Update main.py

main.py

@@ -23,7 +23,7 @@ tokenizer = AutoTokenizer.from_pretrained("Open-Orca/OpenOrca-Platypus2-13B", tr
 def ask_bot(question):
   input_ids = tokenizer.encode(question, return_tensors="pt").to(device)
   with torch.no_grad():
-      output = model.generate(input_ids, max_length=500, num_return_sequences=1, do_sample=True, top_k=50)
+      output = model.generate(input_ids, max_length=100, num_return_sequences=1, do_sample=True, top_k=50)
   generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
   response = generated_text.split("->:")[-1]
   return response
@@ -65,7 +65,7 @@ class CustomLLM(LLM):
 
         input_ids = tokenizer.encode(prompt, return_tensors="pt").to(device)
         with torch.no_grad():
-            output = model.generate(input_ids, max_length=500, num_return_sequences=1, do_sample=True, top_k=50)
+            output = model.generate(input_ids, max_length=100, num_return_sequences=1, do_sample=True, top_k=50)
         generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
         response = generated_text.split("->:")[-1]
         return response
@@ -156,13 +156,13 @@ def chatbot(patient_id, user_data: dict=None):
         human_input = prompt + user_input + " ->:"
       human_text =  user_input.replace("'", "")
       response = llm._call(human_input)
-      response = response.replace("'", "")
-      memory.save_context({"input": user_input}, {"output": response})
-      summary = memory.load_memory_variables({})
-      ai_text = response.replace("'", "")
-      memory.save_context({"input": user_input}, {"output": ai_text})
-      summary = memory.load_memory_variables({})
-      db.insert(("patient_id", "patient_text", "ai_text", "timestamp", "summarized_text"), (patient_id, human_text, ai_text, str(datetime.now()), summary['history'].replace("'", "")))
+      # response = response.replace("'", "")
+      # memory.save_context({"input": user_input}, {"output": response})
+      # summary = memory.load_memory_variables({})
+      # ai_text = response.replace("'", "")
+      # memory.save_context({"input": user_input}, {"output": ai_text})
+      # summary = memory.load_memory_variables({})
+      # db.insert(("patient_id", "patient_text", "ai_text", "timestamp", "summarized_text"), (patient_id, human_text, ai_text, str(datetime.now()), summary['history'].replace("'", "")))
       db.close_db()
       return {"response": response}
     finally: