Update app.py

app.py

@@ -1,92 +1,99 @@
-import pandas as pd
+import gradio as gr
 
+def greet(name):
+    return f"Hello {name}!"
 
-df = pd.read_json("./tourisme_chatbot.json")
+iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+iface.launch()
+# import pandas as pd
 
-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)
 
+# df = pd.read_json("./tourisme_chatbot.json")
 
-import os
+# 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
-groq_key = os.environ.get('groq_api_key')
 
-## LLM used for RAG
-from langchain_groq import ChatGroq
+# import os
 
-llm = ChatGroq(model="llama-3.1-70b-versatile",api_key=groq_key)
+# # Get the secret key from the environment
+# groq_key = os.environ.get('groq_api_key')
 
-## Embedding model!
-from langchain_huggingface import HuggingFaceEmbeddings
-embed_model = HuggingFaceEmbeddings(model_name="mixedbread-ai/mxbai-embed-large-v1")
+# ## LLM used for RAG
+# from langchain_groq import ChatGroq
 
-# create vector store!
-from langchain_chroma import Chroma
+# llm = ChatGroq(model="llama-3.1-70b-versatile",api_key=groq_key)
 
-vectorstore = Chroma(
-    collection_name="tourism_dataset_store",
-    embedding_function=embed_model,
-    persist_directory="./",
-)
+# ## Embedding model!
+# from langchain_huggingface import HuggingFaceEmbeddings
+# embed_model = HuggingFaceEmbeddings(model_name="mixedbread-ai/mxbai-embed-large-v1")
 
-# add data to vector nstore
-vectorstore.add_texts(context_data)
+# # create vector store!
+# from langchain_chroma import Chroma
 
-retriever = vectorstore.as_retriever()
+# vectorstore = Chroma(
+#     collection_name="tourism_dataset_store",
+#     embedding_function=embed_model,
+#     persist_directory="./",
+# )
 
-from langchain_core.prompts import PromptTemplate
+# # add data to vector nstore
+# vectorstore.add_texts(context_data)
 
-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:""")
+# retriever = vectorstore.as_retriever()
 
-rag_prompt = PromptTemplate.from_template(template)
+# from langchain_core.prompts import PromptTemplate
 
-from langchain_core.output_parsers import StrOutputParser
-from langchain_core.runnables import RunnablePassthrough
+# 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_chain = (
-    {"context": retriever, "question": RunnablePassthrough()}
-    | rag_prompt
-    | llm
-    | StrOutputParser()
-)
+# rag_prompt = PromptTemplate.from_template(template)
 
-import gradio as gr
+# 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
+# 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()