Added streamlit application file.

app.py

@@ -0,0 +1,34 @@
+import streamlit as st
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+
+@st.cache_resource
+def load_model():
+    model = GPT2LMHeadModel.from_pretrained("finetuned-distilgpt2")
+    tokenizer = GPT2Tokenizer.from_pretrained("finetuned-distilgpt2")
+    tokenizer.pad_token = tokenizer.eos_token
+    return model, tokenizer
+
+model, tokenizer = load_model()
+
+def chat_with_model(query):
+    inputs = tokenizer.encode(query, return_tensors="pt", padding=True, truncation=True, max_length=512)
+    outputs = model.generate(
+        inputs,
+        max_length=150,
+        num_return_sequences=1,
+        no_repeat_ngram_size=2,
+        top_k=50,
+        top_p=0.95,
+        temperature=1.0,
+        pad_token_id=tokenizer.pad_token_id,
+    )
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
+
+st.title("Chat with Akshay")
+st.text("Fine-tuned GPT-2 for interactive conversations about me.")
+
+user_input = st.text_input("You:", placeholder="Type your message here...")
+if user_input:
+    response = chat_with_model(user_input)
+    st.text_area("GPT-2 as Akshay:", response, height=200)