Updated the app.py

app.py

@@ -1,187 +1,89 @@
+# 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)
-
-
-# import os
-
-# # Get the secret key from the environment
-# groq_key = os.environ.get('groq_api_key')
-
-# ## LLM used for RAG
-# from langchain_groq import ChatGroq
-
-# llm = ChatGroq(model="llama-3.1-70b-versatile",api_key=groq_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 nstore
-# 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?']
+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 = userdata.get('groq_api_key')
 
+#LLM Used for RAG
+from langchain_groq import ChatGroq
 
+llm = ChatGroq(model="llama-3.1-70b-versatile",api_key=groq_api_key)
 
-# title = "Real-time AI App with Groq API and LangChain to Answer Morroco Tourism questions"
-# demo = gr.Interface(
-#     title=title,
-#     fn=rag_memory_stream,
-#     inputs="text",
-#     outputs="text",
-#     examples=examples,
-#     allow_flagging="never",
-# )
+#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
 
-# if __name__ == "__main__":
-#     demo.launch()
+vectorstore = Chroma(
+    collection_name="tourism_dataset_store",
+    embedding_function=embed_model,
+    persist_directory="./",
+)
 
-import os
-import pandas as pd
-import gradio as gr
+# Add data to vector store
+vectorstore.add_texts(context_data)
 
-# Read JSON file
-df = pd.read_json("./tourisme_chatbot.json")
+retriever = vectorstore.as_retriever()
 
-context_data = []
-for i in range(len(df)):
-    context = ""
-    for j in range(4):
-        context += df.columns[j]
-        context += ": "
-        context += df.iloc[i, j]  # Using iloc to avoid the deprecation warning
-        context += " "
-    context_data.append(context)
-
-# Lazy initialization function
-llm = None
-embed_model = None
-vectorstore = None
-groq_key = os.environ.get('groq_api_key')
-
-def initialize_model():
-    global llm, embed_model, vectorstore
-    
-    # Only initialize the models and vector store when needed
-    if llm is None:
-        from langchain_groq import ChatGroq
-        llm = ChatGroq(model="llama-3.1-70b-versatile", api_key=groq_key)
-
-    if embed_model is None:
-        from langchain_huggingface import HuggingFaceEmbeddings
-        embed_model = HuggingFaceEmbeddings(model_name="mixedbread-ai/mxbai-embed-large-v1")
-
-    if vectorstore is None:
-        from langchain_chroma import Chroma
-        vectorstore = Chroma(
-            collection_name="tourism_dataset_store",
-            embedding_function=embed_model,
-            persist_directory="./",
-        )
-        vectorstore.add_texts(context_data)  # Adding the context data to the vector store
-
-# Initialize model before creating RAG chain
-initialize_model()
-
-# RAG Chain setup
 from langchain_core.prompts import PromptTemplate
-from langchain_core.output_parsers import StrOutputParser
-from langchain_core.runnables import RunnablePassthrough
 
-# Define prompt template
 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)
 
-# Create RAG chain
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnablePassthrough
+
 rag_chain = (
-    {"context": vectorstore.as_retriever(), "question": RunnablePassthrough()}
+    {"context": retriever, "question": RunnablePassthrough()}
     | rag_prompt
     | llm
     | StrOutputParser()
 )
 
-# Function for real-time stream of results
+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
 
-# Gradio Interface setup
-examples = ['Tourist attraction sites in Morocco', 'What are some fun activities to do in Morocco?', 'What can I do in Marrakech 40000 Morocco?']
+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"
+title = "Real-time AI App with Groq API and LangChain to Answer Moroccon Tourism questions"
 demo = gr.Interface(
     title=title,
     fn=rag_memory_stream,
     inputs="text",
     outputs="text",
-    examples=examples,
     allow_flagging="never",
 )
 
-if __name__ == "__main__":
-    demo.launch()
+if __name__ == '__main__':
+    demo.launch()