Create app.py

app.py

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+import gradio as gr
+from transformers import GPT2Tokenizer, GPT2LMHeadModel
+
+# Load pre-trained model and tokenizer
+model_name = "gpt2"
+tokenizer = GPT2Tokenizer.from_pretrained(model_name)
+model = GPT2LMHeadModel.from_pretrained(model_name)
+
+def generate_response(prompt):
+  score1 = 0
+  score2=0
+  # Tokenize the prompt
+  input_ids = tokenizer.encode(prompt, return_tensors="pt")
+
+  # Generate response using beam search
+  output = model.generate(input_ids, max_length=100,
+                          num_return_sequences=2, no_repeat_ngram_size=2, num_beams=5)
+
+  # Decode and store responses with basic scoring
+  responses = []
+  for i, out in enumerate(output):
+    response = tokenizer.decode(out, skip_special_tokens=True)
+    responses.append(response)
+
+  return responses[0], score1, responses[1], score2
+
+  
+
+# Gradio interface
+iface = gr.Interface(
+  fn=generate_response,
+  inputs=["text"],
+  outputs=[gr.Textbox(label="Response 1"),
+           gr.Slider(0,5,interactive=True,label="score 1",step=1),
+           gr.Textbox(label="Response 2"),
+           gr.Slider(0,5,interactive=True,label="score 2",step=1)],
+  title="GROUP1_TASK1",
+  description="Enter a question to generate responses from GPT-2 model.",
+  
+)
+
+iface.launch()