Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AlbertForSequenceClassification
+import numpy as np
+import os
+
+# Define paths to saved models
+save_dir = "./saved_models"
+tasks = ["sentiment", "emotion", "hate_speech", "sarcasm"]
+model_paths = {task: f"{save_dir}/{task}" for task in tasks}
+
+# Define label mappings
+label_mappings = {
+    "sentiment": ["negative", "neutral", "positive"],
+    "emotion": ["happy", "sad", "angry", "fear"],
+    "hate_speech": ["no", "yes"],
+    "sarcasm": ["no", "yes"]
+}
+
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained("ai4bharat/indic-bert")
+
+# Load all models
+models = {}
+for task in tasks:
+    if not os.path.exists(model_paths[task]):
+        raise FileNotFoundError(f"Model directory {model_paths[task]} not found. Ensure saved_models/{task} exists.")
+    models[task] = AlbertForSequenceClassification.from_pretrained(model_paths[task])
+
+# Function to predict for a single task
+def predict_task(text, task, model, tokenizer, max_length=128):
+    inputs = tokenizer(
+        text,
+        padding=True,
+        truncation=True,
+        max_length=max_length,
+        return_tensors="pt"
+    )
+    
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        probabilities = torch.softmax(logits, dim=1).squeeze().cpu().numpy()
+    
+    labels = label_mappings[task]
+    return {label: f"{prob*100:.2f}%" for label, prob in zip(labels, probabilities)}
+
+# Gradio interface function
+def predict_all_tasks(text):
+    if not text.strip():
+        return "Please enter some text."
+    
+    results = {}
+    for task in tasks:
+        results[task] = predict_task(text, task, models[task], tokenizer)
+    
+    output = ""
+    for task, probs in results.items():
+        output += f"\n{task.capitalize()} Prediction:\n"
+        for label, prob in probs.items():
+            output += f"  {label}: {prob}\n"
+    
+    return output
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=predict_all_tasks,
+    inputs=gr.Textbox(lines=2, placeholder="Enter Telugu text here..."),
+    outputs="text",
+    title="Telugu Text Analysis",
+    description="Enter Telugu text to predict sentiment, emotion, hate speech, and sarcasm."
+)
+
+if __name__ == "__main__":
+    iface.launch(server_name="0.0.0.0", server_port=7860)