Update app.py

app.py

@@ -7,20 +7,17 @@ model_name = "NinaMwangi/T5_finbot"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = TFAutoModelForSeq2SeqLM.from_pretrained(model_name)
 
-# Load dataset for context retrieval
+# Load dataset
 dataset = load_dataset("virattt/financial-qa-10K")["train"]
 
-# Global chat history
-chat_history = []
-
-# Context lookup
+# Function to retrieve context
 def get_context_for_question(question):
     for item in dataset:
         if item["question"].strip().lower() == question.strip().lower():
             return item["context"]
     return "No relevant context found."
 
-# Inference function
+# Predict function
 def generate_answer(question):
     context = get_context_for_question(question)
     prompt = f"Q: {question} Context: {context} A:"
@@ -41,37 +38,15 @@ def generate_answer(question):
     )
 
     answer = tokenizer.decode(outputs[0], skip_special_tokens=True).strip()
-    chat_history.append((question, answer))
-    return "", chat_history
-
-# Clear history function
-def clear_chat():
-    global chat_history
-    chat_history = []
-    return chat_history
-
-# Gradio UI
-with gr.Blocks(theme=gr.themes.Base()) as interface:
-    gr.Markdown(
-        """
-        # 💬 Finance QA Chatbot
-        Ask a finance-related question and get an accurate, concise response.
-        Built using a fine-tuned T5 Transformer on financial Q&A data.
-        """
-    )
-
-    chatbot = gr.Chatbot(label="Finance Chatbot", height=400, bubble_full_width=False)
-
-    with gr.Row():
-        with gr.Column(scale=8):
-            question_box = gr.Textbox(placeholder="Ask a finance question...", show_label=False, lines=2)
-        with gr.Column(scale=1):
-            submit_btn = gr.Button("Send")
-
-    clear_btn = gr.Button("Clear Chat")
-
-    submit_btn.click(fn=generate_answer, inputs=question_box, outputs=[question_box, chatbot])
-    clear_btn.click(fn=clear_chat, outputs=chatbot)
-
-# Launch app
-interface.launch()
+    return answer
+
+# Interface
+interface = gr.Interface(
+    fn=generate_answer,
+    inputs=gr.Textbox(lines=2, placeholder="Ask a finance question..."),
+    outputs="text",
+    title="Finance QA Chatbot",
+    description="Built using a fine-tuned T5 Transformer. Ask a finance-related question and get an accurate, concise answer."
+)
+
+interface.launch()