Update app.py

app.py

@@ -5,7 +5,6 @@ os.system("pip install transformers gradio torch")
 
 import gradio as gr
 from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
-
 # Use a smaller model to reduce memory usage
 model_name = "distilgpt2"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
@@ -14,20 +13,52 @@ model = AutoModelForCausalLM.from_pretrained(model_name)
 # Initialize the pipeline with the model and tokenizer
 generator = pipeline("text-generation", model=model, tokenizer=tokenizer, device=-1)  # Use device=-1 for CPU
 
-def process_input(text):
+# In-memory storage for generated data
+generated_data = []
+
+def process_input(name, account_number, balance, query):
     try:
-        output = generator(text, max_length=50, do_sample=True)
-        return output[0]['generated_text']
+        # Format the query with user details
+        formatted_query = f"Name: {name}\nAccount Number: {account_number}\nBalance: {balance}\nQuery: {query}"
+        output = generator(formatted_query, max_length=150, do_sample=True)
+        response = output[0]['generated_text']
+        
+        # Save generated data
+        generated_data.append({
+            'name': name,
+            'account_number': account_number,
+            'balance': balance,
+            'query': query,
+            'response': response
+        })
+        
+        return response
     except Exception as e:
         return f"An error occurred: {e}"
 
+def view_history():
+    # Display stored generated data
+    if generated_data:
+        history = "\n\n".join([f"Name: {data['name']}\nAccount Number: {data['account_number']}\nBalance: {data['balance']}\nQuery: {data['query']}\nResponse: {data['response']}" for data in generated_data])
+        return history
+    return "No history available."
+
 # Set up the Gradio interface
-iface = gr.Interface(
-    fn=process_input,
-    inputs=gr.Textbox(label="Enter your query"),
-    outputs=gr.Textbox(label="Response"),
-    title="Interactive Bank Management App"
-)
+with gr.Blocks() as demo:
+    with gr.Row():
+        name = gr.Textbox(label="Enter your name")
+        account_number = gr.Textbox(label="Enter your account number")
+        balance = gr.Textbox(label="Enter your account balance")
+        query = gr.Textbox(label="Enter your query")
+        submit_button = gr.Button("Submit")
+    
+    response_output = gr.Textbox(label="Response")
+    history_button = gr.Button("View History")
+    history_output = gr.Textbox(label="Query History")
+
+    # Define the interactions
+    submit_button.click(process_input, inputs=[name, account_number, balance, query], outputs=response_output)
+    history_button.click(view_history, outputs=history_output)
 
 # Launch the Gradio app
-iface.launch()
+demo.launch()