Update app.py

app.py

@@ -18,4 +18,138 @@ from langchain.schema.output_parser import StrOutputParser
 from langchain_core.messages import HumanMessage, SystemMessage
 
 df = pd.read_csv('./RAW_recipes.csv')
-print(df.head())
+
+# Variables
+max_length = 231637 #total number of recipes aka rows
+curr_len = 10000 # how much we want to process and embed
+
+#Concatenate all rows into one string
+curr_i = 0
+recipe_info = []
+for index, row in df.iterrows():
+    if curr_i >= curr_len:
+        break
+    curr_i+=1
+    name, id, minutes, contributor_id, submitted, tags, nutrition, n_steps, steps, description, ingredients, n_ingredients = row
+
+    #convert to list
+    nutrition = ast.literal_eval(nutrition)
+    steps = ast.literal_eval(steps)
+    
+    #format nutrition
+    nutrition_map = ["Calorie"," Total Fat", 'Sugar', 'Sodium', 'Protein', 'Saturated Fat', 'Total Carbohydrate']
+    nutrition_labeled = []
+    for label, num in zip(nutrition_map, nutrition):
+        nutrition_labeled.append(f"{label} : {num} % daily value")
+
+    #format steps
+    for i in range(len(steps)):
+        steps[i] = f"{i+1}. " + steps[i]
+    recipe_info.append(f'''
+    {name} : {minutes} minutes, submitted on {submitted} 
+    description: {description},
+    ingredients: {ingredients}
+    number of ingredients: {n_ingredients}
+    tags: {tags}, nutrition: {nutrition_labeled}, total steps: {n_steps}
+    steps: {steps}
+    '''.replace("\r", "").replace("\n", ""))
+
+
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=150)
+
+#split into recipe_info into chunks
+docs = []
+for doc in recipe_info:
+    # Wrap each string in a Document object
+    document = Document(page_content=doc)  # create a Document object with the content
+    chunk = text_splitter.split_documents([document])  # Pass a list of Document objects
+    docs.append(chunk)
+
+# merge all chunks into one 
+merged_documents = []
+for doc in docs:
+  merged_documents.extend(doc)
+
+# Hugging Face model for embeddings.
+model_name = "sentence-transformers/all-MiniLM-L6-v2"
+model_kwargs = {'device': 'cpu'}
+embeddings = HuggingFaceEmbeddings(
+    model_name=model_name,
+    model_kwargs=model_kwargs,
+)
+
+#initialize weaviate client
+client = weaviate.Client(
+  embedded_options = EmbeddedOptions()
+)
+
+
+vector_search = Weaviate.from_documents(
+    client = client,
+    documents = merged_documents,
+    embedding = embeddings,
+    by_text = False
+)
+
+
+# Instantiate Weaviate Vector Search as a retriever
+
+# Basic RAG.
+# k to search for only the 25 most relevant documents.
+# score_threshold to use only documents with a relevance score above 0.77.
+k = 10
+score_threshold = 0.77
+
+retriever = vector_search.as_retriever(
+   search_type = "mmr", 
+   search_kwargs = {
+      "k": k,
+      "score_threshold": score_threshold
+   }
+)
+
+template = """
+You are an assistant for question-answering tasks.
+Use the following pieces of retrieved context to answer the question at the end.
+The following pieces of retrieved context are recipes.
+If you don't know the answer, just say that you don't know. Don't try to make up an answer.
+Dont say anthing mean or offensive.
+
+Context: {context}
+
+Question: {question}
+"""
+
+custom_rag_prompt = ChatPromptTemplate.from_template(template)
+
+llm = ChatOpenAI(
+    model_name="gpt-3.5-turbo",
+    temperature=0.2)
+
+# Regular chain format: chain = prompt | model | output_parser
+rag_chain = (
+    {"context": retriever,  "question": RunnablePassthrough()}
+    | custom_rag_prompt
+    | llm
+    | StrOutputParser()
+)
+
+
+def get_response(query):
+  return rag_chain.invoke(query)
+
+
+with gr.Blocks(theme=Base(), title="RAG Recipe AI") as demo:
+    gr.Markdown("RAG Recipe AI")
+    textbox = gr.Textbox(label="Question:")
+    with gr.Row():
+        button = gr.Button("Submit", variant="primary")
+    with gr.Column():
+        output1 = gr.Textbox(lines=1, max_lines=10, label="Answer:")
+    # Call get_response function upon clicking the Submit button.
+    button.click(get_response, textbox, outputs=[output1])
+
+demo.launch()
+
+
+