Create app.py

app.py

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+import streamlit as st
+from dotenv import load_dotenv
+from langchain.chains import RetrievalQA
+from langchain.chains.question_answering import load_qa_chain
+from langchain_community.llms import HuggingFaceHub
+from langchain.document_loaders import AssemblyAIAudioTranscriptLoader
+from langchain.embeddings import HuggingFaceHubEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import FAISS
+from langchain.prompts import PromptTemplate
+from tempfile import NamedTemporaryFile
+
+# Load environment variables
+load_dotenv()
+
+# Function to create a prompt for retrieval QA chain
+def create_qa_prompt() -> PromptTemplate:
+    template = """\n\nHuman: Use the following pieces of context to answer the question at the end. If the answer is not clear, say I DON'T KNOW
+{context}
+Question: {question}
+\n\nAssistant:
+Answer:"""
+
+    return PromptTemplate(template=template, input_variables=["context", "question"])
+
+# Function to create documents from a list of URLs
+def create_docs(urls_list):
+    documents = []
+    for url in urls_list:
+        st.write(f'Transcribing {url}')
+        documents.append(AssemblyAIAudioTranscriptLoader(file_path=url).load()[0])
+    return documents
+
+# Function to create a Hugging Face embeddings model
+def make_embedder():
+    model_name = "sentence-transformers/all-mpnet-base-v2"
+    model_kwargs = {'device': 'cpu'}
+    encode_kwargs = {'normalize_embeddings': False}
+    return HuggingFaceHubEmbeddings(
+        repo_id=model_name,
+        task="feature-extraction"
+    )
+
+# Function to create a retrieval QA chain
+def make_qa_chain():
+    llm = HuggingFaceHub(
+        repo_id="HuggingFaceH4/zephyr-7b-beta",
+        model_kwargs={
+            "max_new_tokens": 512,
+            "top_k": 30,
+            "temperature": 0.01,
+            "repetition_penalty": 1.5,
+        },
+    )
+    return llm
+    # return RetrievalQA.from_chain_type(
+    #     llm,
+    #     retriever=db.as_retriever(search_type="mmr", search_kwargs={'fetch_k': 3}),
+    #     return_source_documents=True,
+    #     chain_type_kwargs={
+    #         "prompt": create_qa_prompt(),
+    #     }
+    # )
+
+# Streamlit UI
+def main():
+    st.set_page_config(page_title="Audio Query Chatbot", page_icon=":microphone:", layout="wide")
+
+    # Left pane - Audio file upload
+    col1, col2 = st.columns([1, 2])
+
+    with col1:
+        st.header("Upload Audio File")
+        uploaded_file = st.file_uploader("Choose a WAV or MP3 file", type=["wav", "mp3"], key="audio_uploader")
+
+        if uploaded_file is not None:
+            with NamedTemporaryFile(suffix='.mp3') as temp:
+                temp.write(uploaded_file.getvalue())
+                temp.seek(0)
+                docs = create_docs([temp.name])
+
+                # text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+                # texts = text_splitter.split_documents(docs)
+
+                # for text in texts:
+                    # text.metadata = {"audio_url": text.metadata["audio_url"]}
+
+                st.success('Audio file transcribed successfully!')
+
+                # hf = make_embedder()
+                # db = FAISS.from_documents(texts, hf)
+
+                # qa_chain = make_qa_chain(db)
+
+    # Right pane - Chatbot Interface
+    with col2:
+        st.header("Chatbot Interface")
+
+        if uploaded_file is not None:
+            with st.form(key="form"):
+                user_input = st.text_input("Ask your question", key="user_input")
+
+                # Automatically submit the form on Enter key press
+                st.markdown("<div><br></div>", unsafe_allow_html=True)  # Adds some space
+                st.markdown(
+                    """<style>
+                    #form input {margin-bottom: 15px;}
+                    </style>""", unsafe_allow_html=True
+                )
+
+                submit = st.form_submit_button("Submit Question")
+
+            # Display the result once the form is submitted
+            if submit:
+                llm = make_qa_chain()
+                chain = load_qa_chain(llm, chain_type="stuff")
+                # docs = db.similarity_search(user_input)
+                result = chain.run(question=user_input,input_documents = docs)
+                # result = qa_chain.invoke(user_input)
+                # result = qa_chain({"query": user_input})
+                st.success("Query Result:")
+                st.write(f"User: {user_input}")
+                st.write(f"Assistant: {result}")
+
+                # st.subheader("Source Documents:")
+                # for idx, elt in enumerate(result['source_documents']):
+                #     st.write(f"Source {idx + 1}:")
+                #     st.write(f"Filepath: {elt.metadata['audio_url']}")
+                #     st.write(f"Contents: {elt.page_content}")
+
+if __name__ == "__main__":
+    main()