Update app.py

app.py

@@ -1,11 +1,88 @@
 import gradio as gr
 
+
+import os
+hftoken = os.environ["hftoken"]
+
+from langchain_huggingface import HuggingFaceEndpoint
+
+repo_id = "mistralai/Mistral-7B-Instruct-v0.3"
+llm = HuggingFaceEndpoint(repo_id = repo_id, max_new_tokens = 128, temperature = 0.7, huggingfacehub_api_token = hftoken)
+
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+
+prompt = ChatPromptTemplate.from_template("tell me a joke about {topic}")
+chain = prompt | llm | StrOutputParser()
+
+# from langchain.document_loaders.csv_loader import CSVLoader
+from langchain_community.document_loaders.csv_loader import CSVLoader
+
+
+loader = CSVLoader(file_path='aiotsmartlabs_faq.csv', source_column = 'prompt')
+data = loader.load()
+
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_chroma import Chroma
+from langchain_huggingface.embeddings import HuggingFaceEndpointEmbeddings
+
+# CHECK MTEB LEADERBOARD & FIND BEST EMBEDDING MODEL
+model = "BAAI/bge-m3"
+embeddings = HuggingFaceEndpointEmbeddings(model = model)
+
+vectorstore = Chroma.from_documents(documents = data, embedding = embeddings)
+retriever = vectorstore.as_retriever()
+
+# from langchain.prompts import PromptTemplate
+
+from langchain_core.prompts import ChatPromptTemplate
+
+prompt = ChatPromptTemplate.from_template("""Given the following history, context and a question, generate an answer based on the context only.
+
+In the answer try to provide as much text as possible from "response" section in the source document context without making much changes.
+If somebody asks "Who are you?" or a similar phrase, state "I am Rishi's assistant built using a Large Language Model!"
+If the answer is not found in the context, kindly state "I don't know. Please ask Rishi on Discord. Discord Invite Link: https://discord.gg/6ezpZGeCcM. Or email at rishi@aiotsmartlabs.com" Don't try to make up an answer.
+
+CONTEXT: {context}
+
+HISTORY: {history}
+
+QUESTION: {question}""")
+
+from langchain_core.runnables import RunnablePassthrough
+
+# Define the chat response function
 def chatresponse(message, history):
-    return history
+    history_langchain_format = []
+    for human, ai in history:
+        history_langchain_format.append(HumanMessage(content=human))
+        history_langchain_format.append(AIMessage(content=ai))
+    history_langchain_format.append(HumanMessage(content=message))
+    gpt_response = llm(history_langchain_format)
+
+    rag_chain = (
+    {"context": retriever, "history": history_langchain_format, "question": RunnablePassthrough()}
+    | prompt
+    | llm
+    | StrOutputParser()
+    )
+
+    
+    output = rag_chain.invoke(message)
+    response = output.split('ANSWER: ')[-1].strip()
+    return response
 
 # Launch the Gradio chat interface
 gr.ChatInterface(chatresponse).launch()
 
+# import gradio as gr
+
+# def chatresponse(message, history):
+#     return history
+
+# # Launch the Gradio chat interface
+# gr.ChatInterface(chatresponse).launch()
+
 # import gradio as gr
 # from huggingface_hub import InferenceClient