Update app.py

removed gr.state

app.py

@@ -1,27 +1,30 @@
 import gradio as gr
-print("Gradio version:", gr.__version__)
 from datasets import load_dataset
 from transformers import AutoTokenizer, TFAutoModelForSeq2SeqLM
 
+# Load model and tokenizer
 model_name = "NinaMwangi/T5_finbot"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = TFAutoModelForSeq2SeqLM.from_pretrained(model_name)
 
+# Load dataset for context retrieval
 dataset = load_dataset("virattt/financial-qa-10K")["train"]
 
-# Function to retrieve matching context
+# Global chat history
+chat_history = []
+
+# Context lookup
 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."
 
-# Define the prediction function (inference)
-def generate_answer(question, chat_history):
+# Inference function
+def generate_answer(question):
     context = get_context_for_question(question)
     prompt = f"Q: {question} Context: {context} A:"
 
-
     inputs = tokenizer(
         prompt,
         return_tensors="tf",
@@ -41,37 +44,34 @@ def generate_answer(question, chat_history):
     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
-            )
+            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")
 
-    # Chat state
-    state = gr.State([])
-
-    # Bind function
-    submit_btn.click(
-    fn=generate_answer,
-    inputs=[question_box, state],
-    outputs=[question_box, chatbot, state]
-)
-
-    clear_btn.click(lambda: [], inputs=[], outputs=[chatbot, state])
+    submit_btn.click(fn=generate_answer, inputs=question_box, outputs=[question_box, chatbot])
+    clear_btn.click(fn=clear_chat, outputs=chatbot)
 
-# Run app
-interface.launch(share=True)
+# Launch app
+interface.launch()