Added bg_app.py and requirements.txt

bg_app.py

@@ -0,0 +1,103 @@
+#bg_app.py
+
+import gradio as gr
+from langchain.schema import HumanMessage, AIMessage
+from langchain.chains import create_retrieval_chain, create_history_aware_retriever
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain.vectorstores import Chroma
+from langchain.llms import HuggingFacePipeline
+from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, pipeline
+
+# Load the pre-existing vector store
+vector_store = Chroma(persist_directory="./bg_data_english")
+similarity_retriever = vector_store.as_retriever(
+    search_type="similarity_score_threshold", search_kwargs={"k": 5, "score_threshold": 0.2}
+)
+
+# Load the LLM
+quantization_config = BitsAndBytesConfig(load_in_8bit=True)
+tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b-it")
+llm_model = AutoModelForCausalLM.from_pretrained(
+    "google/gemma-2-9b-it",
+    quantization_config=quantization_config,
+)
+text_generation_pipeline = pipeline(
+    model=llm_model,
+    tokenizer=tokenizer,
+    task="text-generation",
+    return_full_text=False,
+    max_new_tokens=350,
+)
+llm = HuggingFacePipeline(pipeline=text_generation_pipeline)
+
+# Reformulating user queries with history context
+rephrase_system_prompt = """Given a chat history and the latest user question
+which might reference context in the chat history, formulate a standalone question
+which can be understood without the chat history. Do NOT answer the question,
+just reformulate it if needed and otherwise return it as is."""
+
+rephrase_prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", rephrase_system_prompt),
+        MessagesPlaceholder("chat_history"),
+        ("human", "{input}"),
+    ]
+)
+
+history_aware_retriever = create_history_aware_retriever(llm, similarity_retriever, rephrase_prompt)
+
+# Define the question-answering system prompt
+qa_system_prompt = """You are a saintly guide inspired by the teachings of the Bhagavad Gita, offering wisdom and moral guidance. Answer questions in a friendly and compassionate tone, drawing insights from the scripture to help users with their life challenges.  
+Use the provided context to craft your response and remain faithful to the philosophy of the Bhagavad Gita.  
+If you don't know the answer, humbly admit it or request the user to clarify or provide more details.  
+Limit your response to 5 lines unless the user explicitly asks for more explanation.  
+
+Question:  
+{input}  
+
+Context:  
+{context}  
+
+Answer:  
+"""
+
+qa_prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", qa_system_prompt),
+        MessagesPlaceholder("chat_history"),
+        ("human", "{input}"),
+    ]
+)
+
+question_answer_chain = create_stuff_documents_chain(llm, qa_prompt)
+qa_rag_chain = create_retrieval_chain(history_aware_retriever, question_answer_chain)
+
+# Function to generate answers
+chat_history = []
+
+def chat(question):
+    global chat_history
+    response = qa_rag_chain.invoke({"input": question, "chat_history": chat_history})
+    answer = response["answer"].strip()
+    if answer.startswith("Saintly Guide:"):
+        answer = answer[len("Saintly Guide:"):].strip()
+    elif answer.startswith("AI:"):
+        answer = answer[len("AI:"):].strip()
+    chat_history.extend([HumanMessage(content=question), AIMessage(content=response["answer"])])
+    return answer
+
+# Create Gradio interface
+interface = gr.Interface(
+    fn=chat,
+    inputs=gr.Textbox(label="Ask your question", placeholder="What's troubling you?"),
+    outputs=gr.Textbox(label="Answer"),
+    title="Bhagavad Gita Chatbot",
+    description="Ask questions inspired by the teachings of the Bhagavad Gita and receive saintly guidance."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    interface.launch()
+
+

requirements.txt

@@ -1 +1,15 @@
-huggingface_hub==0.25.2
+
+
+gradio
+transformers
+langchain>=0.0.227
+langchain-hub
+langchain-vectorstores
+sentence-transformers
+torch
+bitsandbytes
+accelerate
+chromadb
+huggingface-hub
+pydantic
+typing-extensions