for the summer collection",
+}
+
+def test_generate_campaign():
+    response = client.post("/generate", json=sample_payload)
+
+    print("\n[DEBUG] Status Code:", response.status_code)
+    try:
+        print("[DEBUG] Response JSON:", response.json())
+    except Exception as e:
+        print("[DEBUG] Response could not be parsed:", e)
+        print("[DEBUG] Raw Response Text:", response.text)
+
+    assert response.status_code == 200
+    assert "campaign" in response.json()
+    assert isinstance(response.json()["campaign"], str)
+    assert len(response.json()["campaign"]) > 10