Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForQuestionAnswering, AutoTokenizer
+import torch
+
+# Load model and tokenizer
+MODEL_NAME = "your-hf-username/raft-qa"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForQuestionAnswering.from_pretrained(MODEL_NAME)
+
+def answer_question(context, question):
+    inputs = tokenizer(question, context, return_tensors="pt", truncation=True, max_length=512)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    
+    start_scores, end_scores = outputs.start_logits, outputs.end_logits
+    start_idx = torch.argmax(start_scores)
+    end_idx = torch.argmax(end_scores) + 1
+    answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(inputs["input_ids"][0][start_idx:end_idx]))
+    
+    return answer if answer.strip() else "No answer found."
+
+# Define UI
+with gr.Blocks(theme="soft") as demo:
+    gr.Markdown("# 🤖 RAFT: Retrieval-Augmented Fine-Tuning for QA")
+    gr.Markdown("Ask a question based on the provided context and see how RAFT improves response accuracy!")
+    
+    with gr.Row():
+        context_input = gr.Textbox(lines=5, label="Context", placeholder="Enter background text here...")
+        question_input = gr.Textbox(lines=2, label="Question", placeholder="What is the main idea?")
+    
+    answer_output = gr.Textbox(label="Answer", interactive=False)
+    
+    submit_btn = gr.Button("Generate Answer")
+    submit_btn.click(answer_question, inputs=[context_input, question_input], outputs=answer_output)
+
+demo.launch()