adding app.py + requirements.txt

.gitignore

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+venv
+keys.txt

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Model name (you can swap this for another emotion model if you like)
+model_name = "j-hartmann/emotion-english-distilroberta-base"
+#minoosh/finetuned_bert-base-on-IEMOCAP_1
+
+# Device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name).to(device)
+model.eval()
+
+# Prediction function
+def predict_emotion(text: str):
+    # Handle empty input
+    if not text or not text.strip():
+        return {"Error": "Please enter some text."}
+
+    # Tokenize
+    inputs = tokenizer(
+        text,
+        return_tensors="pt",
+        truncation=True,
+        padding=True,
+        max_length=256,  # you can adjust this if needed
+    ).to(device)
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probs = outputs.logits.softmax(dim=-1)[0]
+
+    # Map id -> label using model config
+    id2label = model.config.id2label
+    scores = {id2label[i]: float(probs[i]) for i in range(len(probs))}
+
+    # Sort by highest probability first (optional but nice in the UI)
+    scores = dict(sorted(scores.items(), key=lambda x: x[1], reverse=True))
+    return scores
+
+# Gradio interface
+demo = gr.Interface(
+    fn=predict_emotion,
+    inputs=gr.Textbox(lines=4, label="Enter text"),
+    outputs=gr.Label(label="Emotion Probabilities"),
+    title="Emotion Classifier",
+    description="Enter a sentence and see the predicted emotion distribution.",
+    allow_flagging="never",
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio 
+torch==2.8.0
+transformers==4.57.2
+pydantic==2.9.1         
+
+huggingface-hub==0.36.0
+tokenizers==0.22.1
+safetensors==0.7.0