Delete app.py

app.py

@@ -1,138 +0,0 @@
-import streamlit as st
-from langchain_pinecone import PineconeVectorStore
-from langchain_openai import OpenAI, ChatOpenAI
-from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain.prompts import PromptTemplate
-from langchain_huggingface import HuggingFaceEmbeddings
-from langchain.output_parsers import PydanticOutputParser
-from dotenv import load_dotenv
-import os
-from pydantic import BaseModel, Field
-from typing import List, Union
-from typing_extensions import Literal
-
-
-# Pydantic Schema
-class Question(BaseModel):
-    question: str = Field(..., description="The question prompt that the user needs to answer.")
-    type: Literal['fill_missing', 'MCQ', 'short_answer'] = Field(..., description="The type of question: fill_missing, MCQ, or short_answer.")
-    options: Union[List[str], None] = Field(None, description="The options for the question, used only for MCQ type.")
-
-class Questions(BaseModel):
-    no_of_questions: int = Field(..., description="The total number of questions generated.")
-    questions: List[Question] = Field(..., description="A list of Question objects.")
-
-
-# Function to download Hugging Face embeddings
-def download_hugging_face_embeddings():
-    embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')
-    return embeddings
-
-
-# Load environment variables
-def load_env_variables():
-    load_dotenv()
-    PINECONE_API_KEY = os.environ.get('PINECONE_API_KEY')
-    OPENAI_API_KEY = os.environ.get('OPENAI_API_KEY')
-    GOOGLE_API_KEY = os.environ.get("GOOGLE_API_KEY")
-
-    os.environ["PINECONE_API_KEY"] = PINECONE_API_KEY
-    os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
-    os.environ["GOOGLE_API_KEY"] = GOOGLE_API_KEY
-
-
-# Function to initialize Pinecone vector store and retriever
-def initialize_vector_store(embeddings):
-    index_name = "yolotest"
-    vector_store = PineconeVectorStore.from_existing_index(index_name=index_name, embedding=embeddings)
-    retriever = vector_store.as_retriever(search_type="similarity", search_kwargs={"k": 5})
-    return retriever
-
-
-# Function to initialize LLM
-def initialize_llm():
-    llm = ChatOpenAI(api_key=os.environ.get("OPENAI_API_KEY"), temperature=0, model='gpt-3.5-turbo-0125')
-    llm_structured = llm.with_structured_output(Questions)
-    return llm_structured
-
-
-# Function to initialize prompt template
-def initialize_prompt_template(parser):
-    prompt_template = """
-    You are given some context below. Based on the context, generate questions with one of the following types: ['fill_missing', 'MCQ', 'short_answer'].
-
-    Context: {context}
-
-    Make sure to generate a variety of questions. The types should be distributed among 'fill_missing', 'MCQ', and 'short_answer'. 
-
-    For MCQs, provide at least 3 options. For 'fill_missing', make sure to leave a gap that can be filled. For 'short_answer', make sure the answer is clear from the context.
-
-    Return a list of questions, with each question having the following structure:
-    - 'question': The question prompt.
-    - 'type': The type of question: 'fill_missing', 'MCQ', or 'short_answer'.
-    - 'options': For 'MCQ', a list of options. For other types, this field should be omitted.
-
-    The response should be with a format that matches the following structure:
-    {format_instructions}
-    """
-    return prompt_template
-
-
-# Initialize components before user query
-embeddings = download_hugging_face_embeddings()
-load_env_variables()
-retriever = initialize_vector_store(embeddings)
-llm_structured = initialize_llm()
-parser = PydanticOutputParser(pydantic_object=Questions)
-prompt_template = initialize_prompt_template(parser)
-
-
-# Function to generate questions from the context
-def generate_questions_from_context(query: str, retriever, llm_structured, prompt_template, parser):
-    # Retrieve relevant documents from the vector store using the retriever
-    retrieved_docs = retriever.invoke(query)
-    retrieved_docs = [rec.page_content for rec in retrieved_docs]
-
-    # Combine the retrieved documents into a single context string
-    context = " ".join([doc for doc in retrieved_docs])
-
-    # Initialize prompt template
-    prompt = PromptTemplate(template=prompt_template, input_variables=["context"],
-                            partial_variables={"format_instructions": parser.get_format_instructions()})
-
-    # Create chain and generate response
-    chain = prompt | llm_structured
-    response = chain.invoke({
-        "context": context, "format_instructions": parser.get_format_instructions()
-    })
-    return response
-
-
-# Streamlit Interface
-def main():
-    st.title("A Simple RAG App to Generate Questions in Specific Formats")
-
-    # Display user query input
-    query = st.chat_input("Say something: ")
-    if query:
-        with st.spinner('Generating questions...'):
-            st.write(f"Your query: {query}")  # Display user query
-            
-            # Generate questions
-            result = generate_questions_from_context(query, retriever, llm_structured, prompt_template, parser)
-            st.write(result)
-            # # Display the result in a more readable format
-            # st.subheader("Generated Questions")
-            # for i, question in enumerate(result.questions):
-            #     st.markdown(f"### Question {i + 1}: {question.question}")
-            #     st.markdown(f"**Type**: {question.type}")
-            #     if question.type == "MCQ" and question.options:
-            #         st.markdown("**Options**:")
-            #         for option in question.options:
-            #             st.markdown(f"- {option}")
-            #     st.markdown("---")
-
-
-# Run the app
-if __name__ == "__main__":
-    main()