✅ fixed genratore ollama CI/CD and Dockerfile for deployment in hugging face

src/marketingCampaignGen/components/model/generator.py

@@ -1,8 +1,3 @@
-# src/marketingCampaignGen/utils/campaign_generator.py
-
-
-# from langchain.llms import Ollama
-from unittest.mock import patch
 from langchain_community.llms import Ollama
 from langchain.prompts import PromptTemplate
 from langchain.chains import LLMChain
@@ -11,10 +6,8 @@ from src.marketingCampaignGen.utils.vector_store import get_similar_examples
 
 class CampaignGenerator:
     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"],
             template="""
@@ -37,41 +30,20 @@ Write the campaign in the specified tone and make it persuasive.
 
         self.chain = LLMChain(llm=self.llm, prompt=self.template)
 
-    # def generate(self, input_data: dict) -> str:
-    #     feature_query = ", ".join(input_data["features"])
-    #     examples = get_similar_examples(feature_query, top_k=2)
-
-    #     # Flatten list of documents
-    #     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.invoke(...).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
-    # Adjust path below to the exact module & class where .invoke() is called
-    @patch("src.marketingCampaignGen.components.model.generator.Ollama.invoke")
-    def test_generate_campaign(mock_invoke):
-        mock_invoke.return_value = "Mocked marketing 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
-
+    def generate(self, input_data: dict) -> str:
+        feature_query = ", ".join(input_data["features"])
+        examples = get_similar_examples(feature_query, top_k=2)
+
+        # Flatten list of documents
+        flat_examples = [doc for sublist in examples["documents"] for doc in sublist]
+        example_text = "\n\n".join(flat_examples) if flat_examples else "None"
+
+        return 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
+        )