Upload app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import json
+import os
+
+# --- 1. CONFIGURATION ---
+MODEL_PATH = "." 
+LABELS = ["anger", "fear", "joy", "sadness", "surprise"]
+DEFAULT_THRESHOLDS = {"anger": 0.5, "fear": 0.5, "joy": 0.5, "sadness": 0.5, "surprise": 0.5}
+
+# --- 2. LOAD RESOURCES ---
+# Load Model
+try:
+    model = AutoModelForSequenceClassification.from_pretrained(MODEL_PATH)
+    # Check if safe tensors actually exist
+    if not os.path.exists(os.path.join(MODEL_PATH, "model.safetensors")):
+        raise FileNotFoundError
+except:
+    model = None # Fallback
+
+# Load Tokenizer
+try:
+    tokenizer = AutoTokenizer.from_pretrained("roberta-large")
+except:
+    tokenizer = AutoTokenizer.from_pretrained("roberta-base")
+
+# Load Thresholds
+if os.path.exists("fine_thresholds.json"):
+    with open("fine_thresholds.json", "r") as f:
+        THRESHOLDS = json.load(f)
+else:
+    THRESHOLDS = DEFAULT_THRESHOLDS
+
+# --- 3. LOGIC ---
+def analyze(text):
+    if not text or not text.strip():
+        return {}, "⚠️ Please type something first!"
+    if model is None: 
+        return {}, "⚠️ Model files missing in this folder."
+
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
+    model.eval()
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probs = torch.sigmoid(outputs.logits).numpy()[0]
+    
+    results = {}
+    detected_emotions = []
+    
+    for i, label in enumerate(LABELS):
+        score = float(probs[i])
+        results[label] = score
+        if score > THRESHOLDS.get(label, 0.5):
+            detected_emotions.append(label.upper())
+            
+    if not detected_emotions:
+        final_verdict = "😐 No strong emotion detected."
+    else:
+        final_verdict = " ".join([f"**{e}**" for e in detected_emotions])
+        
+    return results, final_verdict
+
+# --- 4. PROFESSIONAL CSS (The "Form" Look) ---
+custom_css = """
+/* 1. Background Gradient */
+.gradio-container {
+    background: linear-gradient(135deg, #e0e7ff 0%, #f3f4f6 100%) !important;
+}
+
+/* 2. The White Card Container */
+.form-card {
+    max-width:600px;
+    margin:auto;
+    background: white !important;
+    border-radius: 12px !important;
+    padding: 30px !important;
+    box-shadow: 0 10px 30px rgba(0,0,0,0.1) !important;
+    border-top: 6px solid #6366f1 !important; /* Indigo Top Bar */
+    border: 1px solid #e5e7eb !important;
+}
+
+/* 3. Header Text */
+.form-header h1 {
+    color: #4338ca;
+    text-align: center;
+    font-weight: 800;
+    margin-bottom: 5px;
+}
+.form-header p {
+    color: #6b7280;
+    text-align: center;
+    margin-bottom: 20px;
+}
+
+/* 4. Button Styling */
+button.primary {
+    background: #4f46e5 !important;
+    font-size: 1.1em !important;
+}
+"""
+
+# --- 5. UI LAYOUT ---
+theme = gr.themes.Base(primary_hue="indigo", font=[gr.themes.GoogleFont("Inter"), "system-ui"])
+
+with gr.Blocks( title="Sentiment Check") as app:
+    
+    # We wrap everything in a Column with our custom 'form-card' class
+    with gr.Column(elem_classes="form-card"):
+        
+        # Header
+        gr.HTML("""
+            <div class="form-header">
+                <h1>🧠 Sentiment Check</h1>
+                <p>Advanced Neural Emotion Detection System</p>
+            </div>
+        """)
+        
+        # Section 1: Input
+        gr.Markdown("### 1. Source Text")
+        txt_input = gr.Textbox(
+            lines=4, 
+            placeholder="Type your text here...", 
+            show_label=False,
+            container=False # Makes it look cleaner
+        )
+        
+        # Action Button
+        btn_run = gr.Button("GENERATE REPORT", variant="primary", size="lg")
+        
+        # Divider
+        gr.HTML("<hr style='margin: 20px 0; border: 0; border-top: 1px solid #e5e7eb;'>")
+        
+        # Section 2: Results
+        gr.Markdown("### 2. Analysis Results")
+        with gr.Row():
+            with gr.Column(scale=1):
+                lbl_verdict = gr.Markdown("**Status:** Waiting for data...", label="Prediction")
+            with gr.Column(scale=1):
+                lbl_chart = gr.Label(num_top_classes=5, label="Confidence Spectrum")
+    
+    # Footer
+    gr.Markdown(
+        "<div style='text-align:center; color:#9ca3af; margin-top:15px; font-size:0.8em;'>Secure Local Deployment • Powered by RoBERTa</div>"
+    )
+
+    # Logic
+    btn_run.click(fn=analyze, inputs=txt_input, outputs=[lbl_chart, lbl_verdict])
+
+app.launch(theme=theme, css=custom_css,share=False)#,auth=("admin", "pass123"))