Update app.py

app.py

@@ -72,17 +72,40 @@
 #             st.download_button(label=':blue[Download]',data=file,file_name=OP,mime="image/png") 
 #         st.success("Thanks for using the app !!!")
 
+# import torch
+# import streamlit as st
+# from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# #torch.set_default_device("cuda")
+
+# model = AutoModelForCausalLM.from_pretrained("soulhq-ai/phi-2-insurance_qa-sft-lora", torch_dtype="auto", trust_remote_code=True)
+# tokenizer = AutoTokenizer.from_pretrained("soulhq-ai/phi-2-insurance_qa-sft-lora", trust_remote_code=True)
+# i=st.text_input('Prompt', 'Life of Brian')
+# #inputs = tokenizer('''### Instruction: What Does Basic Homeowners Insurance Cover?\n### Response: ''', return_tensors="pt", return_attention_mask=False)
+# inputs = tokenizer(i, return_tensors="pt", return_attention_mask=False)
+# outputs = model.generate(**inputs, max_length=1024)
+# text = tokenizer.batch_decode(outputs)[0]
+# print(text)
+
 import torch
 import streamlit as st
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-#torch.set_default_device("cuda")
-
-model = AutoModelForCausalLM.from_pretrained("soulhq-ai/phi-2-insurance_qa-sft-lora", torch_dtype="auto", trust_remote_code=True)
-tokenizer = AutoTokenizer.from_pretrained("soulhq-ai/phi-2-insurance_qa-sft-lora", trust_remote_code=True)
-i=st.text_input('Prompt', 'Life of Brian')
-#inputs = tokenizer('''### Instruction: What Does Basic Homeowners Insurance Cover?\n### Response: ''', return_tensors="pt", return_attention_mask=False)
-inputs = tokenizer(i, return_tensors="pt", return_attention_mask=False)
-outputs = model.generate(**inputs, max_length=1024)
-text = tokenizer.batch_decode(outputs)[0]
-print(text)
+from transformers import AutoModelForCausalseq2seqLM, AutoTokenizer
+
+model_name="facebook/blenderbot-400M-distill"
+
+model=AutoModelForCausalseq2seqLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+ch=[]
+chat():
+    
+    h_s="\n".join(ch)
+    i=st.input("enter")
+    IS=TOKENIZER.ENCODE_PLUS(H_S,I,return_tensors="pt")
+    outputs=model.generate(**inputs,max_length=60)
+    response=tokenizer.decode(outputs[0],skip_special_tokens=True).strip()
+    c_h.appned(i)
+    c_h.append(response)
+    return response
+if __name__ == "__main__":
+    chat()
+