Create app.py

app.py

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+import gradio as gr
+from langchain.text_splitter import CharacterTextSplitter
+from sentence_transformers import SentenceTransformer
+import faiss
+from PyPDF2 import PdfReader
+from docx import Document
+from transformers import pipeline  # Hugging Face for summarization
+
+# Initialize Sentence Transformer for embeddings
+model = SentenceTransformer('all-MiniLM-L6-v2')
+
+# Vector Store (FAISS)
+dimension = 384  # Embedding size
+index = faiss.IndexFlatL2(dimension)
+
+# Initialize Hugging Face summarization model
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+
+# Function to extract text from PDFs
+def extract_text_from_pdf(file_path):
+    reader = PdfReader(file_path)
+    text = ""
+    for page in reader.pages:
+        text += page.extract_text()
+    return text
+
+# Function to extract text from DOCX
+def extract_text_from_docx(file_path):
+    doc = Document(file_path)
+    text = ""
+    for paragraph in doc.paragraphs:
+        text += paragraph.text + "\n"
+    return text
+
+# Function to process files
+def process_files(files):
+    texts = []
+    for file in files:
+        if file.name.endswith('.pdf'):
+            texts.append(extract_text_from_pdf(file.name))
+        elif file.name.endswith('.docx'):
+            texts.append(extract_text_from_docx(file.name))
+    return texts
+
+# Function to tokenize and chunk text
+def chunk_text(text, chunk_size=500, overlap=50):
+    text_splitter = CharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=overlap)
+    return text_splitter.split_text(text)
+
+# Function to create embeddings and populate FAISS index
+def create_embeddings_and_store(chunks):
+    global index
+    # Reset the FAISS index before adding new embeddings
+    index = faiss.IndexFlatL2(dimension)
+    for chunk in chunks:
+        embedding = model.encode([chunk])
+        embedding = embedding.astype('float32')  # Ensure embedding is in correct format
+        index.add(embedding)
+
+# Function for summarizing the text before sending
+def summarize_text(text):
+    summary = summarizer(text, max_length=300, min_length=100, do_sample=False)
+    return summary[0]['summary_text']
+
+# Function to dynamically truncate context to fit the Groq API's token limit
+def truncate_context(context, max_tokens=4000):  # Adjust max_tokens based on Groq's limits
+    if len(context) > max_tokens:
+        context = context[:max_tokens]  # Truncate context to fit within the token limit
+    return context
+
+# Function to query Groq with context and question
+def query_groq(question, context):
+    try:
+        if not question.strip():
+            return "Error: Question is empty or invalid."
+        if not context.strip():
+            return "Error: No context available from the uploaded documents."
+
+        # Dynamically truncate context to fit within the token limit
+        max_context_tokens = 4000  # Groq's token limit for context
+        context = truncate_context(context, max_tokens=max_context_tokens)
+
+        # Query Groq API with the truncated context
+        chat_completion = client.chat.completions.create(
+            messages=[{"role": "system", "content": "You are a helpful assistant. Use the context provided to answer the question."},
+                      {"role": "assistant", "content": context},
+                      {"role": "user", "content": question}],
+            model="llama3-8b-8192", stream=False)
+        if chat_completion and chat_completion.choices:
+            return chat_completion.choices[0].message.content
+        else:
+            return "Error: Received an unexpected response from Groq API."
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# Function to handle RAG pipeline
+def rag_pipeline(files, question, summarize_before_sending=False):
+    try:
+        if not files:
+            return "Error: No files uploaded. Please upload at least one document."
+
+        # Process uploaded files
+        texts = process_files(files)
+        if not texts:
+            return "Error: Could not extract text from the uploaded files."
+
+        # Combine all extracted text into a single context
+        combined_text = " ".join(texts)
+
+        if summarize_before_sending:
+            # Summarize the text to reduce token count
+            combined_text = summarize_text(combined_text)
+
+        # Ensure the combined text is within Groq's token limit
+        max_text_size = 4000  # Adjust based on Groq's token limits
+        combined_text = truncate_context(combined_text, max_tokens=max_text_size)
+
+        # Chunk and create embeddings
+        chunks = chunk_text(combined_text)
+        create_embeddings_and_store(chunks)
+
+        # Query Groq LLM with context and question
+        answer = query_groq(question, combined_text)
+        return answer
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# Enhanced UI with modern and clean style
+with gr.Blocks() as app:
+    with gr.Row():
+        # Left Column for instructions
+        with gr.Column(scale=1, min_width=250):
+            gr.Markdown("""
+            <div style="background: linear-gradient(145deg, #6e7dff, #1c2b58); padding: 30px; border-radius: 12px; box-shadow: 0 5px 15px rgba(0, 0, 0, 0.1); font-family: 'Roboto', sans-serif;">
+                <h2 style="color: #fff; font-size: 32px; font-weight: bold;">DocAI: Document Assistant</h2>
+                <p style="color: #ddd; font-size: 18px;">Welcome to DocAI! Upload your documents and get intelligent answers based on their content.</p>
+                <p style="color: #ddd; font-size: 16px; line-height: 1.6;"><strong>Steps to use:</strong></p>
+                <ul style="color: #ddd; font-size: 16px; line-height: 1.6;">
+                    <li>Upload your PDF or DOCX files.</li>
+                    <li>Ask questions related to the document.</li>
+                    <li>Enable "Summarize Before Sending" for a brief summary of the document.</li>
+                    <li>Click "Submit" to get your answers.</li>
+                </ul>
+                <p style="color: #ddd; font-size: 16px; line-height: 1.6;">Upload multiple files and get answers based on their contents.</p>
+            </div>
+            """)
+
+        # Right Column for the main application content
+        with gr.Column(scale=2, min_width=600):
+            gr.Markdown("""
+            <div style="background: linear-gradient(135deg, #6e7dff, #1c2b58); padding: 20px; border-radius: 15px; box-shadow: 0 5px 15px rgba(0, 0, 0, 0.2); font-family: 'Roboto', sans-serif;">
+                <h2 style="color: #fff; font-size: 36px; font-weight: bold; text-align: center; letter-spacing: 2px; text-transform: uppercase;">
+                    Ask Your Document
+                </h2>
+                <p style="color: #ddd; font-size: 18px; text-align: center; line-height: 1.6;">
+                    Get intelligent answers based on the content of your uploaded documents. Just ask a question!
+                </p>
+            </div>
+            """)
+
+            # File input
+            file_input = gr.File(
+                label="Upload Documents (PDF/DOCX)", 
+                file_types=[".pdf", ".docx"], 
+                file_count="multiple", 
+                interactive=True
+            )
+
+            # Question input
+            question_input = gr.Textbox(
+                label="Ask a question", 
+                placeholder="Type your question here...", 
+                interactive=True, 
+                lines=2, 
+                max_lines=4
+            )
+
+            # Summarize before sending checkbox
+            summarize_before_input = gr.Checkbox(
+                label="Summarize Before Sending", 
+                value=False
+            )
+
+            # Output text box with enhanced styling
+            output = gr.Textbox(
+                label="Answer from LLM", 
+                interactive=False, 
+                lines=4, 
+                max_lines=6
+            )
+            
+            # Submit button with icon and modern styling
+            submit_button = gr.Button("Submit", icon="send")
+
+            # Loading spinner
+            with gr.Row():
+                with gr.Column(scale=1, min_width=250):
+                    gr.Markdown("<div style='font-size: 14px; color: #555;'>Your answer will appear here...</div>")
+            
+            # Apply the logic for the button to trigger the RAG pipeline
+            submit_button.click(rag_pipeline, inputs=[file_input, question_input, summarize_before_input], outputs=output)
+
+# Launch the app
+app.launch()