Update frontend.py

frontend.py

@@ -1,122 +1,71 @@
-import torch
-import subprocess
-import streamlit as st
-from run_localGPT import load_model
-from langchain.vectorstores import Chroma
-from constants import CHROMA_SETTINGS, EMBEDDING_MODEL_NAME, PERSIST_DIRECTORY, MODEL_ID, MODEL_BASENAME
-from langchain.embeddings import HuggingFaceInstructEmbeddings
-from langchain.chains import RetrievalQA
-from streamlit_extras.add_vertical_space import add_vertical_space
-from langchain.prompts import PromptTemplate
-from langchain.memory import ConversationBufferMemory
-
-
-def model_memory():
-    # Adding history to the model.
-    template = """Use the following pieces of context to answer the question at the end. If you don't know the answer,\
-    just say that you don't know, don't try to make up an answer.
-
-    {context}
-
-    {history}
-    Question: {question}
-    Helpful Answer:"""
-
-    prompt = PromptTemplate(input_variables=["history", "context", "question"], template=template)
-    memory = ConversationBufferMemory(input_key="question", memory_key="history")
-
-    return prompt, memory
-
-
-# Sidebar contents
-with st.sidebar:
-    st.title("🤗💬 Converse with your Data")
-    st.markdown(
-        """
-    ## About
-    This app is an LLM-powered chatbot built using:
-    - [Streamlit](https://streamlit.io/)
-    - [LangChain](https://python.langchain.com/)
-    - [LocalGPT](https://github.com/PromtEngineer/localGPT) 
- 
-    """
-    )
-    add_vertical_space(5)
-    st.write("Made with ❤️ by [Prompt Engineer](https://youtube.com/@engineerprompt)")
-
-
-if torch.backends.mps.is_available():
-    DEVICE_TYPE = "mps"
-elif torch.cuda.is_available():
-    DEVICE_TYPE = "cuda"
-else:
-    DEVICE_TYPE = "cpu"
-
-
-# if "result" not in st.session_state:
-#     # Run the document ingestion process.
-#     run_langest_commands = ["python", "ingest.py"]
-#     run_langest_commands.append("--device_type")
-#     run_langest_commands.append(DEVICE_TYPE)
-
-#     result = subprocess.run(run_langest_commands, capture_output=True)
-#     st.session_state.result = result
-
-# Define the retreiver
-# load the vectorstore
-if "EMBEDDINGS" not in st.session_state:
-    EMBEDDINGS = HuggingFaceInstructEmbeddings(model_name=EMBEDDING_MODEL_NAME, model_kwargs={"device": DEVICE_TYPE})
-    st.session_state.EMBEDDINGS = EMBEDDINGS
-
-if "DB" not in st.session_state:
-    DB = Chroma(
-        persist_directory=PERSIST_DIRECTORY,
-        embedding_function=st.session_state.EMBEDDINGS,
-        client_settings=CHROMA_SETTINGS,
+import argparse
+import os
+import sys
+import tempfile
+
+import requests
+from flask import Flask, render_template, request
+from werkzeug.utils import secure_filename
+
+sys.path.append(os.path.join(os.path.dirname(__file__), ".."))
+
+app = Flask(__name__)
+app.secret_key = "LeafmanZSecretKey"
+
+API_HOST = "https://dkdaniz-katara.hf.space:5110/api"
+
+# PAGES #
+@app.route("/", methods=["GET", "POST"])
+def home_page():
+    if request.method == "POST":
+        if "user_prompt" in request.form:
+            user_prompt = request.form["user_prompt"]
+            print(f"User Prompt: {user_prompt}")
+
+            main_prompt_url = f"{API_HOST}/prompt_route"
+            response = requests.post(main_prompt_url, data={"user_prompt": user_prompt})
+            print(response.status_code)  # print HTTP response status code for debugging
+            if response.status_code == 200:
+                # print(response.json())  # Print the JSON data from the response
+                return render_template("home.html", show_response_modal=True, response_dict=response.json())
+        elif "documents" in request.files:
+            delete_source_url = f"{API_HOST}/delete_source"  # URL of the /api/delete_source endpoint
+            if request.form.get("action") == "reset":
+                response = requests.get(delete_source_url)
+
+            save_document_url = f"{API_HOST}/save_document"
+            run_ingest_url = f"{API_HOST}/run_ingest"  # URL of the /api/run_ingest endpoint
+            files = request.files.getlist("documents")
+            for file in files:
+                print(file.filename)
+                filename = secure_filename(file.filename)
+                with tempfile.SpooledTemporaryFile() as f:
+                    f.write(file.read())
+                    f.seek(0)
+                    response = requests.post(save_document_url, files={"document": (filename, f)})
+                    print(response.status_code)  # print HTTP response status code for debugging
+            # Make a GET request to the /api/run_ingest endpoint
+            response = requests.get(run_ingest_url)
+            print(response.status_code)  # print HTTP response status code for debugging
+
+    # Display the form for GET request
+    return render_template(
+        "home.html",
+        show_response_modal=False,
+        response_dict={"Prompt": "None", "Answer": "None", "Sources": [("ewf", "wef")]},
     )
-    st.session_state.DB = DB
-
-if "RETRIEVER" not in st.session_state:
-    RETRIEVER = DB.as_retriever()
-    st.session_state.RETRIEVER = RETRIEVER
 
-if "LLM" not in st.session_state:
-    LLM = load_model(device_type=DEVICE_TYPE, model_id=MODEL_ID, model_basename=MODEL_BASENAME)
-    st.session_state["LLM"] = LLM
 
-
-if "QA" not in st.session_state:
-    prompt, memory = model_memory()
-
-    QA = RetrievalQA.from_chain_type(
-        llm=LLM,
-        chain_type="stuff",
-        retriever=RETRIEVER,
-        return_source_documents=True,
-        chain_type_kwargs={"prompt": prompt, "memory": memory},
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--port", type=int, default=5111, help="Port to run the UI on. Defaults to 5111.")
+    parser.add_argument(
+        "--host",
+        type=str,
+        default="0.0.0.0",
+        help="Host to run the UI on. Defaults to 127.0.0.1. "
+        "Set to 0.0.0.0 to make the UI externally "
+        "accessible from other devices.",
     )
-    st.session_state["QA"] = QA
-
-st.title("LocalGPT App 💬")
-# Create a text input box for the user
-prompt = st.text_input("Input your prompt here")
-# while True:
-
-# If the user hits enter
-if prompt:
-    # Then pass the prompt to the LLM
-    response = st.session_state["QA"](prompt)
-    answer, docs = response["result"], response["source_documents"]
-    # ...and write it out to the screen
-    st.write(answer)
-
-    # With a streamlit expander
-    with st.expander("Document Similarity Search"):
-        # Find the relevant pages
-        search = st.session_state.DB.similarity_search_with_score(prompt)
-        # Write out the first
-        for i, doc in enumerate(search):
-            # print(doc)
-            st.write(f"Source Document # {i+1} : {doc[0].metadata['source'].split('/')[-1]}")
-            st.write(doc[0].page_content)
-            st.write("--------------------------------")
+    args = parser.parse_args()
+    app.run(debug=False, host=args.host, port=args.port)