Update app.py

app.py

@@ -1,22 +1,58 @@
 import gradio as gr
-from fine_tuning import load_and_train, generate_answer
+from concurrent.futures import ThreadPoolExecutor
+import fine_tuned
+from fine_tuned import load_and_train
 
-# Load and train model
+# -----------------------------
+# Load fine-tuned model
+# -----------------------------
 model, tokenizer, device = load_and_train()
 
-# Wrap for Gradio
-def gradio_generate(prompt, max_tokens):
-    return generate_answer(model, tokenizer, device, prompt, max_tokens)
+# -----------------------------
+# RAG Backend (Stub Example)
+# -----------------------------
+def generate_answer_rag(prompt, max_tokens=200):
+    """
+    Replace this stub with your actual RAG pipeline.
+    For now, just a dummy response.
+    """
+    return f"[RAG answer for]: {prompt[:50]}..."
 
+
+# -----------------------------
+# Combined Answer Function
+# -----------------------------
+def combined_generate(prompt, max_tokens):
+    with ThreadPoolExecutor() as executor:
+        # Submit both tasks in parallel
+        ft_future = executor.submit(fine_tuned.generate_answer, model, tokenizer, device, prompt, max_tokens)
+        rag_future = executor.submit(generate_answer_rag, prompt, max_tokens)
+
+        fine_tuned_answer = ft_future.result()
+        rag_answer = rag_future.result()
+
+    return {
+        "Fine-tuned Model Answer": fine_tuned_answer,
+        "RAG Answer": rag_answer
+    }
+
+
+# -----------------------------
+# Gradio Interface
+# -----------------------------
 iface = gr.Interface(
-    fn=gradio_generate,
+    fn=combined_generate,
     inputs=[
         gr.Textbox(label="Enter your question:", lines=5, placeholder="Type your question here..."),
         gr.Slider(minimum=50, maximum=500, step=10, value=200, label="Max tokens to generate")
     ],
-    outputs=gr.Textbox(label="Generated Answer"),
-    title="Chat with My Fine-Tuned Model 🤖",
-    description="This app allows you to ask questions about MakeMyTrip's financial data."
+    outputs=[
+        gr.Textbox(label="Fine-tuned Model Answer"),
+        gr.Textbox(label="RAG Answer")
+    ],
+    title="Compare Fine-tuned Model vs RAG 🤖📚",
+    description="Ask a question and get answers from both the fine-tuned model and the RAG pipeline."
 ).queue()
 
 iface.launch()
+