Update app.py

app.py

@@ -1,90 +1,90 @@
-from langchain_community.embeddings import OpenAIEmbeddings
-from langchain_community.vectorstores import Pinecone
-from langchain_text_splitters import CharacterTextSplitter
-from langchain_openai import OpenAIEmbeddings
-from langchain_community.document_loaders import HuggingFaceDatasetLoader
-from langchain_pinecone import PineconeVectorStore
-from pinecone import Pinecone, ServerlessSpec
-from langchain_pinecone import PineconeVectorStore
-from langchain_openai import ChatOpenAI
-from langchain_core.output_parsers import StrOutputParser
-from langchain import hub
-from langchain_core.runnables import RunnablePassthrough
-import os
-import gradio as gr
-
-from dotenv import load_dotenv
-load_dotenv()
-
-
-dataset_name = "Pijush2023/Yale_Psychilogy"
-page_content_column = 'Biography'
-loader = HuggingFaceDatasetLoader(dataset_name, page_content_column)
-data = loader.load()
-
-text_splitter = CharacterTextSplitter(chunk_size=300, chunk_overlap=50)
-documents = text_splitter.split_documents(data)
-
-
-embeddings=OpenAIEmbeddings(api_key=os.environ['OPENAI_API_KEY'])
-# Instantiate chat model
-chat_model= ChatOpenAI(api_key=os.environ['OPENAI_API_KEY'], temperature=0.5, model='gpt-3.5-turbo-0125')
-
-# pip install pinecone-client
-pc = Pinecone(api_key=os.environ['PINECONE_API_KEY'])
-
-index_name = "medical"
-
-if index_name not in pc.list_indexes().names():
-  pc.create_index(
-      name=index_name,
-      dimension=1536,
-      metric='cosine',
-      spec=ServerlessSpec(
-          cloud='aws',
-          region='us-east-1'
-      )
-  )
-
-vectorstore = PineconeVectorStore(index_name=index_name, embedding=embeddings)
-vectorstore.add_documents(documents)
-
-query = "who is the best doctor for depression?"
-vectorstore.similarity_search(query,k=1)
-
-retriever = vectorstore.as_retriever(search_kwargs={'k':1})
-docs = retriever.invoke("who is the best doctors for depression ?")
-
-prompt=hub.pull("rlm/rag-prompt")
-
-rag_chain=(
-    {"context":retriever , "question" : RunnablePassthrough()}
-    | prompt
-    | chat_model
-    | StrOutputParser()
-)
-
-query="depression"
-rag_chain.invoke(query)
-
-def generate_answer(message, history):
-    return rag_chain.invoke(message)
-
-# Set up chat bot interface
-answer_bot = gr.ChatInterface(
-                            generate_answer,
-                            chatbot=gr.Chatbot(height=300),
-                            textbox=gr.Textbox(placeholder="Ask me a question about Doctor on Psychiatry", container=False, scale=7),
-                            title="Psychiatry Doctor Chat-Bot",
-                            description="This is a chat bot related to top School in United States about Psychiatry",
-                            theme="soft",
-                            examples=["depression", "Mental-Stress", "Bipolar Disorder", "Eating Disorders" , "etc....."],
-                            cache_examples=False,
-                            retry_btn=None,
-                            undo_btn=None,
-                            clear_btn=None,
-                            submit_btn="Ask"
-                        )
-
-answer_bot.launch()
-
+from langchain_community.embeddings import OpenAIEmbeddings
+from langchain_community.vectorstores import Pinecone
+from langchain_text_splitters import CharacterTextSplitter
+from langchain_openai import OpenAIEmbeddings
+from langchain_community.document_loaders import HuggingFaceDatasetLoader
+from langchain_pinecone import PineconeVectorStore
+from pinecone import Pinecone, ServerlessSpec
+from langchain_pinecone import PineconeVectorStore
+from langchain_openai import ChatOpenAI
+from langchain_core.output_parsers import StrOutputParser
+from langchain import hub
+from langchain_core.runnables import RunnablePassthrough
+import os
+import gradio as gr
+
+from dotenv import load_dotenv
+load_dotenv()
+
+
+dataset_name = "Pijush2023/Yale_Psychilogy"
+page_content_column = 'Biography'
+loader = HuggingFaceDatasetLoader(dataset_name, page_content_column)
+data = loader.load()
+
+text_splitter = CharacterTextSplitter(chunk_size=300, chunk_overlap=50)
+documents = text_splitter.split_documents(data)
+
+
+embeddings=OpenAIEmbeddings(api_key=os.environ['OPENAI_API_KEY'])
+# Instantiate chat model
+chat_model= ChatOpenAI(api_key=os.environ['OPENAI_API_KEY'], temperature=0.5, model='gpt-3.5-turbo-0125')
+
+# pip install pinecone-client
+pc = Pinecone(api_key=os.environ['PINECONE_API_KEY'])
+
+index_name = "medical"
+
+if index_name not in pc.list_indexes().names():
+  pc.create_index(
+      name=index_name,
+      dimension=1536,
+      metric='cosine',
+      spec=ServerlessSpec(
+          cloud='aws',
+          region='us-east-1'
+      )
+  )
+
+vectorstore = PineconeVectorStore(index_name=index_name, embedding=embeddings)
+vectorstore.add_documents(documents)
+
+query = "who is the best doctor for depression?"
+vectorstore.similarity_search(query,k=1)
+
+retriever = vectorstore.as_retriever(search_kwargs={'k':1})
+docs = retriever.invoke("who is the best doctors for depression ?")
+
+prompt=hub.pull("rlm/rag-prompt")
+
+rag_chain=(
+    {"context":retriever , "question" : RunnablePassthrough()}
+    | prompt
+    | chat_model
+    | StrOutputParser()
+)
+
+query="depression"
+rag_chain.invoke(query)
+
+def generate_answer(message, history):
+    return rag_chain.invoke(message)
+
+# Set up chat bot interface
+answer_bot = gr.ChatInterface(
+                            generate_answer,
+                            chatbot=gr.Chatbot(height=300),
+                            textbox=gr.Textbox(placeholder="Ask me a question about Doctor on Psychiatry", container=False, scale=7),
+                            title="Psychiatry Doctor Chat-Bot",
+                            description="This is a chat bot related to top School in United States about Psychiatry",
+                            theme="soft",
+                            examples=["depression", "Mental-Stress", "Bipolar Disorder", "Eating Disorders" , "etc....."],
+                            cache_examples=False,
+                            retry_btn=None,
+                            undo_btn=None,
+                            clear_btn=None,
+                            submit_btn="Ask"
+                        )
+
+answer_bot.launch(share=True)
+