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app.py

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+import gradio as gr
+import tflite_runtime.interpreter as tflite
+import numpy as np
+from PIL import Image
+import os
+from huggingface_hub import InferenceClient
+
+# 1. Load the TFLite Model
+interpreter = tflite.Interpreter(model_path="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 (Mistral is great for medical info)
+# Note: To use this, add your HF_TOKEN to the Space "Secrets" in Settings
+client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.3", token=os.getenv("HF_TOKEN"))
+
+def predict_and_advise(image):
+    # --- PART A: SKIN DETECTION ---
+    # Preprocess image (resizing to match your model's input size)
+    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]
+    
+    # Get top prediction
+    top_index = np.argmax(output_data)
+    disease_name = labels[top_index]
+    confidence = float(output_data[top_index])
+
+    # --- PART B: MEDICAL AI ASSISTANT ---
+    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
+    }
+
+# 4. Create the API Interface
+demo = gr.Interface(
+    fn=predict_and_advise,
+    inputs=gr.Image(),
+    outputs=gr.JSON(),
+)
+
+demo.launch()

labels.txt

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+acne
+actinic_keratosis
+atopic_dermatitis
+basal_cell_carcinoma
+dermatofibroma
+eczema
+exanthems
+hair_loss
+herpes
+light_diseases
+lupus
+melanocytic_nevi
+melanoma
+nail_fungus
+poison_ivy
+psoriasis
+scabies
+seborrheic_keratosis
+systemic_disease
+tinea
+urticaria
+vascular_tumors
+vasculitis
+warts

requirements.txt.txt

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+tflite-runtime
+numpy
+Pillow
+huggingface_hub

skin_model.tflite

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bfa3b931863e36821fc12e6c589febd1b1f9f5f451e57660054743442c499e37
+size 2839400