main_final.py

import gradio as gr
from litellm import completion
import glob
import os
from retriever_reranker_final import Retriever

PROMPT = """\
You are a helpful assistant that can answer questions.
Rules:
- Provide clear and concise answers to all questions.
- Always paraphrase the context when forming your response. Do not copy the text directly.
- Use only the provided context to answer questions.
- Only give the answer.
- Avoid repeating long phrases from the context.
- Structure your responses in a way that is easy to read and understand.

"""
#- Include only information relevant to the question and ignore unrelated details.

class QuestionAnsweringBot:

    def __init__(self, docs) -> None:
        self.retriever = Retriever(docs)
        self.max_citations = 3

    def answer_question(self, question: str, api_key: str, methods: list[str]) -> list[str]:
        try:
            os.environ['GROQ_API_KEY'] = api_key

            if not methods:
                return ["No search method selected. Please select at least one search method.", "-", "-"]

            retr_context = self.retriever.get_docs(question, methods)
            reranked_context = self.retriever.rerank(question, retr_context)

            context_with_all_chunks = "\n".join([
                f"[{chunk_id}] {chunk_text}" for chunk_id, chunk_text in reranked_context.items()
            ])

            messages = [
                {"role": "system", "content": PROMPT},
                {"role": "user", "content": f"Context:\n{context_with_all_chunks}\nQuestion: {question}"}
            ]

            response = completion(
                model="groq/llama3-8b-8192",
                messages=messages
            )
            response_text = response.choices[0].message.content

            used_chunks = self._filter_used_chunks(response_text, reranked_context)
            top_chunks = self._get_top_chunks(used_chunks, self.max_citations)

            context_with_citations = "\n".join([
                f"[{chunk_id}] {chunk_text}" for chunk_id, chunk_text in top_chunks.items()
            ])
            used_sources = " ".join([f"[{chunk_id}]" for chunk_id in top_chunks.keys()])
            final_response = f"{response_text} \nUsed chunks: {used_sources}"

            return [
                final_response,
                context_with_citations,
                f"{reranked_context}"
            ]
        except Exception as e:
            print(f"Error: {e}")
            return ["Error occurred during processing.", "-", "-"]

    def _filter_used_chunks(self, response_text, reranked_context):
        used_chunks = {}
        for chunk_id, chunk_text in reranked_context.items():
            if any(word.lower() in response_text.lower() for word in chunk_text.split()):
                used_chunks[chunk_id] = chunk_text
        return used_chunks

    def _get_top_chunks(self, used_chunks, max_citations):
        unique_chunks = {}
        added_texts = set()
        for chunk_id, chunk_text in used_chunks.items():
            if any(chunk_text[:50] in text for text in added_texts):
                continue
            unique_chunks[chunk_id] = chunk_text
            added_texts.add(chunk_text[:50])
            if len(unique_chunks) >= max_citations:
                break
        return unique_chunks


# Load documents
all_docs = {}
for path in glob.glob("data/*.txt"):
    with open(path) as f:
        doc_name = os.path.basename(path)
        all_docs[doc_name] = f.read()

bot = QuestionAnsweringBot(all_docs)

with gr.Blocks() as demo:
    gr.HTML("""
 <div style="text-align: center; font-size: 40px; font-weight: bold; margin-bottom: 10px;">
            Harry Potter Encyclopedia
        </div>
        <div style="text-align: center; font-size: 25px ;;margin-bottom: 10px; color: #ffdac7">
            Performed by Subtelna Sofiia (CS-414) and Iryna Iskovych (CS-415)
        </div>
        <div style="font-size: 20px; ">
    This bot answers questions about the world of Harry Potter and the plot of the first book. System uses 3 files - general information about the world, main characters and the text of the first book. However, some of the information about the universe or later years can be missing.
    
    <div style="font-weight: bold; margin-top: 25px;">
      Instructions
    </div>
    <ol>
      <li>Enter your Groq API Key in the textbox below.</li>
      <li>The API key can be generated using this <a href="https://console.groq.com/keys">link</a></li>
      <li>Input your query</li>
      <li>Select the scoring method from the proposed ones:</li>
      <ul>
      <li>BM25</li>
      <li>Semantic search</li>
      <li>Combined search (combination of BM25 and semantic search)</li>
    </ul>
      <li>Click "Submit" button</li>

    </ol>
        </div>
        """)
    question_input = gr.Textbox(
        label="Question",
        placeholder="Ask your question here",
    )

    api_key_input = gr.Textbox(label="API key", placeholder="Provide API key here", info="Input key from Groq")
    search_method = gr.Radio(
        info="Choose the search method from the proposed ones",
        choices=["BM25", "semantic", "combined search"],
        label="Search Method",
        value="BM25"
    )

    submit_button = gr.Button("Submit")

    output_bot = gr.Textbox(label="Answer")
    output_citations = gr.Textbox(label="Citations")
    #output_context = gr.Textbox(label="Context")

    submit_button.click(
        bot.answer_question,
        inputs=[question_input, api_key_input, search_method],
        outputs=[output_bot,output_citations]#, output_context]
    )

demo.launch()