final

app.py

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+import gradio as gr
+import os
+from langchain.chains import ConversationalRetrievalChain
+from langchain_community.vectorstores import FAISS
+from langchain_groq import ChatGroq
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain.chains.question_answering import load_qa_chain
+from langchain.prompts import PromptTemplate
+from langchain.memory import ConversationBufferMemory
+from dotenv import load_dotenv
+from langchain_community.document_loaders import TextLoader
+
+load_dotenv()
+
+# Load environment variables
+GROQ_API_KEY = os.getenv("GROQ_API_KEY")
+
+# Set up LLM and embeddings
+llm = ChatGroq(groq_api_key=GROQ_API_KEY, model_name="llama3-8b-8192")
+embeddings = HuggingFaceEmbeddings()
+
+# Load documents and create vectorstore
+loader = TextLoader("sample.txt")  # Put your content here
+documents = loader.load()
+db = FAISS.from_documents(documents, embeddings)
+
+# Prompt template
+template = """
+You are a helpful assistant. Use the following conversation and context to answer the question.
+If you don't know the answer, just say you don't know.
+
+Context: {context}
+Conversation History: {chat_history}
+Question: {question}
+
+Helpful answer:
+"""
+
+prompt = PromptTemplate(input_variables=["context", "chat_history", "question"], template=template)
+
+memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+
+qa_chain = ConversationalRetrievalChain.from_llm(
+    llm=llm,
+    retriever=db.as_retriever(),
+    memory=memory,
+    condense_question_prompt=prompt,
+)
+
+# Gradio UI
+def chat_with_voice(audio):
+    text = audio  # audio input is transcribed to text
+    result = qa_chain.invoke({"question": text})
+    return result['answer']
+
+iface = gr.Interface(
+    fn=chat_with_voice,
+    inputs=gr.Audio(source="microphone", type="filepath", label="Speak Your Question"),
+    outputs="text",
+    title="Voice-Powered LLM Chatbot",
+    description="Talk to a chatbot using your voice. Backed by Groq's Llama 3 and vector search.",
+)
+
+if __name__ == "__main__":
+    iface.launch()

requirements.txt

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+gradio
+langchain
+langchain-community
+langchain-groq
+faiss-cpu
+sentence-transformers
+python-dotenv
+groq