Update app.py

app.py

@@ -5,9 +5,11 @@ from groq import Groq
 from sentence_transformers import SentenceTransformer
 from pypdf import PdfReader
 import gradio as gr
+import nest_asyncio
+
+nest_asyncio.apply()
 
 # -------------------- Configuration --------------------
-# Get API key from environment variable (set as a secret on Hugging Face)
 GROQ_API_KEY = os.getenv("GROQ_API_KEY")
 if not GROQ_API_KEY:
     raise ValueError("GROQ_API_KEY environment variable not set.")
@@ -17,7 +19,6 @@ embedding_model = SentenceTransformer("all-MiniLM-L6-v2")
 
 # -------------------- PDF processing --------------------
 def extract_text_from_pdf(pdf_file):
-    """Extract text from a PDF file."""
     reader = PdfReader(pdf_file)
     text = ""
     for page in reader.pages:
@@ -27,18 +28,15 @@ def extract_text_from_pdf(pdf_file):
     return text.strip()
 
 def chunk_text(text, chunk_size=800, overlap=150):
-    """Split text into overlapping chunks."""
     chunks = []
     start = 0
     while start < len(text):
         end = start + chunk_size
-        chunk = text[start:end]
-        chunks.append(chunk)
+        chunks.append(text[start:end])
         start = end - overlap
     return chunks
 
 def create_faiss_index(chunks):
-    """Build a FAISS index from text chunks."""
     embeddings = embedding_model.encode(chunks)
     embeddings = np.array(embeddings).astype("float32")
     dimension = embeddings.shape[1]
@@ -47,7 +45,6 @@ def create_faiss_index(chunks):
     return index
 
 def retrieve(query, index, chunks, top_k=3):
-    """Retrieve most relevant chunks for a query."""
     query_embedding = embedding_model.encode([query])
     query_embedding = np.array(query_embedding).astype("float32")
     distances, indices = index.search(query_embedding, top_k)
@@ -58,7 +55,6 @@ def retrieve(query, index, chunks, top_k=3):
     return retrieved
 
 def ask_llama(context, question):
-    """Query Groq's Llama model with context."""
     prompt = f"""
 You are a pharmaceutical expert assistant.
 
@@ -81,12 +77,11 @@ Question:
     except Exception as e:
         return f"Groq API Error: {str(e)}"
 
-# -------------------- Global state (for demo simplicity) --------------------
+# -------------------- Global state --------------------
 index = None
 chunks = None
 
 def process_pdf(pdf):
-    """Upload and index a PDF."""
     global index, chunks
     if pdf is None:
         return "⚠️ Please upload a PDF."
@@ -98,7 +93,6 @@ def process_pdf(pdf):
     return "✅ Document processed successfully!"
 
 def chatbot(question):
-    """Answer a question using the indexed document."""
     global index, chunks
     if index is None:
         return "⚠️ Please upload and process a PDF first."
@@ -136,7 +130,7 @@ textarea {
 }
 """
 
-with gr.Blocks(css=custom_css) as demo:
+with gr.Blocks() as demo:
     gr.Markdown("""
     <h1 style='text-align:center; font-size:48px; font-weight:800;
                background: linear-gradient(90deg,#ff6a00,#ee0979,#667eea);
@@ -165,11 +159,9 @@ with gr.Blocks(css=custom_css) as demo:
             label="🧠 AI Answer",
             lines=18,
             max_lines=30,
-            show_copy_button=True,
             interactive=False
         )
         ask_button.click(fn=chatbot, inputs=question_input, outputs=answer_output)
 
-# Launch the app (Hugging Face Spaces will call demo.launch() automatically)
-if __name__ == "__main__":
-    demo.launch()
+# Launch with custom CSS (Gradio 6+ compatible)
+demo.launch(css=custom_css)