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app.py

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+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="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", 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("Chat PDF")
+    st.header("Chat with PDF")
+
+    api_key = st.secrets["inference_api_key"]
+
+    user_question = st.text_input("Ask a Question from the PDF Files")
+
+    if user_question:
+        user_input(user_question, api_key)
+
+    with st.sidebar:
+        st.title("Menu:")
+        pdf_docs = st.file_uploader("Upload your PDF Files and Click on the Submit & Process Button", accept_multiple_files=True)
+        if st.button("Submit & Process"):
+            with st.spinner("Processing..."):
+                raw_text = get_pdf_text(pdf_docs)
+                text_chunks = get_text_chunks(raw_text)
+                get_vector_store(text_chunks, api_key)
+                st.success("Done")
+                
+if __name__ == "__main__":
+    main()

requirements.txt

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+requests
+streamlit
+faiss-cpu
+PyPDF2
+langchain
+langchain-groq
+langchain-community
+python-dotenv
+gtts