Update app.py

app.py

@@ -1,92 +1,3 @@
-# # import warnings
-# # warnings.filterwarnings('ignore')
-
-# import pandas as pd
-# df = pd.read_json("./tourisme_chatbot.json")
-
-# context_data = []
-# for i in range(len(df)):
-#   context = ""
-#   for j in range(4):
-#     context += df.columns[j]
-#     context += ": "
-#     context += df.iloc[i][j]
-#     context += " "
-#   context_data.append(context)
-
-# # Get the secret key from the environment
-# import os
-# groq_api_key = os.environ.get('groq_api_keys')
-
-# #LLM Used for RAG
-# from langchain_groq import ChatGroq
-
-# llm = ChatGroq(model="llama-3.1-70b-versatile",api_key=groq_api_key)
-
-# #Embedding model
-# from langchain_huggingface import HuggingFaceEmbeddings
-# embed_model = HuggingFaceEmbeddings(model_name="mixedbread-ai/mxbai-embed-large-v1")
-
-# # create vector store!
-# from langchain_chroma import Chroma
-
-# vectorstore = Chroma(
-#     collection_name="tourism_dataset_store",
-#     embedding_function=embed_model,
-#     persist_directory="./",
-# )
-
-# # Add data to vector store
-# vectorstore.add_texts(context_data)
-
-# retriever = vectorstore.as_retriever()
-
-# from langchain_core.prompts import PromptTemplate
-
-# template = ("""You are a Moroccan tourism expert.
-#     Use the provided context to answer the question.
-#     If you don't know the answer, say so. Explain your answer in detail.
-#     Do not discuss the context in your response; just provide the answer directly.
-
-#     Context: {context}
-
-#     Question: {question}
-
-#     Answer:""")
-
-# rag_prompt = PromptTemplate.from_template(template)
-
-# from langchain_core.output_parsers import StrOutputParser
-# from langchain_core.runnables import RunnablePassthrough
-
-# rag_chain = (
-#     {"context": retriever, "question": RunnablePassthrough()}
-#     | rag_prompt
-#     | llm
-#     | StrOutputParser()
-# )
-
-# import gradio as gr
-
-# def rag_memory_stream(text):
-#     partial_text = ""
-#     for new_text in rag_chain.stream(text):
-#         partial_text += new_text
-#         yield partial_text
-
-# examples = ['Tourist attraction sites in Morocco', 'What are some fun activities to do in Morocco?']
-
-# title = "Real-time AI App with Groq API and LangChain to Answer Morocco Tourism questions"
-# demo = gr.Interface(
-#     title=title,
-#     fn=rag_memory_stream,
-#     inputs="text",
-#     outputs="text",
-#     allow_flagging="never",
-# )
-
-# if __name__ == '__main__':
-#     demo.launch()
 import pandas as pd
 import os
 import gradio as gr
@@ -170,7 +81,7 @@ demo = gr.Interface(
     fn=rag_memory_stream,
     inputs="text",
     outputs="text",
-    examples = "examples",
+    examples = examples,
     allow_flagging="never",
 )