Update app.py

app.py

@@ -99,16 +99,17 @@
 #     iface.launch()
 
 
+
+
 import gradio as gr
 from pinecone import Pinecone
 from sentence_transformers import SentenceTransformer
 from openai import AzureOpenAI
-from huggingface_hub import login as hf_login
 import cohere
 import os
 from dotenv import load_dotenv
 
-load_dotenv()  # Load keys from .env file
+load_dotenv()
 
 # === ENVIRONMENT VARIABLES ===
 AZURE_OPENAI_KEY = os.getenv("AZURE_OPENAI_KEY")
@@ -138,6 +139,7 @@ llm_client = AzureOpenAI(
     azure_endpoint=AZURE_OPENAI_ENDPOINT
 )
 
+# === Core Function ===
 def run_rag_query(query: str, model_choice: str) -> str:
     if model_choice not in EMBED_INDEXES:
         return f"Invalid model selected. Choose from {list(EMBED_INDEXES.keys())}"
@@ -177,7 +179,7 @@ Answer:"""
     )
     answer = response.choices[0].message.content
 
-    # Include source in retrieved chunks
+    # Add source to results
     top_matches = "\n\n".join([
         f"**Rank {i+1}:**\n"
         f"**Source:** {m.metadata.get('source', 'N/A')}\n"
@@ -187,12 +189,13 @@ Answer:"""
 
     return f"### ✅ Answer:\n{answer}\n\n---\n### 📄 Top Retrieved Chunks:\n{top_matches}"
 
-# Gradio UI
+
+# === Gradio UI ===
 with gr.Blocks(title="QWEN vs COHERE RAG App") as iface:
     gr.Markdown("## 📚 QWEN vs COHERE RAG App")
     gr.Markdown("Ask a question and retrieve contextual answers from your embedded documents.\n"
                 "[📁 View PDF Files](https://drive.google.com/drive/folders/1fq-PyNptFg20cknkzNrmW6Tev-869RY9?usp=sharing)")
-    
+
     with gr.Row():
         query = gr.Textbox(label="Enter your query", lines=2, scale=3)
         model_choice = gr.Radio(["cohere", "qwen"], label="Choose embedding model", scale=1)
@@ -201,17 +204,8 @@ with gr.Blocks(title="QWEN vs COHERE RAG App") as iface:
 
     submit_btn = gr.Button("🔍 Run Query")
 
-    # Add spinner and action
-    with gr.Row():
-        status = gr.Markdown("")
-
-    def wrapped_run(query, model_choice):
-        status.update("⏳ Running... please wait")
-        result = run_rag_query(query, model_choice)
-        status.update("✅ Done")
-        return result
-
-    submit_btn.click(fn=wrapped_run, inputs=[query, model_choice], outputs=output)
+    # Spinner: Gradio shows spinner automatically while fn= is running
+    submit_btn.click(fn=run_rag_query, inputs=[query, model_choice], outputs=output)
 
 if __name__ == "__main__":
     iface.launch()