Create app.py

app.py

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+import gradio as gr
+import fitz  # PyMuPDF
+import os
+from sentence_transformers import SentenceTransformer
+import numpy as np
+import faiss
+import uuid
+from groq import Groq
+
+# Load embedding model
+embedder = SentenceTransformer("all-MiniLM-L6-v2")
+
+# Initialize vector store and document store
+document_chunks = []
+doc_embeddings = []
+doc_ids = []
+index = None
+
+# Get Groq API key from environment variable
+GROQ_API_KEY = os.getenv("GROQ_API_KEY")
+
+client = Groq(api_key=GROQ_API_KEY)
+
+# Load and split PDF
+def extract_text_from_pdf(pdf_path):
+    doc = fitz.open(pdf_path)
+    text = ""
+    for page in doc:
+        text += page.get_text()
+    return text
+
+# Chunking logic
+def chunk_text(text, max_tokens=500):
+    import re
+    sentences = re.split(r'(?<=[.!?]) +', text)
+    chunks = []
+    chunk = ""
+    tokens = 0
+
+    for sentence in sentences:
+        sentence_tokens = len(sentence.split())
+        if tokens + sentence_tokens > max_tokens:
+            chunks.append(chunk.strip())
+            chunk = sentence
+            tokens = sentence_tokens
+        else:
+            chunk += " " + sentence
+            tokens += sentence_tokens
+
+    if chunk:
+        chunks.append(chunk.strip())
+    return chunks
+
+# Indexing
+def index_pdf(pdf_file):
+    global document_chunks, doc_embeddings, doc_ids, index
+
+    if not pdf_file:
+        return "❌ Please upload a PDF file."
+
+    text = extract_text_from_pdf(pdf_file.name)
+    document_chunks = chunk_text(text)
+
+    doc_embeddings = embedder.encode(document_chunks)
+    doc_embeddings = np.array(doc_embeddings).astype("float32")
+
+    dimension = doc_embeddings.shape[1]
+    index = faiss.IndexFlatL2(dimension)
+    index.add(doc_embeddings)
+
+    doc_ids = [str(uuid.uuid4()) for _ in range(len(document_chunks))]
+
+    return "✅ PDF indexed successfully. You can now ask questions."
+
+# Retrieve top chunks
+def retrieve_relevant_chunks(query, k=3):
+    query_embedding = embedder.encode([query]).astype("float32")
+    distances, indices = index.search(query_embedding, k)
+    return [document_chunks[i] for i in indices[0]]
+
+# Generate answer using Groq
+def generate_answer(user_query):
+    if index is None:
+        return "❌ Please upload and index a PDF first."
+
+    top_chunks = retrieve_relevant_chunks(user_query, k=3)
+    context = "\n\n".join(top_chunks)
+
+    messages = [
+        {"role": "system", "content": "You are a helpful academic assistant who answers questions based on uploaded PDF papers."},
+        {"role": "user", "content": f"Context: {context}\n\nQuestion: {user_query}"}
+    ]
+
+    try:
+        response = client.chat.completions.create(
+            messages=messages,
+            model="llama3-8b-8192",
+        )
+        return response.choices[0].message.content.strip()
+    except Exception as e:
+        return f"❌ Error generating response: {e}"
+
+# Gradio UI
+with gr.Blocks(title="📘 PDF Question Assistant") as demo:
+    gr.Markdown("# 📘 Ask Questions About Your PDF")
+    with gr.Tab("📄 Upload & Index"):
+        with gr.Row():
+            pdf_input = gr.File(label="Upload PDF File", type="filepath", file_types=[".pdf"])
+            upload_btn = gr.Button("🔍 Index PDF", variant="primary")
+        upload_status = gr.Textbox(label="", interactive=False, placeholder="Status will appear here...")
+
+    with gr.Tab("❓ Ask a Question"):
+        with gr.Row():
+            query = gr.Textbox(label="Ask something from the PDF", placeholder="E.g. What is the main argument of the paper?")
+            query_btn = gr.Button("🧠 Get Answer")
+        answer = gr.Textbox(label="Answer", placeholder="AI-generated answer will appear here...", lines=8)
+
+    upload_btn.click(fn=index_pdf, inputs=[pdf_input], outputs=[upload_status])
+    query_btn.click(fn=generate_answer, inputs=[query], outputs=[answer])
+
+if __name__ == "__main__":
+    demo.launch()