File size: 1,837 Bytes
00c4c0f e93edf7 00c4c0f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | import gradio as gr
import tensorflow as tf # Use standard tensorflow
import numpy as np
from PIL import Image
import os
from huggingface_hub import InferenceClient
# 1. Load the TFLite Model using tensorflow's interpreter
interpreter = tf.lite.Interpreter(model_path="skin_model.tflite")
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
# 2. Load Labels
with open("labels.txt", "r") as f:
labels = [line.strip() for line in f.readlines()]
# 3. Setup AI Assistant Client
client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.3", token=os.getenv("HF_TOKEN"))
def predict_and_advise(image):
# Preprocess image
input_shape = input_details[0]['shape']
img = Image.fromarray(image).resize((input_shape[1], input_shape[2]))
input_data = np.expand_dims(np.array(img, dtype=np.float32), axis=0)
# Run inference
interpreter.set_tensor(input_details[0]['index'], input_data)
interpreter.invoke()
output_data = interpreter.get_tensor(output_details[0]['index'])[0]
top_index = np.argmax(output_data)
disease_name = labels[top_index]
confidence = float(output_data[top_index])
prompt = f"A skin analysis AI has detected {disease_name}. Briefly explain what this is and provide 3 general care tips. End by saying: 'This is not a medical diagnosis; please see a dermatologist.'"
try:
advice = client.text_generation(prompt, max_new_tokens=250)
except:
advice = "Could not fetch advice. Please consult a dermatologist."
return {
"condition": disease_name,
"confidence": f"{confidence*100:.2f}%",
"assistant_advice": advice
}
demo = gr.Interface(
fn=predict_and_advise,
inputs=gr.Image(),
outputs=gr.JSON(),
)
demo.launch()
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