Delete app.py

app.py

@@ -1,62 +0,0 @@
-import torch
-from peft import PeftModel, PeftConfig
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-# config = PeftConfig.from_pretrained("/content/llama-2-7b-medichat")
-model = AutoModelForCausalLM.from_pretrained("NousResearch/Llama-2-7b-chat-hf", return_dict=True, load_in_8bit=True, device_map='auto')
-tokenizer = AutoTokenizer.from_pretrained("NousResearch/Llama-2-7b-chat-hf")
-model = PeftModel.from_pretrained(model, "maxspin/medichat")
-
-import gradio as gr
-
-
-iface.launch()
-
-def query_handling(query, conversation):
-  if "thanks" in query.lower() or "thank you" in query.lower() or "thank you very much" in query.lower():
-    conversation=""
-  return conversation
-
-def process_response(input_string):
-    # Find the indices of the first [INST] and last [/INST]
-    start_index = input_string.find("[INST]")
-    end_index = input_string.rfind("[/INST]")
-
-    # If both [INST] and [/INST] are found
-    if start_index != -1 and end_index != -1:
-        # Extract the substring between [INST] and [/INST]
-        inst_substring = input_string[start_index:end_index + len("[/INST]")]
-        # Remove the extracted substring from the original string
-        cleaned_string = input_string.replace(inst_substring, "")
-    else:
-        # If [INST] or [/INST] is not found, keep the original string
-        cleaned_string = input_string
-
-    # Remove the special characters <s> and </s>
-    cleaned_string = cleaned_string.replace("<s>", "").replace("</s>", "").replace("[INST]","").replace("[/INST]","")
-
-    return cleaned_string
-
-
-conversation=""
-def predict(prompt):
-    global conversation
-    conversation = conversation+f"[INST]{prompt}[/INST]"
-    input_sequense = "<s>"+conversation
-    batch = tokenizer(f"{input_sequense}", return_tensors='pt')
-    batch = batch.to('cuda')
-    with torch.cuda.amp.autocast():
-      output_tokens = model.generate(**batch, max_new_tokens=4000)
-      response =  tokenizer.decode(output_tokens[0], skip_special_tokens=True)
-      print('\n\n', tokenizer.decode(output_tokens[0], skip_special_tokens=True))
-      response = process_response(response)
-      conversation+=response
-      conversation = query_handling(prompt,conversation)
-      print(conversation)
-    return response
-
-iface = gr.Interface(
-    fn=predict,
-    inputs="text",  # Accepts a single text input
-    outputs="text"  # Outputs a single text response
-)