Upload app.py

app.py

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+import streamlit as st
+from transformers import AutoModelForSeq2SeqLM, T5Tokenizer
+
+def generate_response(input_prompt):
+    fine_tuned_model_path = "Robin246/inxai"  # Update with your Hugging Face model path
+
+    # Load the fine-tuned model and tokenizer
+    fine_tuned_model = AutoModelForSeq2SeqLM.from_pretrained(fine_tuned_model_path)
+    tokenizer = T5Tokenizer.from_pretrained(fine_tuned_model_path)
+
+    input_text = f"Input prompt: {input_prompt}"
+
+    input_ids = tokenizer.encode(input_text, return_tensors="pt", max_length=64, padding="max_length", truncation=True)
+    output_ids = fine_tuned_model.generate(input_ids, max_length=256, num_return_sequences=1, num_beams=1, early_stopping=True)
+    generated_output = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+
+    return generated_output
+
+def main():
+    st.title('INXAI LLM model')
+    user_input = st.text_input('Input Prompt:', '')
+
+    if st.button('Generate'):
+        response = generate_response(user_input)
+        st.write('Generated Reply:', response)
+
+if __name__ == '__main__':
+    main()