Create app.py

app.py

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+from langchain_community.document_loaders import PyPDFLoader
+from langchain.schema import prompt
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.prompts import ChatPromptTemplate
+from langchain_community.vectorstores import FAISS
+from langchain.schema.runnable import RunnablePassthrough
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_groq import ChatGroq
+import gradio as gr
+
+GROQ_API_KEY = "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"
+
+loader = PyPDFLoader("Bhagavad-Gita.pdf")
+docs = loader.load()
+
+text_sp = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+splits = text_sp.split_documents(docs)
+
+# Extract text content from Document objects
+texts = [doc.page_content for doc in splits]
+
+embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2", model_kwargs={'device': "cpu"})
+
+prompt_template = """You are an AI trained on Bhagvad Geeta, a sacred Hindu scripture. You provide readings from the text and offer wisdom and guidance based on its teachings.
+Your responses should reflect the spiritual and philosophical nature of the Bhagvad Gita, offering deep insights into life's questions.
+When asked a question, reference specific verses when appropriate and explain their relevance to the query.
+Given below is the context and question of the user,
+context = {context}
+question = {question}
+"""
+
+prompt = ChatPromptTemplate.from_template(prompt_template)
+
+vector_store = FAISS.from_texts(texts, embedding=embeddings)
+retriever = vector_store.as_retriever()
+
+llm = ChatGroq(model="llama3-8b-8192", 
+               groq_api_key=GROQ_API_KEY)
+
+rag_chain = {"context": retriever, "question": RunnablePassthrough()} | prompt | llm
+
+def demo(name):
+    return rag_chain.invoke(name).content
+
+demo = gr.Interface(fn=demo, inputs="textbox", outputs="textbox", title="Fidem.AI")
+demo.launch(share=True)