Update app.py

app.py

@@ -30,23 +30,14 @@ def askme(symptoms, question):
     # Remove system messages and content    
     # Extract and return the generated text, removing the prompt
     # Extract only the assistant's response
-    answer = response_text.split('<|im_start|>assistant')[-1].strip()
-    #answer =response_text.split("assistant")[1].strip().split("user")[0].strip()   
-    return answer
-
-
-def askmeold(symptoms, question):
-    template = [
-        {"role": "system", "content": "You are an AI Medical Assistant trained on a vast dataset of health information. Please be thorough and provide an informative answer. If you don't know the answer to a specific medical inquiry, advise seeking professional help."},
-        {"role": "user", "content": f"Symptoms: {symptoms}\nQuestion: {question}\n"},
-        {"role": "assistant", "content": "{assistant_response}\n"}
-    ]
-
-    prompt = tokenizer.apply_chat_template(template, tokenize=False, add_generation_prompt=True)
-    inputs = tokenizer(prompt, return_tensors="pt").to(device)
-    outputs = model.generate(**inputs, max_new_tokens=300, use_cache=True)
-    response_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0].strip()
-    return response_text
+    #answer = response_text.split('<|im_start|>assistant')[-1].strip()  
+    start_idx = response_text.find("<|im_start|>assistant")
+    end_idx = response_text.find("<|im_end|>", start_idx)
+    assistant_response = response_text[start_idx + len("<|im_start|>assistant"):end_idx]
+
+    return assistant_response.split(". ")[0] + "
+
+
 
 
 # Example usage