Create app.py

app.py

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+import os
+import gradio as gr
+import torch
+from langchain.chains import ConversationalRetrievalChain
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.document_loaders import PyPDFLoader
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import Chroma
+from langchain_community.llms import HuggingFacePipeline
+from transformers import pipeline
+
+# Set Hugging Face Cache Directory
+os.environ["HF_HOME"] = "/tmp/huggingface_cache"
+
+# Check for GPU availability
+DEVICE = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+# Global variables
+conversation_retrieval_chain = None
+chat_history = []
+llm_pipeline = None
+embeddings = None
+persist_directory = "/tmp/chroma_db"  # Storage for vector DB
+
+
+def init_llm():
+    """Initialize LLM and Embeddings"""
+    global llm_pipeline, embeddings
+
+    hf_token = os.getenv("HUGGINGFACEHUB_API_TOKEN")
+    if not hf_token:
+        raise ValueError("HUGGINGFACEHUB_API_TOKEN is not set in environment variables.")
+
+    model_id = "tiiuae/falcon-7b-instruct"
+    hf_pipeline = pipeline("text-generation", model=model_id, device=DEVICE)
+    llm_pipeline = HuggingFacePipeline(pipeline=hf_pipeline)
+
+    embeddings = HuggingFaceEmbeddings(
+        model_name="sentence-transformers/all-MiniLM-L6-v2",
+        model_kwargs={"device": DEVICE}
+    )
+
+
+def process_document(file):
+    """Process uploaded PDF and create a retriever"""
+    global conversation_retrieval_chain
+
+    if not llm_pipeline or not embeddings:
+        init_llm()
+
+    # Load PDF and split text
+    loader = PyPDFLoader(file.name)
+    documents = loader.load()
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1024, chunk_overlap=64)
+    texts = text_splitter.split_documents(documents)
+
+    # Load or create ChromaDB
+    if os.path.exists(persist_directory):
+        db = Chroma(persist_directory=persist_directory, embedding_function=embeddings)
+    else:
+        db = Chroma.from_documents(texts, embedding=embeddings, persist_directory=persist_directory)
+
+    retriever = db.as_retriever(search_type="similarity", search_kwargs={'k': 6})
+
+    # Initialize ConversationalRetrievalChain
+    conversation_retrieval_chain = ConversationalRetrievalChain.from_llm(
+        llm=llm_pipeline, retriever=retriever
+    )
+
+    return "📄 PDF uploaded and processed successfully! You can now ask questions."
+
+
+def process_prompt(prompt, chat_history_display):
+    """Generate a response using the retrieval chain"""
+    global conversation_retrieval_chain, chat_history
+
+    if not conversation_retrieval_chain:
+        return chat_history_display + [("❌ No document uploaded.", "Please upload a PDF first.")]
+
+    output = conversation_retrieval_chain({"question": prompt, "chat_history": chat_history})
+    answer = output["answer"]
+
+    chat_history.append((prompt, answer))
+    
+    return chat_history
+
+
+# Define Gradio UI
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("<h1 style='text-align: center;'>Personal Data Assistant</h1>")
+
+    with gr.Row():
+        dark_mode = gr.Checkbox(label="🌙 Toggle light/dark mode")
+    
+    with gr.Row():
+        with gr.Box():
+            gr.Markdown("Hello there! I'm your friendly data assistant, ready to answer any questions regarding your data. Could you please upload a PDF file for me to analyze?")
+            file_input = gr.File(label="Upload File")
+            upload_button = gr.Button("📂 Upload File")
+
+    status_output = gr.Textbox(label="Status", interactive=False)
+
+    chat_history_display = gr.Chatbot(label="Chat History")
+    
+    with gr.Row():
+        user_input = gr.Textbox(placeholder="Type your message here...", scale=4)
+        submit_button = gr.Button("📩", scale=1)
+        clear_button = gr.Button("🔄", scale=1)
+
+    # Button Click Actions
+    upload_button.click(process_document, inputs=file_input, outputs=status_output)
+    submit_button.click(process_prompt, inputs=[user_input, chat_history_display], outputs=chat_history_display)
+    clear_button.click(lambda: [], outputs=chat_history_display)
+
+# Launch Gradio App
+if __name__ == "__main__":
+    demo.launch(share=True)