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import os |
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import gradio as gr |
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from dotenv import load_dotenv |
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import indexing |
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import retrieval |
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import utils |
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list_llm = [ |
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"mistralai/Mistral-7B-Instruct-v0.3", |
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"microsoft/Phi-3.5-mini-instruct", |
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"meta-llama/Llama-3.1-8B-Instruct", |
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"meta-llama/Llama-3.2-3B-Instruct", |
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"TinyLlama/TinyLlama-1.1B-Chat-v1.0", |
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"google/gemma-2-2b-it", |
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"Qwen/Qwen2.5-3B-Instruct", |
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] |
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list_llm_simple = [os.path.basename(llm) for llm in list_llm] |
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def retrieve_api(): |
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load_dotenv() |
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global huggingfacehub_api_token |
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huggingfacehub_api_token = os.environ.get("HUGGINGFACE_API_KEY") |
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def initialize_database(list_file_obj, chunk_size, chunk_overlap, progress=gr.Progress()): |
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list_file_path = [x.name for x in list_file_obj if x is not None] |
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collection_name = indexing.create_collection_name(list_file_path[0]) |
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doc_splits, full_text = indexing.load_doc(list_file_path, chunk_size, chunk_overlap) |
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summary = utils.generate_summary(full_text) |
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vector_db = indexing.create_db(doc_splits, collection_name) |
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return vector_db, collection_name, summary, "Complete!" |
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def initialize_llm(llm_option, llm_temperature, max_tokens, top_k, vector_db, progress=gr.Progress()): |
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llm_name = list_llm[llm_option] |
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qa_chain = retrieval.initialize_llmchain( |
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llm_name, huggingfacehub_api_token, llm_temperature, max_tokens, top_k, vector_db, progress |
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) |
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return qa_chain, "Complete!" |
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def conversation(qa_chain, message, history): |
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qa_chain, new_history, response_sources = retrieval.invoke_qa_chain(qa_chain, message, history) |
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highlights = utils.extract_highlight_snippets(response_sources) |
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return qa_chain, gr.update(value=""), new_history, *highlights |
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def challenge_me(qa_chain): |
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questions = utils.generate_challenge_questions(qa_chain) |
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return questions |
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def evaluate_answers(qa_chain, questions, user_answers): |
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feedback = utils.evaluate_responses(qa_chain, questions, user_answers) |
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return feedback |
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def gradio_ui(): |
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with gr.Blocks(theme=gr.themes.Monochrome(), css="footer {display:none}") as demo: |
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vector_db = gr.State() |
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qa_chain = gr.State() |
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collection_name = gr.State() |
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gr.Markdown("""<h1 style='text-align:center;'>📚 GenAI Document Assistant</h1> |
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<h3 style='text-align:center;color:gray;'>Smart, interactive reading of research papers, legal docs, and more.</h3>""") |
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with gr.Tab("1️⃣ Upload Document"): |
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document = gr.File(label="Upload PDF or TXT", file_types=[".pdf", ".txt"], file_count="multiple") |
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slider_chunk_size = gr.Slider(100, 1000, value=600, step=20, label="Chunk Size") |
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slider_chunk_overlap = gr.Slider(10, 200, value=40, step=10, label="Chunk Overlap") |
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db_progress = gr.Textbox(label="Processing Status") |
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summary_box = gr.Textbox(label="Auto Summary (≤ 150 words)", lines=5) |
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db_btn = gr.Button("📥 Process Document") |
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with gr.Tab("2️⃣ QA Chain Initialization"): |
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llm_btn = gr.Radio(list_llm_simple, label="Select LLM", value=list_llm_simple[0], type="index") |
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slider_temperature = gr.Slider(0.01, 1.0, value=0.7, step=0.1, label="Temperature") |
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slider_maxtokens = gr.Slider(224, 4096, value=1024, step=32, label="Max Tokens") |
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slider_topk = gr.Slider(1, 10, value=3, step=1, label="Top-K") |
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llm_progress = gr.Textbox(label="LLM Status") |
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qachain_btn = gr.Button("⚙️ Initialize QA Chain") |
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with gr.Tab("3️⃣ Ask Anything"): |
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chatbot = gr.Chatbot(height=300) |
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msg = gr.Textbox(placeholder="Ask a question from the document...") |
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submit_btn = gr.Button("💬 Ask") |
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clear_btn = gr.ClearButton([msg, chatbot]) |
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ref1 = gr.Textbox(label="Reference 1") |
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ref2 = gr.Textbox(label="Reference 2") |
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ref3 = gr.Textbox(label="Reference 3") |
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with gr.Tab("4️⃣ Challenge Me"): |
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challenge_btn = gr.Button("🎯 Generate Questions") |
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q1 = gr.Textbox(label="Question 1") |
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a1 = gr.Textbox(label="Your Answer 1") |
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q2 = gr.Textbox(label="Question 2") |
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a2 = gr.Textbox(label="Your Answer 2") |
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q3 = gr.Textbox(label="Question 3") |
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a3 = gr.Textbox(label="Your Answer 3") |
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eval_btn = gr.Button("✅ Submit Answers") |
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feedback = gr.Textbox(label="Feedback", lines=5) |
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db_btn.click(initialize_database, [document, slider_chunk_size, slider_chunk_overlap], [vector_db, collection_name, summary_box, db_progress]) |
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qachain_btn.click(initialize_llm, [llm_btn, slider_temperature, slider_maxtokens, slider_topk, vector_db], [qa_chain, llm_progress]) |
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submit_btn.click(conversation, [qa_chain, msg, chatbot], [qa_chain, msg, chatbot, ref1, ref2, ref3]) |
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challenge_btn.click(challenge_me, [qa_chain], [q1, q2, q3]) |
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eval_btn.click(evaluate_answers, [qa_chain, [q1, q2, q3], [a1, a2, a3]], [feedback]) |
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demo.launch(debug=True) |
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if __name__ == "__main__": |
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retrieve_api() |
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gradio_ui() |
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