Update app.py

app.py

@@ -1,24 +1,19 @@
 
-from transformers import pipeline, Conversation
-import gradio as gr
+from transformers import AutoTokenizer, AutoModelForQuestionAnswering
+import torch
 
+tokenizer = AutoTokenizer.from_pretrained("emilyalsentzer/Bio_ClinicalBERT")
+model = AutoModelForQuestionAnswering.from_pretrained("emilyalsentzer/Bio_ClinicalBERT")
 
-chatbot = pipeline(model="emilyalsentzer/Bio_ClinicalBERT", task="conversational")
+def answer_question(context, question):
+    inputs = tokenizer.encode_plus(question, context, return_tensors="pt")
+    answer_start_scores, answer_end_scores = model(**inputs).values()
 
-message_list = []
-response_list = []
+    start = torch.argmax(answer_start_scores)
+    end = torch.argmax(answer_end_scores) + 1
 
-def Medical_Bot(message, history):
-    global message_list, response_list
-    message_list.append(message)
-    conversation = Conversation(text=message, past_user_inputs=message_list[:-1], generated_responses=response_list)
-    
-    conversation = chatbot(conversation)
+    answer = tokenizer.convert_tokens_to_string(
+        tokenizer.convert_ids_to_tokens(inputs["input_ids"][0][start:end])
+    )
+    return answer
 
-    reply = conversation.generated_responses[-1]
-    response_list.append(reply)
-    return reply
-
-demo_chatbot = gr.ChatInterface(Medical_Bot, title="Medical Bot", description="Enter text to start chatting.")
-
-demo_chatbot.launch()