Delete summarizer.py

summarizer.py

@@ -1,100 +0,0 @@
-import streamlit as st
-from PyPDF2 import PdfReader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_groq import ChatGroq
-from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings
-from langchain.vectorstores import FAISS
-from langchain.chains.question_answering import load_qa_chain
-from langchain.prompts import PromptTemplate
-import tempfile
-from gtts import gTTS
-import os
-
-def text_to_speech(text):
-    tts = gTTS(text=text, lang='en')
-    audio_file = tempfile.NamedTemporaryFile(suffix=".mp3", delete=False)
-    temp_filename = audio_file.name
-    tts.save(temp_filename)
-    st.audio(temp_filename, format='audio/mp3')
-    os.remove(temp_filename)
-
-def get_pdf_text(pdf_docs):
-    text=""
-    for pdf in pdf_docs:
-        pdf_reader= PdfReader(pdf)
-        for page in pdf_reader.pages:
-            text+= page.extract_text()
-    return  text
-
-def get_text_chunks(text):
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
-    chunks = text_splitter.split_text(text)
-    return chunks
-    
-def get_vector_store(text_chunks, api_key):
-    embeddings = HuggingFaceInferenceAPIEmbeddings(api_key=api_key, model_name="sentence-transformers/all-MiniLM-l6-v2")
-    vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
-    vector_store.save_local("faiss_index")
-
-def get_conversational_chain():
-
-    prompt_template = """
-    Answer the question as detailed as possible from the provided context, make sure to provide all the details, if the answer is not in
-    provided context just say, "answer is not available in the context", don't provide the wrong answer\n\n
-    Context:\n {context}?\n
-    Question: \n{question}\n
-    Answer:
-    """
-
-    model = ChatGroq(temperature=0, groq_api_key=os.environ["groq_api_key"], model_name="llama3-8b-8192")
-
-    prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
-    chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
-
-    return chain
-
-def user_input(user_question, api_key):
-    embeddings = HuggingFaceInferenceAPIEmbeddings(api_key=api_key, model_name="sentence-transformers/all-MiniLM-l6-v2")
-    
-    new_db = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
-    docs = new_db.similarity_search(user_question)
-
-    chain = get_conversational_chain()
-
-    response = chain(
-        {"input_documents":docs, "question": user_question}
-        , return_only_outputs=True)
-
-    print(response)  # Debugging line
-    
-    st.write("Replies:")
-    if isinstance(response["output_text"], str):
-        response_list = [response["output_text"]]
-    else:
-        response_list = response["output_text"]
-    
-    for text in response_list:
-        st.write(text)
-        # Convert text to speech for each response
-        text_to_speech(text)
-
-def main():
-    
-    st.set_page_config(layout="centered")
-    st.header("Chat with DOCS")
-    st.markdown("<h1 style='font-size:20px;'>ChatBot by Muhammad Huzaifa</h1>", unsafe_allow_html=True)
-    api_key = st.secrets["inference_api_key"]
-    
-    
-    with st.sidebar:
-        st.header("Chat with PDF")
-        # st.title("Menu:")
-        if st.button("Chat With PDF"):
-            st.switch_page('/app.py')
-
-    # Check if any document is uploaded
-    if pdf_docs:
-        user_question = st.text_input("Ask a question from the Docs")
-        if user_question:
-            user_input(user_question, api_key)
-    else: