Final Fix: Removed local path checks

src/streamlit_app.py

@@ -63,21 +63,25 @@ def save_smart_data(text, ai_label, ai_score, corrected_label=None):
         df.to_csv(FEEDBACK_FILE, mode='a', header=False, index=False)
 
 # --- MODEL ENGINE PATH FIX ---
-# 1. This must be your EXACT Model Repo name from Screenshot 281
-MODEL_PATH = "SumedhGajbhiye/Sentiment-Analyzer" 
+import streamlit as st
+from transformers import pipeline
+
+# This MUST match your model repo name from Screenshot 281
+MODEL_REPO = "SumedhGajbhiye/Sentiment-Analyzer"
 
 @st.cache_resource
-def load_engine():
+def load_analysis_engine():
     try:
-        # We use the repo name directly. 
-        # Transformers will automatically download it if it's not local.
-        return pipeline("sentiment-analysis", model=MODEL_PATH, tokenizer=MODEL_PATH)
+        # This tells the app to download weights from the Hub, not the local folder
+        return pipeline("sentiment-analysis", model=MODEL_REPO, tokenizer=MODEL_REPO)
     except Exception as e:
-        st.error(f"Error loading model: {e}")
+        st.error(f"Could not connect to System Core: {e}")
         return None
 
-# Remove or comment out any code that does: 
-# if not os.path.exists(MODEL_PATH): st.error("Not found")
+# IMPORTANT: Find and delete the line in your code that says:
+# if not os.path.exists("model.safetensors"): st.error("System Core... not found")
+
+
 
 # --- HEADER SECTION ---
 st.title("Sentiment Analyzer")
@@ -95,12 +99,12 @@ with st.sidebar:
         st.info("Waiting for input...")
 
 # --- ANALYSIS INTERFACE ---
-# Check if model file exists locally
-if not os.path.exists("model.safetensors"):
-    st.error("System Core (model.safetensors) not found in directory!")
+# 1. We call the cached loader (it handles the Hub download automatically)
+classifier = load_analysis_engine()
+
+if classifier is None:
+    st.error("System Core could not be initialized. Please check the logs.")
 else:
-    classifier = load_engine(MODEL_PATH)
-    
     user_input = st.text_input(
         "QUERY INPUT:", 
         placeholder="Enter sentence (English/Hindi/Hinglish)...", 
@@ -109,15 +113,18 @@ else:
 
     if user_input:
         with st.status("Neural Scan in Progress...", expanded=False) as status:
+            # Running the inference
             result = classifier(user_input)[0]
             status.update(label="Analysis Complete", state="complete")
 
         label = result['label']
         score = result['score']
         
-        # Adjust these keys to match your specific model's output labels (e.g., LABEL_0, LABEL_1)
+        # Mapping labels (Adjust if your XLM-Roberta uses LABEL_0/1/2)
         emoji_map = {"POSITIVE": "🟢", "NEUTRAL": "🟡", "NEGATIVE": "🔴"}
-        color = "#00ff88" if "POSITIVE" in label.upper() else "#ff4b4b" if "NEGATIVE" in label.upper() else "#ffaa00"
+        
+        # Color logic
+        color = "#00ff88" if "POS" in label.upper() else "#ff4b4b" if "NEG" in label.upper() else "#ffaa00"
 
         st.markdown(f'''
             <div class="glass-card">
@@ -131,7 +138,7 @@ else:
         fig = go.Figure(go.Indicator(
             mode = "gauge+number",
             value = score * 100,
-            gauge = {'axis': {'range': [None, 100]}, 'bar': {'color': color}}
+            gauge = {'axis': {'range': [0, 100]}, 'bar': {'color': color}}
         ))
         fig.update_layout(height=250, paper_bgcolor='rgba(0,0,0,0)', font={'color': "#fff"})
         st.plotly_chart(fig, use_container_width=True)