Update app.py

app.py

@@ -1,99 +1,93 @@
-import gradio as gr
-
-def greet(name):
-    return f"Hello {name}!"
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
-# import pandas as pd
+import pandas as pd
 
 
-# df = pd.read_json("./tourisme_chatbot.json")
+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)
+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
+import os
 
-# # Get the secret key from the environment
-# groq_key = os.environ.get('groq_api_key')
+# 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 used for RAG
+from langchain_groq import ChatGroq
 
-# llm = ChatGroq(model="llama-3.1-70b-versatile",api_key=groq_key)
+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")
+## 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
+# create vector store!
+from langchain_chroma import Chroma
 
-# vectorstore = Chroma(
-#     collection_name="tourism_dataset_store",
-#     embedding_function=embed_model,
-#     persist_directory="./",
-# )
+vectorstore = Chroma(
+    collection_name="tourism_dataset_store",
+    embedding_function=embed_model,
+    persist_directory="./",
+)
 
-# # add data to vector nstore
-# vectorstore.add_texts(context_data)
+# add data to vector nstore
+vectorstore.add_texts(context_data)
 
-# retriever = vectorstore.as_retriever()
+retriever = vectorstore.as_retriever()
 
-# from langchain_core.prompts import PromptTemplate
+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:""")
+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)
+rag_prompt = PromptTemplate.from_template(template)
 
-# from langchain_core.output_parsers import StrOutputParser
-# from langchain_core.runnables import RunnablePassthrough
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnablePassthrough
 
-# rag_chain = (
-#     {"context": retriever, "question": RunnablePassthrough()}
-#     | rag_prompt
-#     | llm
-#     | StrOutputParser()
-# )
+rag_chain = (
+    {"context": retriever, "question": RunnablePassthrough()}
+    | rag_prompt
+    | llm
+    | StrOutputParser()
+)
 
-# import gradio as gr
+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
+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?', 'What can I do in Marrakech 40000 Morocco?']
+examples = ['Tourist attraction sites in Morocco', 'What are some fun activities to do in Morocco?', 'What can I do in Marrakech 40000 Morocco?']
 
 
 
 
-# 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",
-# )
+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",
+)
 
 
-# if __name__ == "__main__":
-#     demo.launch()
+if __name__ == "__main__":
+    demo.launch()