add files

app.py

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+import streamlit as st
+from langchain.document_loaders import PyPDFLoader
+from langchain.vectorstores import FAISS
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.chains import RetrievalQA
+from langchain.llms import OpenAI
+from langchain.llms import HuggingFaceHub
+# from langchain import HuggingFaceHub
+
+if "responses" not in st.session_state:
+    st.session_state.responses = []
+
+if "questions" not in st.session_state:
+    st.session_state.questions = []
+
+
+def app():
+    st.set_page_config(
+        page_title="Chat with AI",
+        page_icon="🤖",
+        layout="centered"
+    )
+    st.title("Chat with AI")
+    st.markdown(":violet[Get Huggingface API Read Token or Open AI API Key]")
+    st.markdown("#### Select an Option")
+    Option = st.selectbox(
+        label="Select the model",
+        options=(
+            "Select the model",
+            "HuggingFace(Uses Falcon 4b Model)",
+            "OpenAI"
+        ),
+        label_visibility="collapsed"
+    )
+    if Option != "Select the model":
+        st.markdown("#### Enter your " + Option + " API key")
+        API = st.text_input(
+            "Enter your " + Option + " API key",
+            label_visibility="collapsed"
+        )
+        if API != "":
+            st.markdown("#### Upload a document")
+            doc = st.file_uploader("Upload a document", type=["pdf"], label_visibility="collapsed")
+            if doc is not None:
+                with open("doc.pdf", "wb") as f:
+                    f.write(doc.getbuffer())
+                loader = PyPDFLoader("doc.pdf")
+                pages = loader.load_and_split()
+                embeddings = HuggingFaceEmbeddings(
+                    model_name="all-MiniLM-L6-v2"
+                )
+                faiss_index = FAISS.from_documents(pages, embeddings)
+                llm = OpenAI(open_api_key=API) if Option == "OpenAI" else (
+                    HuggingFaceHub(
+                        repo_id="tiiuae/falcon-7b-instruct",
+                        model_kwargs={
+                            "temperature": 0.5,
+                            "max_new_tokens": 500
+                        },
+                        huggingfacehub_api_token=API,
+                    )
+                )
+                qa = RetrievalQA.from_chain_type(
+                    llm=llm,
+                    chain_type="stuff",
+                    retriever=faiss_index.as_retriever(
+                        search_type="mmr",
+                        search_kwargs={'fetch_k': 10}),
+                    return_source_documents=True
+                )
+
+                container = st.container()
+                st.write("Ask Your Question Here")
+                question = st.text_input(
+                    "Ask your question here",
+                    label_visibility="collapsed"
+                )
+                with container:
+                    with st.chat_message("assistant"):
+                        st.write("How can I help you?")
+
+                    if question != "":
+                        response = qa(question)
+                        st.session_state.responses.insert(0, response)
+                        st.session_state.questions.insert(0, question)
+
+                        for i in range(len(st.session_state.responses)):
+                            with st.chat_message("user"):
+                                st.write(st.session_state.questions[i - 1])
+
+                            with st.chat_message("assistant"):
+                                with st.expander(
+                                    "Response (Click here to collapse)",
+                                    expanded=True
+                                ):
+                                    result = st.session_state.responses[i]
+                                    st.write(result['result'])
+                                st.write("Source documents: "
+                                         "(Most relevant are first)")
+                                for i in result['source_documents']:
+                                    with st.expander(
+                                        "Page: " + str(i.metadata['page'])
+                                    ):
+                                        st.write(i.page_content)
+                                st.divider()
+
+
+if __name__ == "__main__":
+    app()