Add Gradio application and dependencies for deployment

README.md

@@ -1,14 +1,14 @@
 ---
-title: DL Gen AI Project
-emoji: 👀
-colorFrom: yellow
-colorTo: pink
+title: Multi-Label Emotion Classifier
+emoji: 🧠
+colorFrom: red
+colorTo: green
 sdk: gradio
-sdk_version: 6.0.1
+sdk_version: 3.48.0 # Use a recent stable version
 app_file: app.py
-pinned: false
-license: apache-2.0
-short_description: DL Gen AI Project 2025
+license: mit
 ---
+# Multi-Label Emotion Classification using RoBERTa-Large
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+This Space deploys the winning model from the DL/GenAI project.
+The model is a fine-tuned RoBERTa-Large transformer utilizing 5-Fold Cross-Validation and dynamic threshold optimization for superior Macro F1-Score.

app.py

@@ -0,0 +1,132 @@
+import gradio as gr
+import torch
+import numpy as np
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# --- Configuration ---
+# Your Model's Hugging Face Repository ID
+# NOTE: This assumes you successfully pushed the model during the CV loop!
+HF_REPO_ID = "sumittguptaa148/DL-Gen-AI-Project"
+MODEL_NAME = "roberta-large" # The base model name
+MAX_LEN = 256
+LABELS = ['anger', 'fear', 'joy', 'sadness', 'surprise']
+
+# Optimized Thresholds from the latest output:
+BEST_THRESHOLDS = {
+    'anger': 0.64,
+    'fear': 0.34,
+    'joy': 0.79,
+    'sadness': 0.78,
+    'surprise': 0.48
+}
+
+# --- Load Model and Tokenizer ---
+try:
+    # We load AutoModel for a custom architecture, but since we pushed
+    # using Trainer, the hub might save it with an AutoModelForSequenceClassification structure
+    # Let's try to load the base AutoModel and then load the state dict manually
+    
+    # Load the tokenizer
+    tokenizer = AutoTokenizer.from_pretrained(HF_REPO_ID)
+
+    # Load the model weights and structure.
+    # We use AutoModelForSequenceClassification here as Hugging Face Trainer usually
+    # saves a classification head that is compatible with this class structure
+    # when pushing to the hub.
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model = AutoModelForSequenceClassification.from_pretrained(HF_REPO_ID, num_labels=len(LABELS))
+    model.to(device)
+    model.eval()
+    print(f"Model and Tokenizer loaded from {HF_REPO_ID}")
+
+except Exception as e:
+    print(f"Error loading model from Hugging Face Hub: {e}")
+    # Fallback/Dummy definitions for deployment setup
+    tokenizer = None
+    model = None
+    device = "cpu"
+
+
+# --- Prediction Function ---
+def predict_emotion(text):
+    if model is None:
+        return "Model failed to load. Please check logs."
+    
+    # Tokenize input
+    inputs = tokenizer(
+        text, 
+        padding=True, 
+        truncation=True, 
+        max_length=MAX_LEN, 
+        return_tensors="pt"
+    )
+    
+    # Move tensors to device
+    inputs = {k: v.to(device) for k, v in inputs.items()}
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits.cpu().numpy()
+
+    # Convert logits to probabilities (sigmoid)
+    probs = 1 / (1 + np.exp(-logits))[0]
+    
+    # Apply dynamic thresholds and format output
+    results = {}
+    for i, label in enumerate(LABELS):
+        prob = probs[i]
+        threshold = BEST_THRESHOLDS[label]
+        
+        # Classification
+        is_present = " Present" if prob >= threshold else " Absent"
+        
+        # Format for display
+        results[f"{label.capitalize()} ({is_present})"] = f"{prob:.4f} (Threshold: {threshold:.2f})"
+        
+    return results
+
+# --- Gradio Interface ---
+# Output components for Gradio
+output_components = [
+    gr.Textbox(label=f"{label.capitalize()} (Classification & Prob)", lines=1) for label in LABELS
+]
+
+# Map output components to keys in the dictionary returned by predict_emotion
+# Gradio expects the output component labels to match the dictionary keys
+output_keys = [f"{label.capitalize()} ({{}})" for label in LABELS] # Placeholder for "Present/Absent" part
+
+# Custom function to create the correct output components for Gradio
+def get_output_components():
+    # Use Textbox for results
+    outputs = []
+    for label in LABELS:
+        outputs.append(gr.Textbox(label=f"{label.capitalize()} Emotion Result", lines=1))
+    return outputs
+
+
+# Custom wrapper to ensure output matches the order of Gradio components
+def predict_emotion_gradio(text):
+    raw_results = predict_emotion(text)
+    
+    # Reorder results to match the output components list
+    ordered_results = []
+    for label in LABELS:
+        # Find the key that starts with the label (e.g., 'Anger (✅ Present)')
+        key_match = next(k for k in raw_results if k.startswith(label.capitalize()))
+        ordered_results.append(raw_results[key_match])
+        
+    return tuple(ordered_results)
+
+
+title = "Multi-Label Emotion Classification with RoBERTa-Large"
+description = "A DL/GenAI project classifying text into Anger, Fear, Joy, Sadness, and Surprise. The model uses a fine-tuned RoBERTa-Large with 5-Fold CV and dynamic threshold optimization."
+
+gr.Interface(
+    fn=predict_emotion_gradio,
+    inputs=gr.Textbox(lines=5, placeholder="Enter a sentence or short text here...", label="Input Text"),
+    outputs=get_output_components(), # Use the custom function to get ordered components
+    title=title,
+    description=description,
+    allow_flagging="never",
+    theme="huggingface"
+).launch()

requirements.txt

@@ -0,0 +1,7 @@
+torch
+pandas
+numpy
+transformers
+datasets
+scikit-learn
+gradio # Front-end framework