Update app.py

app.py

@@ -1,3 +1,38 @@
 import gradio as gr
+from transformers import pipeline
 
-gr.Interface.load("models/Amitesh007/text_generation-finetuned-gpt2").launch()
+fine_tuned_model = "Amitesh007/text_generation-finetuned-gpt2"
+def generate(text,the_model,max_length,temperature,num_beams,top_k,top_p,repetition_penalty):
+    generator = pipeline('text-generation', model=fine_tuned_model)
+    result = generator(text, num_return_sequences=2,
+    max_length=max_length,
+    temperature=temperature,
+    num_beams=num_beams,
+    top_k=top_k,
+    top_p=top_p,
+    repetition_penalty = repetition_penalty,
+    no_repeat_ngram_size=2,early_stopping=False)
+    return result[0]["generated_text"],result[1]["generated_text"]
+
+demo = gr.Interface(
+    fn=generate,
+    inputs=[
+        gr.Textbox(lines=5, label="Input Text"),
+        gr.Dropdown(choices=['gpt2'],value = 'gpt2',label="current model"),
+        gr.Slider(value=50,label="Max Length",minimum=1,maximum=1000),
+        gr.Slider(value=1.0,label="Temperature",minimum=0.0,maximum=1.0,step=0.05),
+        gr.Slider(value=4,label="Num Beams",minimum=2,maximum=6,step=1),
+        gr.Slider(value=90,label="Top-k",minimum=0,maximum=100),
+        gr.Slider(value=0.9,label="Top-p",minimum=0.1,maximum=1,step=0.05),
+        gr.Slider(value=1.1,label="Repetition penalty",minimum=0.2,maximum=2,step=0.1)
+
+        ],
+    outputs=[
+        gr.Textbox(label="Generated Text 1"),
+        gr.Textbox(label="Generated Text 2")
+        ],
+    title = "Text Generator GPT2 Pipeline",
+    description = "Text Generator. \n Temperature control randomness, lowering results in less random completions. As approach the zero, the model becomes more repetitive."
+)
+
+demo.launch()