Update app.py

app.py

@@ -1,78 +0,0 @@
-import gradio as gr
-
-
-from transformers import T5ForConditionalGeneration, AutoTokenizer, RobertaTokenizer,AutoModelForCausalLM,pipeline,TrainingArguments
-
-
-models=[
-    "nadiamaqbool81/starcoderbase-1b-hf",
-    "nadiamaqbool81/starcoderbase-1b-hf_python",
-    "nadiamaqbool81/codet5-large-hf",
-    "nadiamaqbool81/codet5-large-hf-python",
-    "nadiamaqbool81/llama-2-7b-int4-java-code-1.178k",
-    "nadiamaqbool81/llama-2-7b-int4-python-code-510"
-]
-names=[
-    "nadiamaqbool81/starcoderbase-java",
-    "nadiamaqbool81/starcoderbase-python",
-    "nadiamaqbool81/codet5-java",
-    "nadiamaqbool81/codet5-python",
-    "nadiamaqbool81/llama-2-java",
-    "nadiamaqbool81/llama-2-python"
-]
-model_box=[
-    gr.load(f"models/{models[0]}"),
-    gr.load(f"models/{models[1]}"),
-    gr.load(f"models/{models[2]}"),
-    gr.load(f"models/{models[3]}"),
-    gr.load(f"models/{models[4]}"),
-    gr.load(f"models/{models[5]}"),
-]
-current_model=model_box[0]
-pythonFlag = "false"
-javaFlag = "false"
-
-
-
-def the_process(input_text, model_choice):
-    global pythonFlag
-    global javaFlag
-    print("Inside the_process for python 0", pythonFlag)
-    global output
-    print("Inside the_process for python 1", model_choice)
-    if(model_choice==1):
-      if(pythonFlag == "false"):
-        print("Inside starcoder for python")
-        tokenizer = AutoTokenizer.from_pretrained("nadiamaqbool81/starcoderbase-1b-hf_python")
-        model = AutoModelForCausalLM.from_pretrained("nadiamaqbool81/starcoderbase-1b-hf_python")
-        output = run_predict(input_text, model, tokenizer)
-        print("output starcoder python" , output)
-    elif(model_choice==0):
-      if(javaFlag == "false"):
-        print("Inside starcoder for java")
-        tokenizer = AutoTokenizer.from_pretrained("nadiamaqbool81/starcoderbase-1b-hf")
-        model = AutoModelForCausalLM.from_pretrained("nadiamaqbool81/starcoderbase-1b-hf")
-        output = run_predict(input_text, model, tokenizer)
-        print("output starcoder java" , output)
-    else:
-      a_variable = model_box[model_choice]
-      output = a_variable(input_text)
-      print("output other" , output)
-    return(output)
-
-
-def run_predict(text, model, tokenizer):
-    prompt = text
-    pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=400)
-    result = pipe(f"<s>[INST] {prompt} [/INST]")
-    arr = result[0]['generated_text'].split('[/INST]')
-    return arr[1]
-
-
-gr.HTML("""<h1 style="font-weight:600;font-size:50;margin-top:4px;margin-bottom:4px;text-align:center;">Text to Code Generation</h1></div>""")
-model_choice = gr.Dropdown(label="Select Model", choices=[m for m in names], type="index", interactive=True)
-input_text = gr.Textbox(label="Input Prompt")
-output_window = gr.Code(label="Generated Code")
-
-interface = gr.Interface(fn=the_process, inputs=[input_text, model_choice], outputs="text")
-interface.launch(debug=True)