Create app.py

app.py

@@ -0,0 +1,75 @@
+import gradio as gr
+import torch
+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
+    global output
+    if(model_choice == 5):
+      if(pythonFlag == "false"):
+        tokenizer = AutoTokenizer.from_pretrained("nadiamaqbool81/llama-2-7b-int4-python-code-510")
+        model = AutoModelForCausalLM.from_pretrained("nadiamaqbool81/llama-2-7b-int4-python-code-510", load_in_4bit=True, torch_dtype=torch.float16, device_map= {"": 0} )
+        output = run_predict(input_text, model, tokenizer)
+        pythonFlag = "true"
+      elif(pythonFlag == "true"):
+        output = run_predict(input_text, model, tokenizer)
+    elif(model_choice == 4):
+      if(javaFlag == "false"):
+       tokenizerJava = AutoTokenizer.from_pretrained("nadiamaqbool81/llama-2-7b-int4-java-code-1.178k")
+       modelJava = AutoModelForCausalLM.from_pretrained("nadiamaqbool81/llama-2-7b-int4-java-code-1.178k", load_in_4bit=True, torch_dtype=torch.float16, device_map= {"": 0})
+       output = run_predict(input_text, modelJava, tokenizerJava)
+       javaFlag = "true"
+      elif(javaFlag == "true"):
+        output = run_predict(input_text, modelJava, tokenizerJava)
+    else:
+      a_variable = model_box[model_choice]
+      output = a_variable(input_text)
+    return(output)
+
+
+def run_predict(text, model, tokenizer):
+    prompt = text
+    pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=300)
+    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()