Create app.py

app.py

@@ -0,0 +1,83 @@
+import os
+from groq import Groq
+import torch
+import transformers
+from transformers import pipeline
+from langchain_groq import ChatGroq
+from langchain.vectorstores import Chroma
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.chains import RetrievalQA
+from langchain.embeddings import HuggingFaceBgeEmbeddings
+from langchain.prompts import PromptTemplate
+import streamlit as st
+
+# Groq API Configuration
+GROQ_API_KEY = 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
+
+# Initialize Groq Client
+groq_client = Groq(api_key=GROQ_API_KEY)
+
+# Configure Llama 3.2 LLM with Groq
+def configure_groq_llm(
+    model_name="llama3-70b-8192",
+    temperature=0.7,
+    max_tokens=2048
+):
+    return ChatGroq(
+        groq_api_key=GROQ_API_KEY,
+        model_name=model_name,
+        temperature=temperature,
+        max_tokens=max_tokens
+    )
+
+# Embedding Configuration
+def get_embeddings(model_name="BAAI/bge-base-en"):
+    encode_kwargs = {'normalize_embeddings': True}
+    return HuggingFaceBgeEmbeddings(
+        model_name=model_name,
+        encode_kwargs=encode_kwargs
+    )
+
+# Prompt Template
+def create_llama_prompt():
+    template = """
+    Use the following context to answer the question:
+    Context: {context}
+    Question: {question}
+    Helpful Answer:"""
+    
+    return PromptTemplate(
+        template=template, 
+        input_variables=["context", "question"]
+    )
+
+# Initialize Components
+embeddings = get_embeddings()
+llm = configure_groq_llm()
+vectordb = Chroma(persist_directory='db', embedding_function=embeddings)
+retriever = vectordb.as_retriever(search_kwargs={"k": 5})
+prompt = create_llama_prompt()
+
+# QA Chain Configuration
+qa_chain = RetrievalQA.from_chain_type(
+    llm=llm,
+    chain_type="stuff",
+    retriever=retriever,
+    chain_type_kwargs={"prompt": prompt},
+    return_source_documents=True
+)
+
+# Streamlit Interface
+def groq_nlp_chatbot():
+    st.title("Groq Llama 3.2 Chatbot")
+    
+    user_input = st.text_input("Your Question:")
+    if user_input:
+        try:
+            response = qa_chain(user_input)
+            st.text_area("Bot's Response:", response['result'])
+        except Exception as e:
+            st.error(f"Error processing request: {e}")
+
+if __name__ == "__main__":
+    groq_nlp_chatbot()