Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+import torch
+from PIL import Image
+import numpy as np
+import librosa
+
+# Load T5 model for simplification
+tokenizer = AutoTokenizer.from_pretrained("t5-base")
+model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")
+
+# Dummy function for stress detection from voice (replace with your actual model)
+def detect_stress_from_voice(audio_path):
+    # For demo, let's randomly return 'low' or 'high' stress
+    # You will replace this with real stress detection logic
+    return "high"
+
+# Dummy function for stress detection from face image (replace with your actual model)
+def detect_stress_from_face(image):
+    # For demo, randomly return 'low' or 'high' stress
+    return "high"
+
+def simplify_task(task, stress_level):
+    if stress_level == "low":
+        return task  # No simplification needed if stress is low
+    
+    input_text = "simplify: " + task
+    inputs = tokenizer.encode(input_text, return_tensors="pt", max_length=512, truncation=True)
+    outputs = model.generate(inputs, max_length=60, num_beams=4, early_stopping=True)
+    simplified_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return simplified_text
+
+def assistant(voice, face_image, task):
+    # Step 1: Detect stress from voice and face image
+    voice_stress = detect_stress_from_voice(voice.name)
+    face_stress = detect_stress_from_face(face_image)
+
+    # Combine stress signals (simple majority vote)
+    stress_level = "high" if (voice_stress == "high" or face_stress == "high") else "low"
+
+    # Step 2: Simplify the task based on stress level
+    simplified = simplify_task(task, stress_level)
+
+    # Return stress level and simplified task
+    return f"Detected Stress Level: {stress_level.capitalize()}", simplified
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Context-Aware Multimodal Assistant")
+    gr.Markdown("Upload your voice recording and face image, then type your task below.")
+
+    voice_input = gr.Audio(label="Upload your voice recording (.wav)", type="filepath")
+    face_input = gr.Image(label="Upload your face image")
+    task_input = gr.Textbox(label="📝 What are you trying to do or say?", placeholder="E.g. I need help writing a message to my manager.")
+
+    output_stress = gr.Textbox(label="🧠 Stress Level Detected", interactive=False)
+    output_simplified = gr.Textbox(label="💬 Simplified Task / Message", interactive=False)
+
+    submit_btn = gr.Button("Simplify Task")
+
+    submit_btn.click(
+        fn=assistant,
+        inputs=[voice_input, face_input, task_input],
+        outputs=[output_stress, output_simplified]
+    )
+
+if __name__ == "__main__":
+    demo.launch()