app.py

# -*- coding: utf-8 -*-
"""final_app
Automatically generated by Colab.
Original file is located at
    https://colab.research.google.com/drive/1pG3uDsJzglvQecdTcY76aXa5ObFadRux
"""

# !pip install gradio langchain langchain-community langchain-huggingface langchain-groq faiss-cpu sentence-transformers pypdf



import gradio as gr
import os
import tempfile
from langchain_community.document_loaders import PyPDFLoader
from langchain_community.vectorstores import FAISS
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_groq import ChatGroq
from langchain.chains import RetrievalQA
from langchain.prompts import PromptTemplate

# Groq API Key
GROQ_API_KEY = "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"
os.environ["GROQ_API_KEY"] = GROQ_API_KEY

# Global variables to store vectorstore and processed files
vectorstore = None
processed_files_list = []

def process_pdfs(files):
    """Process uploaded PDF files and create vector store"""
    global vectorstore, processed_files_list

    if not files:
        return "⚠️ Please upload at least one PDF file", ""

    try:
        all_documents = []
        processed_names = []

        # Process each uploaded PDF
        for file in files:
            # Load PDF
            loader = PyPDFLoader(file.name)
            documents = loader.load()
            all_documents.extend(documents)
            processed_names.append(os.path.basename(file.name))

        if not all_documents:
            return "❌ No content extracted from PDFs", ""

        # Split documents into chunks
        text_splitter = RecursiveCharacterTextSplitter(
            chunk_size=1000,
            chunk_overlap=200,
            length_function=len
        )
        splits = text_splitter.split_documents(all_documents)

        # Create embeddings
        embeddings = HuggingFaceEmbeddings(
            model_name="sentence-transformers/all-MiniLM-L6-v2",
            model_kwargs={'device': 'cpu'}
        )

        # Create vector store
        vectorstore = FAISS.from_documents(splits, embeddings)
        processed_files_list = processed_names

        success_msg = f"✅ Successfully processed {len(files)} document(s)!\n"
        success_msg += f"📊 Created {len(splits)} text chunks for retrieval\n\n"
        success_msg += "📄 Processed files:\n" + "\n".join([f"  • {name}" for name in processed_names])

        return success_msg, "✅ Documents processed! You can now ask questions."

    except Exception as e:
        return f"❌ Error processing documents: {str(e)}", ""

def answer_question(question, chat_history):
    """Answer questions based on the processed documents"""
    global vectorstore

    if not vectorstore:
        return chat_history + [[question, "⚠️ Please upload and process PDF documents first!"]]

    if not question or question.strip() == "":
        return chat_history + [[question, "⚠️ Please enter a valid question."]]

    try:
        # Initialize LLM with stricter temperature for factual answers
        llm = ChatGroq(
            model="llama-3.1-8b-instant",
            temperature=0,  # Set to 0 for most deterministic, factual responses
            max_tokens=1024,
            api_key=GROQ_API_KEY
        )

        # Create custom prompt with strict context-only answering
        prompt_template = """You are a helpful assistant that answers questions ONLY based on the provided context from uploaded PDF documents.
CRITICAL INSTRUCTIONS:
- Answer ONLY if the information is present in the context below
- If the context does not contain relevant information to answer the question, you MUST respond with: "I don't know the answer. This information is not available in the uploaded documents."
- DO NOT use any external knowledge or information not present in the context
- DO NOT make assumptions or inferences beyond what is explicitly stated in the context
- If you're unsure whether the context contains the answer, say you don't know
Context from uploaded documents:
{context}
Question: {question}
Answer (only from the context above):"""

        PROMPT = PromptTemplate(
            template=prompt_template,
            input_variables=["context", "question"]
        )

        # Create retrieval chain with enhanced retrieval settings
        qa_chain = RetrievalQA.from_chain_type(
            llm=llm,
            chain_type="stuff",
            retriever=vectorstore.as_retriever(
                search_type="similarity",
                search_kwargs={
                    "k": 5,  # Retrieve top 5 most relevant chunks
                    "fetch_k": 20  # Fetch more candidates before filtering
                }
            ),
            chain_type_kwargs={"prompt": PROMPT},
            return_source_documents=True
        )

        # Get response
        result = qa_chain({"query": question})
        answer = result['result']
        source_docs = result.get('source_documents', [])

        # Add source information if available
        if source_docs and "don't know" not in answer.lower():
            answer += "\n\n📌 **Sources found in documents:**"
            unique_sources = set()
            for doc in source_docs[:3]:  # Show top 3 sources
                source = doc.metadata.get('source', 'Unknown')
                page = doc.metadata.get('page', 'Unknown')
                source_id = f"{source} (Page {page})"
                if source_id not in unique_sources:
                    unique_sources.add(source_id)

            for source in unique_sources:
                answer += f"\n  • {source}"

        # Update chat history
        chat_history = chat_history + [[question, answer]]

        return chat_history

    except Exception as e:
        error_msg = f"❌ Error generating answer: {str(e)}"
        return chat_history + [[question, error_msg]]

def clear_data():
    """Clear all processed data"""
    global vectorstore, processed_files_list
    vectorstore = None
    processed_files_list = []
    return "🗑️ All data cleared. Please upload new documents.", "", []

# Custom CSS for better styling
custom_css = """
#title {
    text-align: center;
    background: linear-gradient(90deg, #667eea 0%, #764ba2 100%);
    -webkit-background-clip: text;
    -webkit-text-fill-color: transparent;
    font-size: 2.5em;
    font-weight: bold;
    margin-bottom: 10px;
}
#subtitle {
    text-align: center;
    color: #666;
    font-size: 1.2em;
    margin-bottom: 20px;
}
.gradio-container {
    max-width: 1200px !important;
    margin: auto !important;
}
"""

# Create Gradio interface
with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
    # Header
    gr.HTML("<h1 id='title'>📚 Slashbyte RAG</h1>")
    gr.HTML("<p id='subtitle'>Upload PDFs and ask questions using AI-powered retrieval</p>")

    with gr.Row():
        # Left column - Document Upload
        with gr.Column(scale=1):
            gr.Markdown("### 📄 Document Upload")
            file_upload = gr.File(
                label="Upload PDF Documents",
                file_types=[".pdf"],
                file_count="multiple"
            )
            process_btn = gr.Button("🔄 Process Documents", variant="primary", size="lg")
            process_output = gr.Textbox(
                label="Processing Status",
                lines=8,
                interactive=False
            )
            clear_btn = gr.Button("🗑️ Clear All Data", variant="stop")

            gr.Markdown("""
            ---
            ### ℹ️ How to Use
            1. **Upload PDFs** using the file uploader
            2. Click **Process Documents**
            3. **Ask questions** in the chat
            4. Get **AI-powered answers**
            **Features:**
            - 📄 Multiple PDF support
            - 🤖 Powered by Groq LLM
            - 🔍 Semantic search
            - 💾 Chat history
            """)

        # Right column - Chat Interface
        with gr.Column(scale=2):
            gr.Markdown("### 💬 Ask Questions")
            status_text = gr.Textbox(
                label="Status",
                value="⚠️ Upload and process documents to start",
                interactive=False
            )
            chatbot = gr.Chatbot(
                label="Chat History",
                height=400,
                show_label=True
            )
            with gr.Row():
                question_input = gr.Textbox(
                    label="Your Question",
                    placeholder="Ask anything about your documents...",
                    scale=4
                )
                submit_btn = gr.Button("🚀 Ask", variant="primary", scale=1)

            clear_chat_btn = gr.Button("🧹 Clear Chat")

    # Footer
    gr.HTML("""
        <div style='text-align: center; color: #666; padding: 20px; margin-top: 20px; border-top: 1px solid #ddd;'>
            <p>Powered by Langchain, Groq, and HuggingFace | Built with ❤️ using Gradio</p>
        </div>
    """)

    # Event handlers
    process_btn.click(
        fn=process_pdfs,
        inputs=[file_upload],
        outputs=[process_output, status_text]
    )

    submit_btn.click(
        fn=answer_question,
        inputs=[question_input, chatbot],
        outputs=[chatbot]
    ).then(
        lambda: "",
        outputs=[question_input]
    )

    question_input.submit(
        fn=answer_question,
        inputs=[question_input, chatbot],
        outputs=[chatbot]
    ).then(
        lambda: "",
        outputs=[question_input]
    )

    clear_chat_btn.click(
        fn=lambda: [],
        outputs=[chatbot]
    )

    clear_btn.click(
        fn=clear_data,
        outputs=[process_output, status_text, chatbot]
    )

# Launch the app
if __name__ == "__main__":
    demo.launch(
        share=True,
        server_name="0.0.0.0",
        server_port=7860
    )