Create app.py

app.py

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+# streamlit_app.py
+
+import streamlit as st
+from langchain.vectorstores import Chroma
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.document_loaders import TextLoader
+from langchain.document_loaders import PyPDFLoader
+from langchain.document_loaders import DirectoryLoader
+import dotenv
+import os
+from InstructorEmbedding import INSTRUCTOR
+from langchain.embeddings import HuggingFaceInstructEmbeddings
+import google.generativeai as genai
+
+# Load environment variables
+dotenv.load_dotenv()
+
+# Configure Google Generative AI
+genai.configure(api_key=os.getenv('GOOGLE_API_KEY'))
+
+llm = genai.GenerativeModel('gemini-pro')
+
+# Streamlit UI
+st.title("AI Response Generator")
+
+# Input text box
+input_text = st.text_area("Enter your input text:", "What's the point of making myself less accessible?")
+
+loader = DirectoryLoader('./', glob="./*.pdf", loader_cls=PyPDFLoader)
+documents = loader.load()
+
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+texts = text_splitter.split_documents(documents)
+
+instructor_embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl", model_kwargs={"device": "cpu"})
+
+persist_directory = 'db'
+embedding = instructor_embeddings
+
+vectordb = Chroma.from_documents(documents=texts, embedding=embedding, persist_directory=persist_directory)
+vectordb.persist()
+vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding)
+retriever = vectordb.as_retriever(search_kwargs={"k": 3})
+
+# Function to generate response
+def generate_response(input_text):
+
+    docs = retriever.get_relevant_documents(input_text)
+
+    text = ""
+    for doc in docs:
+        text += doc.page_content
+
+    new_input_text = f"Given the below details:\n{text}\n\n do the following \n{input_text}\n"
+    response = llm.generate_content(new_input_text)
+
+    return response.text
+
+
+
+# Button to generate response
+if st.button("Generate Response"):
+    # Generate response
+    response_text = generate_response(input_text)
+
+    # Display the response
+    st.subheader("Generated Response:")
+    st.write(response_text)