Update app.py

app.py

@@ -1,192 +1,44 @@
 import gradio as gr
-import os
-from groq import Groq
-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
 
-# Initialize Sentence Transformer for embeddings
-model = SentenceTransformer('all-MiniLM-L6-v2')
-client = Groq(api_key=os.getenv("groq_api_key"))
-# Vector Store (FAISS)
-dimension = 384  # Embedding size
-index = faiss.IndexFlatL2(dimension)
+def greet(name, intensity):
+    return f"Hello {name.upper()}!" * intensity
 
-# Initialize Hugging Face summarization model
-summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+with gr.Blocks(theme=gr.themes.Soft()) as demo:  # Use a built-in theme
+    gr.Markdown(
+        """
+        # Welcome to My Colorful Gradio App! 👋
+        This is a simple example demonstrating how to create a visually appealing Gradio interface.
+        """
+    )
 
-# 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
-    index = faiss.IndexFlatL2(dimension)
-    for chunk in chunks:
-        embedding = model.encode([chunk])
-        embedding = embedding.astype('float32')
-        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):
-    if len(context) > max_tokens:
-        context = context[:max_tokens]
-    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."
-
-        max_context_tokens = 4000
-        context = truncate_context(context, max_tokens=max_context_tokens)
-
-        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."
-
-        texts = process_files(files)
-        if not texts:
-            return "Error: Could not extract text from the uploaded files."
-
-        combined_text = " ".join(texts)
-
-        if summarize_before_sending:
-            combined_text = summarize_text(combined_text)
-
-        max_text_size = 4000
-        combined_text = truncate_context(combined_text, max_tokens=max_text_size)
-
-        chunks = chunk_text(combined_text)
-        create_embeddings_and_store(chunks)
-
-        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
-            output = gr.Textbox(
-                label="Answer from LLM", 
-                interactive=False, 
-                lines=4, 
-                max_lines=6
-            )
-
-            # Submit button
-            submit_button = gr.Button("Submit", icon="send")
-
-            # Apply the logic for the button to trigger the RAG pipeline
-            submit_button.click(rag_pipeline, inputs=[file_input, question_input], outputs=output)
-
-# Launch the app
-app.launch()
+        with gr.Column():
+            name = gr.Textbox(label="Enter your name", placeholder="Your name here")
+            intensity = gr.Slider(minimum=1, maximum=10, value=1, label="Intensity")
+            greet_btn = gr.Button("Greet Me!", variant="primary") # Use a primary button
+
+        with gr.Column():
+            output = gr.Textbox(label="Greeting Output", lines=4)
+
+    greet_btn.click(greet, inputs=[name, intensity], outputs=output)
+
+    gr.Examples(
+        examples=[
+            ["John Doe", 3],
+            ["Jane Smith", 1],
+            ["A Very Long Name", 5],
+        ],
+        inputs=[name, intensity],
+        outputs=output,
+        label="Try these examples:",
+    )
+
+    gr.Markdown(
+        """
+        ---
+        Created with ❤️ using Gradio.
+        """
+    )
+
+
+demo.launch()