added gradio files

app.py

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+import torch
+import gradio
+from transformers import AutoModelWithLMHead, AutoTokenizer
+
+def generate_response(model, tokenizer, prompt, max_length=200):
+    input_ids = tokenizer.encode(prompt, return_tensors="pt")      # 'pt' for returning pytorch tensor
+    # Create the attention mask and pad token id
+    attention_mask = torch.ones_like(input_ids)
+    pad_token_id = tokenizer.eos_token_id
+    output = model.generate(
+        input_ids,
+        max_length=max_length,
+        num_return_sequences=1,
+        attention_mask=attention_mask,
+        pad_token_id=pad_token_id
+    )
+    return tokenizer.decode(output[0], skip_special_tokens=True)
+
+# Load your model from hub
+
+username = "vsen7"      # change it to your HuggingFace username
+checkpoint = username + '/Medical_Summary'
+loaded_model = AutoModelWithLMHead.from_pretrained(checkpoint)
+
+# Load your tokenizer from hub
+tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+
+# Function for response generation
+def generate_query_response(prompt):
+    model = loaded_model
+    #tokenizer = tokenizer
+    response = generate_response(model, tokenizer, prompt)
+    return response
+
+
+# Gradio interface to generate UI link
+iface = gradio.Interface(fn=generate_query_response,
+                    inputs="textbox",
+                    outputs="textbox",
+                    title="Dialogue Summarization",
+                    description="via gradio",
+                    allow_flagging="never",
+                     )
+iface.launch()

requirements.txt

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+transformers
+torch
+gradio