Create app.py

app.py

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+import streamlit as st
+from transformers import GPT2Tokenizer, TFGPT2LMHeadModel
+
+# Load the fine-tuned model
+model = TFGPT2LMHeadModel.from_pretrained("fine-tuned-gpt2")
+
+# Initialize the tokenizer
+tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+
+# Set the maximum length for the generated text
+max_length = 100
+
+def generate_answer(prompt):
+    # Encode the prompt
+    input_ids = tokenizer.encode(prompt, return_tensors="tf")
+
+    # Generate text using the model
+    output = model.generate(
+        input_ids=input_ids,
+        max_length=max_length,
+        num_return_sequences=1,
+        do_sample=True,
+        temperature=0.5,
+    )
+
+    # Decode the generated text
+    generated_text = tokenizer.decode(output[0])
+
+    return generated_text
+
+def main():
+    st.title("Chatbot")
+
+    # Get user input
+    prompt = st.text_input("Enter your question")
+
+    # Generate answer on button click
+    if st.button("Generate Answer"):
+        answer = generate_answer(prompt)
+        st.text("Generated Answer:")
+        st.text(answer)
+
+if __name__ == '__main__':
+    main()