app.py

import gradio as gr
import fitz
import numpy as np
import requests
import faiss
import re
import json
import pandas as pd
from docx import Document
from pptx import Presentation
from sentence_transformers import SentenceTransformer
from concurrent.futures import ThreadPoolExecutor

# Configuration
GROQ_API_KEY = "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"  # 🔑 REPLACE WITH YOUR ACTUAL KEY
MODEL_NAME = "all-MiniLM-L6-v2"
CHUNK_SIZE = 1024 #512
MAX_TOKENS = 4096
MODEL = SentenceTransformer(MODEL_NAME)
WORKERS = 8

class DocumentProcessor:
    def __init__(self):
        self.index = faiss.IndexFlatIP(MODEL.get_sentence_embedding_dimension())
        self.chunks = []
        self.processor_pool = ThreadPoolExecutor(max_workers=WORKERS)

    def extract_text_from_pptx(self, file_path):
        try:
            prs = Presentation(file_path)
            return " ".join([shape.text for slide in prs.slides for shape in slide.shapes if hasattr(shape, "text")])
        except Exception as e:
            print(f"PPTX Error: {str(e)}")
            return ""

    def extract_text_from_xls_csv(self, file_path):
        try:
            if file_path.endswith(('.xls', '.xlsx')):
                df = pd.read_excel(file_path)
            else:
                df = pd.read_csv(file_path)
            return " ".join(df.astype(str).values.flatten())
        except Exception as e:
            print(f"Spreadsheet Error: {str(e)}")
            return ""

    def extract_text_from_pdf(self, file_path):
        try:
            doc = fitz.open(file_path)
            return " ".join(page.get_text("text", flags=fitz.TEXT_PRESERVE_WHITESPACE) for page in doc)
        except Exception as e:
            print(f"PDF Error: {str(e)}")
            return ""

    def process_file(self, file):
        try:
            file_path = file.name
            print(f"Processing: {file_path}")  # Debug print

            if file_path.endswith('.pdf'):
                text = self.extract_text_from_pdf(file_path)
            elif file_path.endswith('.docx'):
                text = " ".join(p.text for p in Document(file_path).paragraphs)
            elif file_path.endswith('.txt'):
                with open(file_path, 'r', encoding='utf-8') as f:
                    text = f.read()
            elif file_path.endswith('.pptx'):
                text = self.extract_text_from_pptx(file_path)
            elif file_path.endswith(('.xls', '.xlsx', '.csv')):
                text = self.extract_text_from_xls_csv(file_path)
            else:
                return ""

            clean_text = re.sub(r'\s+', ' ', text).strip()
            print(f"Extracted {len(clean_text)} characters from {file_path}")  # Debug
            return clean_text
        except Exception as e:
            print(f"Processing Error: {str(e)}")  # Debug
            return ""

    def semantic_chunking(self, text):
        words = re.findall(r'\S+\s*', text)
        chunks = [''.join(words[i:i+CHUNK_SIZE//2]) for i in range(0, len(words), CHUNK_SIZE//2)]
        return chunks[:]  # Limit to 1000 chunks per document

    def process_documents(self, files):
        self.chunks = []
        if not files:
            return "No files uploaded!"

        print("\n" + "="*40 + " PROCESSING DOCUMENTS " + "="*40)
        texts = list(self.processor_pool.map(self.process_file, files))

        with ThreadPoolExecutor(max_workers=WORKERS) as executor:
            chunk_lists = list(executor.map(self.semantic_chunking, texts))

        all_chunks = [chunk for chunk_list in chunk_lists for chunk in chunk_list]
        print(f"Total chunks generated: {len(all_chunks)}")  # Debug

        if not all_chunks:
            return "Error: No chunks generated from documents"

        try:
            embeddings = MODEL.encode(
                all_chunks,
                batch_size=256, #512
                convert_to_tensor=True,
                show_progress_bar=False
            ).cpu().numpy().astype('float32')

            self.index.reset()
            self.index.add(embeddings)
            self.chunks = all_chunks
            return f"✅ Processed {len(all_chunks)} chunks from {len(files)} files"
        except Exception as e:
            print(f"Embedding Error: {str(e)}")
            return f"Error: {str(e)}"

    def query(self, question):
        if not self.chunks:
            return "Please process documents first", False

        try:
            print("\n" + "="*40 + " QUERY PROCESSING " + "="*40)
            print(f"Question: {question}")

            # Generate embedding for the question
            question_embedding = MODEL.encode([question], convert_to_tensor=True).cpu().numpy().astype('float32')

            # Search FAISS index
            _, indices = self.index.search(question_embedding, 3)
            print(f"Top indices: {indices}")

            # Get context from top chunks
            context = "\n".join([self.chunks[i] for i in indices[0] if i < len(self.chunks)])
            print(f"Context length: {len(context)} characters")

            # API Call with error handling
            headers = {
                "Authorization": f"Bearer {GROQ_API_KEY}",
                "Content-Type": "application/json"
            }

            payload = {
                "messages": [{
                    "role": "user",
                    "content": f"Answer concisely: {question}\nContext: {context}"
                }],
                "model": "mixtral-8x7b-32768",
                "temperature": 0.3,
                "max_tokens": MAX_TOKENS,
                "stream": True
            }

            response = requests.post(
                "https://api.groq.com/openai/v1/chat/completions",
                headers=headers,
                json=payload,
                timeout=20
            )

            print(f"API Status Code: {response.status_code}")  # Debug

            if response.status_code != 200:
                return f"API Error: {response.text}", False

            full_answer = []
            for chunk in response.iter_lines():
                if chunk:
                    try:
                        decoded = chunk.decode('utf-8').strip()
                        if decoded.startswith('data:'):
                            data = json.loads(decoded[5:])
                            if content := data.get('choices', [{}])[0].get('delta', {}).get('content', ''):
                                full_answer.append(content)
                    except Exception as e:
                        print(f"Chunk Error: {str(e)}")
                        continue

            final_answer = ''.join(full_answer)
            print(f"Final Answer: {final_answer}")  # Debug
            return final_answer, True

        except Exception as e:
            print(f"Query Error: {str(e)}")  # Debug
            return f"Error: {str(e)}", False

# Initialize processor
processor = DocumentProcessor()

# Gradio interface with improved error handling
def ask_question(question, chat_history=''):
    if not question.strip():
        return chat_history + [("", "Please enter a valid question")]

    answer, success = processor.query(question)
    return chat_history + [(question, answer)]

with gr.Blocks(title="RAG System") as app:
    gr.Markdown("## 🚀 Multi-Format-Reader Chat-Bot")
    with gr.Row():
        files = gr.File(file_count="multiple",
                      file_types=[".pdf", ".docx", ".txt", ".pptx", ".xls", ".xlsx", ".csv"],
                      label="Upload Documents")
        process_btn = gr.Button("Process", variant="primary")
    status = gr.Textbox(label="Processing Status", interactive=False)
    chatbot = gr.Chatbot(height=500, label="Chat History")
    with gr.Row():
        question = gr.Textbox(label="Your Query",
                            placeholder="Enter your question...",
                            max_lines=3)
        ask_btn = gr.Button("Ask", variant="primary")
    clear_btn = gr.Button("Clear Chat")

    process_btn.click(
        fn=processor.process_documents,
        inputs=files,
        outputs=status
    )

    ask_btn.click(
        fn=ask_question,
        inputs=[question, chatbot],
        outputs=chatbot
    ).then(lambda: "", None, question)  # Clear input after submission

    clear_btn.click(
        fn=lambda: [],
        inputs=None,
        outputs=chatbot
    )

app.launch(share=True, debug=True)