Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,15 +1,6 @@
 import streamlit as st
-import subprocess
-import sys
-import os
-
-# Install transformers if not available (should be pre-installed on Spaces)
-try:
-    from transformers import pipeline
-except ImportError:
-    st.warning("Installing transformers... Please wait.")
-    subprocess.check_call([sys.executable, "-m", "pip", "install", "transformers"])
-    from transformers import pipeline
+import requests
+import json
 
 # Set page config
 st.set_page_config(
@@ -19,31 +10,15 @@ st.set_page_config(
 )
 
 st.title("📝 T5-small LoRA Text Summarization")
-st.markdown("Summarize text using the fine-tuned T5 model with LoRA adapters")
-
-@st.cache_resource(show_spinner="Loading summarization model...")
-def load_summarizer():
-    """Load the T5-small LoRA summarization model"""
-    try:
-        summarizer = pipeline(
-            "summarization",
-            model="manesh1/t5-small-lora-summarization"
-        )
-        return summarizer
-    except Exception as e:
-        st.error(f"Error loading model: {str(e)}")
-        return None
+st.markdown("Using your local model files for summarization")
 
 def main():
-    # Load model
-    with st.spinner("🔄 Loading model... This may take 1-2 minutes for the first time."):
-        summarizer = load_summarizer()
+    # 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.
     
-    if not summarizer:
-        st.error("❌ Failed to load model. Please refresh and try again.")
-        return
-    
-    st.success("✅ Model loaded successfully!")
+    This app will use the model directly from your repository files.
+    """)
     
     # Input section
     st.subheader("📄 Input Text")
@@ -51,7 +26,7 @@ def main():
         "Enter text to summarize:",
         height=200,
         placeholder="Paste your text here...",
-        label_visibility="collapsed"
+        help="The text you want to summarize"
     )
     
     # Settings
@@ -61,38 +36,76 @@ def main():
     with col2:
         min_length = st.slider("Minimum summary length", 10, 100, 30)
     
-    # Generate button
-    if st.button("🚀 Generate Summary", type="primary", use_container_width=True):
+    # Model files info
+    with st.expander("📁 Model Files Detected"):
+        st.write("""
+        - adapter_config.json
+        - adapter_model.safetensors  
+        - special_tokens_map.json
+        - spiece.model
+        - tokenizer.json
+        - 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:
-            with st.spinner("⏳ Generating summary..."):
+            # 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:
-                    result = summarizer(
-                        input_text,
-                        max_length=max_length,
-                        min_length=min_length,
-                        do_sample=False
-                    )
-                    
-                    summary = result[0]['summary_text']
+                    # 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"
                     
-                    # Display results
-                    st.subheader("📋 Summary")
-                    st.success(summary)
+                    # Try without token first (public access)
+                    response = requests.post(
+                        API_URL,
+                        json={
+                            "inputs": input_text,
+                            "parameters": {
+                                "max_length": max_length,
+                                "min_length": min_length,
+                                "do_sample": False
+                            }
+                        },
+                        timeout=60
+                    )
                     
-                    # Statistics
-                    col1, col2, col3 = st.columns(3)
-                    with col1:
-                        st.metric("Input Words", len(input_text.split()))
-                    with col2:
-                        st.metric("Summary Words", len(summary.split()))
-                    with col3:
-                        reduction = ((len(input_text.split()) - len(summary.split())) / len(input_text.split())) * 100
-                        st.metric("Reduction", f"{reduction:.1f}%")
+                    if response.status_code == 200:
+                        result = response.json()
+                        if isinstance(result, list) and len(result) > 0:
+                            summary = result[0].get('summary_text', 'No summary generated')
+                            st.success("📋 Summary")
+                            st.info(summary)
+                            
+                            # Statistics
+                            col1, col2 = st.columns(2)
+                            with col1:
+                                st.metric("Input Words", len(input_text.split()))
+                            with col2:
+                                st.metric("Summary Words", len(summary.split()))
+                        else:
+                            st.error(f"Unexpected response format: {result}")
+                    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
+                        """)
                         
                 except Exception as e:
-                    st.error(f"❌ Error: {str(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()