Rename meal_loader.py to chatbot_logic.py

chatbot_logic.py

@@ -0,0 +1,36 @@
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import Chroma
+from langchain.llms import HuggingFaceHub
+from langchain.chains import RetrievalQA
+from knowledge_base import load_vectorstore
+import os
+
+# Load vectorstore with meal plan embeddings
+db = load_vectorstore()
+retriever = db.as_retriever()
+
+# Load LLM (HuggingFace Inference API)
+llm = HuggingFaceHub(
+    repo_id="mistralai/Mistral-7B-Instruct-v0.1",
+    model_kwargs={"temperature": 0.3, "max_new_tokens": 512}
+)
+
+# Build Retrieval QA Chain
+qa_chain = RetrievalQA.from_chain_type(
+    llm=llm,
+    retriever=retriever,
+    chain_type="stuff"
+)
+
+def get_bot_response(query):
+    """
+    Accepts a user query, runs through RAG chain, and returns a response.
+    """
+    if not query:
+        return "Please ask something."
+
+    try:
+        result = qa_chain.run(query)
+        return result
+    except Exception as e:
+        return f"Error processing query: {str(e)}"

meal_loader.py

@@ -1,11 +0,0 @@
-import os
-from langchain_community.document_loaders import PyMuPDFLoader
-
-documents = []
-meal_dir = "meal_plans"
-
-for filename in os.listdir(meal_dir):
-    if filename.endswith(".pdf"):
-        path = os.path.join(meal_dir, filename)
-        loader = PyMuPDFLoader(path)
-        documents.extend(loader.load())