Update pages/jarvis.py

pages/jarvis.py

@@ -1,48 +1,115 @@
 import streamlit as st
-from transformers import pipeline
-import edge_tts
-import tempfile
-
-# Initialize voice chat pipeline and other settings
-default_lang = "en"
-system_instructions = "[SYSTEM] Answer as Real Jarvis JARVIS, Made by 'Tony Stark', Keep conversation very short, clear, friendly and concise. The text provided is a request for a specific type of response from you, the virtual assistant. The request asks you to provide friendly responses as if You are the character Jarvis, made by 'Tony Stark.' The expectation is that I will avoid introductions and start answering the query directly, elaborating on all aspects of the request. As an AI-powered assistant, your task is to generate responses that appear as if they are created by the user and not an AI-powered assistant. [USER]"
-client = pipeline("text2text-generation", model="mistralai/Mixtral-8x7B-Instruct-v0.1")
-
-# Function to transcribe audio
-def transcribe(audio):
-    lang = "en"
-    text = "Sample transcription"  # Replace with your transcription logic
-    return text
-
-# Function to generate Jarvis response
-def generate_jarvis_response(prompt):
-    formatted_prompt = system_instructions + prompt + "[JARVIS]"
-    output = client(formatted_prompt)
-    response_text = output[0]['generated_text']
-    communicate = edge_tts.Communicate(response_text)
-    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
-        tmp_path = tmp_file.name
-        communicate.save(tmp_path)
-    return tmp_path
-
-# Streamlit UI
-st.title("JARVIS⚡ - Personal Assistant of Tony Stark")
-st.markdown("Voice Chat with your personal assistant")
-
-# Voice chat interface
-audio_file = st.file_uploader("Voice Chat (BETA) - Upload audio file", type=["mp3", "wav"])
-if audio_file:
-    audio_path = transcribe(audio_file)
-    response_path = generate_jarvis_response(audio_path)
-    st.audio(response_path)
-
-# Text-based interaction
-st.markdown("### Text-based Interaction")
-prompt_text = st.text_input("Prompt", "What is Wikipedia?")
-if st.button("Generate Response"):
-    response_path = generate_jarvis_response(prompt_text)
-    st.audio(response_path)
-
-# # Additional links
-# st.markdown("### Try Other Models")
-# st.markdown("[Instant ](https://huggingface.co/spaces/)")
+from langchain_community.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import Chroma
+from langchain.chains import ConversationalRetrievalChain
+from langchain_community.embeddings import HuggingFaceEmbeddings 
+from langchain_community.llms import HuggingFacePipeline
+from langchain.chains import ConversationChain
+from langchain.memory import ConversationBufferMemory
+from langchain_community.llms import HuggingFaceEndpoint
+from pathlib import Path
+import chromadb
+from unidecode import unidecode
+from transformers import AutoTokenizer
+import transformers
+import torch
+import tqdm 
+import accelerate
+import re
+
+# Function to load PDF document and create doc splits
+def load_doc(list_file_path, chunk_size, chunk_overlap):
+    loaders = [PyPDFLoader(x) for x in list_file_path]
+    pages = []
+    for loader in loaders:
+        pages.extend(loader.load())
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=chunk_size, 
+        chunk_overlap=chunk_overlap
+    )
+    doc_splits = text_splitter.split_documents(pages)
+    return doc_splits
+
+# Function to create vector database
+def create_db(splits, collection_name):
+    embedding = HuggingFaceEmbeddings()
+    new_client = chromadb.EphemeralClient()
+    vectordb = Chroma.from_documents(
+        documents=splits,
+        embedding=embedding,
+        client=new_client,
+        collection_name=collection_name,
+    )
+    return vectordb
+
+# Initialize Langchain LLM chain
+def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db):
+    if llm_model == "mistralai/Mixtral-8x7B-Instruct-v0.1":
+        llm = HuggingFaceEndpoint(
+            repo_id=llm_model, 
+            temperature=temperature,
+            max_new_tokens=max_tokens,
+            top_k=top_k,
+            load_in_8bit=True,
+        )
+    # Add other LLM models initialization conditions here...
+    memory = ConversationBufferMemory(
+        memory_key="chat_history",
+        output_key='answer',
+        return_messages=True
+    )
+    retriever = vector_db.as_retriever()
+    qa_chain = ConversationalRetrievalChain.from_llm(
+        llm,
+        retriever=retriever,
+        chain_type="stuff", 
+        memory=memory,
+        return_source_documents=True,
+        verbose=False,
+    )
+    return qa_chain
+
+# Function to process uploaded PDFs and initialize the database
+def process_documents(list_file_obj, chunk_size, chunk_overlap):
+    list_file_path = [x.name for x in list_file_obj if x is not None]
+    collection_name = create_collection_name(list_file_path[0])
+    doc_splits = load_doc(list_file_path, chunk_size, chunk_overlap)
+    vector_db = create_db(doc_splits, collection_name)
+    return vector_db
+
+# Streamlit app
+def main():
+    st.title("PDF-based Chatbot")
+    st.write("Ask any questions about your PDF documents")
+
+    # Step 1: Upload PDF documents
+    uploaded_files = st.file_uploader("Upload your PDF documents (single or multiple)", type=["pdf"], accept_multiple_files=True)
+
+    # Step 2: Process documents and initialize vector database
+    if uploaded_files:
+        chunk_size = st.slider("Chunk size", min_value=100, max_value=1000, value=600, step=20)
+        chunk_overlap = st.slider("Chunk overlap", min_value=10, max_value=200, value=40, step=10)
+        if st.button("Generate Vector Database"):
+            vector_db = process_documents(uploaded_files, chunk_size, chunk_overlap)
+            st.success("Vector database generated successfully!")
+
+            # Step 3: Initialize QA chain with selected LLM model
+            st.header("Initialize Question Answering (QA) Chain")
+            llm_model = st.selectbox("Choose LLM Model", list_llm_simple)
+            temperature = st.slider("Temperature", min_value=0.01, max_value=1.0, value=0.7, step=0.1)
+            max_tokens = st.slider("Max Tokens", min_value=224, max_value=4096, value=1024, step=32)
+            top_k = st.slider("Top-k Samples", min_value=1, max_value=10, value=3, step=1)
+            if st.button("Initialize QA Chain"):
+                qa_chain = initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db)
+                st.success("QA Chain initialized successfully!")
+
+                # Step 4: Chatbot interaction
+                st.header("Chatbot")
+                message = st.text_input("Type your message here")
+                if st.button("Submit"):
+                    response = qa_chain(message)
+                    st.write(f"Chatbot Response: {response['answer']}")
+
+if __name__ == "__main__":
+    main()