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app.py

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+import gradio as gr
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_openai import ChatOpenAI
+from langchain.chains import RetrievalQA
+from langchain.vectorstores import FAISS
+
+
+def recipe_generator(ingredients):
+  """
+  This function takes a list of ingredients as input and returns a recipe using the LangChain library and OpenAI API.
+  """
+  # Load the LangChain model and vector store
+  embedd = HuggingFaceEmbeddings(model_name="intfloat/multilingual-e5-large")
+  vector_store = FAISS.load_local("faiss_index", embedd,allow_dangerous_deserialization=True)
+
+  # Create the LangChain chain
+  llm = ChatOpenAI(
+      openai_api_key="sk-proj-JjAAcDuAsxPkm3zg9Iz2T3BlbkFJ5txMWIx2TS6T24rPYhjN",
+      model="gpt-3.5-turbo",
+      temperature=0.3
+  )
+  qa_chain = RetrievalQA.from_chain_type(
+      llm,
+      retriever=vector_store.as_retriever(),
+      return_source_documents=True
+  )
+
+  # Generate the recipe
+  question = f"Make a recipe using the following ingredients: {ingredients}"
+  result = qa_chain({"query": question})
+  return result["result"]
+
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=recipe_generator,
+    inputs=[
+        gr.Textbox(
+            show_label=False,
+            placeholder="Enter your ingredients separated by commas"
+        ),
+    ],
+    outputs=[
+        "textbox",
+    ],
+    examples=[
+        ["chicken, rice, vegetables"],
+        ["pasta, tomato sauce, cheese"],
+    ],
+    title="Recipe Generator",
+    description="Enter a list of ingredients and I will generate a recipe for you."
+)
+
+# Launch the Gradio app
+interface.launch()

requirements.txt

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+faiss-cpu 
+langchain-community
+sentence-transformers
+tiktoken
+openai
+langchain-openai
+langchain_community
+langchain_openai
+langchain