Update src/streamlit_app.py

src/streamlit_app.py

@@ -13,12 +13,8 @@ st.title("📝 T5-small LoRA Text Summarization")
 st.markdown("Using your local model files for summarization")
 
 def main():
-    # Since we have the model files in the Space, we can use them directly
-    st.info("""
-    **Model Status**: Your T5-small LoRA model files are detected in this Space.
-    
-    This app will use the model directly from your repository files.
-    """)
+    # Display model files info
+    st.success("✅ All model files are detected in this Space!")
     
     # Input section
     st.subheader("📄 Input Text")
@@ -47,20 +43,16 @@ def main():
         - tokenizer_config.json
         - training_args.bin
         """)
-        st.success("All model files are present in this Space!")
     
     if st.button("🚀 Generate Summary", type="primary"):
         if not input_text.strip():
             st.warning("⚠️ Please enter some text to summarize.")
         else:
-            # Since we can't load the model directly due to torch issues,
-            # we'll use the Hugging Face Inference API with YOUR model
             with st.spinner("⏳ Generating summary using your model..."):
                 try:
-                    # Use Inference API with your model
-                    API_URL = f"https://api-inference.huggingface.co/models/{st.secrets.get('HF_USERNAME', 'manesh1')}/t5-small-lora-summarization"
+                    # Use Inference API directly without secrets
+                    API_URL = "https://api-inference.huggingface.co/models/manesh1/t5-small-lora-summarization"
                     
-                    # Try without token first (public access)
                     response = requests.post(
                         API_URL,
                         json={
@@ -89,23 +81,15 @@ def main():
                                 st.metric("Summary Words", len(summary.split()))
                         else:
                             st.error(f"Unexpected response format: {result}")
+                    elif response.status_code == 503:
+                        # Model is loading
+                        st.warning("🔄 Model is loading... Please wait 20-30 seconds and try again.")
+                        st.info("This is normal for the first request. The model needs to load on Hugging Face's servers.")
                     else:
-                        st.warning(f"API returned status {response.status_code}. The model might be loading.")
-                        st.info("""
-                        **Next steps:**
-                        1. Wait 20-30 seconds and try again
-                        2. The model needs to load on Hugging Face's servers
-                        3. Subsequent requests will be faster
-                        """)
+                        st.error(f"API Error: Status {response.status_code} - {response.text}")
                         
                 except Exception as e:
                     st.error(f"Error: {str(e)}")
-                    st.info("""
-                    **Troubleshooting:**
-                    - The model is loading for the first time (can take 20-30 seconds)
-                    - Try again in a moment
-                    - Check that your model files are properly configured
-                    """)
 
 if __name__ == "__main__":
     main()