Update app.py

app.py

@@ -4,7 +4,7 @@ from googletrans import Translator
 import gradio as gr
 
 # Load a Pre-trained Medical Question Answering Model
-model_name = "deepset/bert-base-cased-squad2"
+model_name = "deepset/roberta-base-squad2"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForQuestionAnswering.from_pretrained(model_name)
 
@@ -28,9 +28,12 @@ def translate_to_bangla(text):
 def chatbot_response(user_input):
     """Generates a response based on the medical question-answering model."""
     
+    print(f"User Input (Bangla): {user_input}")
+
     translated_input = translate_to_english(user_input)  # Translate to English
+    print(f"Translated Input (English): {translated_input}")
 
-    # Define a fixed context for medical information
+    # Define a fixed medical context
     context = """
     Fever can be caused by infections, dehydration, or viral illnesses. 
     It is recommended to rest, stay hydrated, and take paracetamol if necessary. 
@@ -43,14 +46,18 @@ def chatbot_response(user_input):
     with torch.no_grad():
         outputs = model(**inputs)
 
-    # Extract the most relevant answer
+    # Extract answer span
     answer_start = outputs.start_logits.argmax()
     answer_end = outputs.end_logits.argmax() + 1
     response_text = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(inputs["input_ids"][0][answer_start:answer_end]))
 
-    # Translate the response back to Bangla
+    print(f"Generated Response (English): {response_text}")
+
+    # Translate response back to Bangla
     translated_response = translate_to_bangla(response_text)
 
+    print(f"Final Response (Bangla): {translated_response}")
+
     return translated_response
 
 # Create Gradio Interface