Update app.py

app.py

@@ -2,104 +2,45 @@ import gradio as gr
 import tensorflow as tf
 from transformers import TFAutoModel, AutoTokenizer
 import numpy as np
-import shap
-from scipy.special import softmax
 
-## Model and Tokenizer Setup
-MODEL_NAME = "distilbert-base-uncased-finetuned-sst-2-english"
+# Load pre-trained model
+MODEL_NAME = "cardiffnlp/twitter-roberta-base-sentiment-latest"
 tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
-model = TFAutoModel.from_pretrained(MODEL_NAME)
 
-# Constants
-SEQ_LEN = 128
-CONDITIONS = [
-    "Common Cold", "COVID-19", "Allergies", "Anxiety Disorder", "Skin Infection",
-    "Heart Condition", "Digestive Issues", "Migraine", "Muscle Strain", "Arthritis"
+# Ensure model loads with TensorFlow and compatibility fixes
+model = tf.keras.models.load_model("model.h5", custom_objects={
+    "TFRobertaModel": TFAutoModel.from_pretrained(MODEL_NAME)
+})
+
+# Labels for predictions
+LABELS = [
+    "Cardiologist", "Dermatologist", "ENT Specialist", "Gastroenterologist",
+    "General Physicians", "Neurologist", "Ophthalmologist",
+    "Orthopedist", "Psychiatrist", "Respirologist", "Rheumatologist",
+    "Surgeon"
 ]
 
-# Dynamic Condition Predictions
-def predict_condition(description: str):
-    tokens = tokenizer(
-        description, max_length=SEQ_LEN, truncation=True, padding="max_length", return_tensors="tf"
-    )
-    outputs = model(tokens).last_hidden_state[:, 0, :]  # CLS token output
-    scores = softmax(outputs.numpy())
-    predictions = dict(zip(CONDITIONS, scores.flatten()))
-    return predictions
-
-# Lifestyle Tips
-LIFESTYLE_TIPS = {
-    "Common Cold": "Rest, stay hydrated, and use saline nasal sprays.",
-    "COVID-19": "Quarantine, stay hydrated, and seek medical attention if symptoms worsen.",
-    "Allergies": "Avoid allergens, take antihistamines, and use air purifiers.",
-    "Anxiety Disorder": "Practice mindfulness, exercise, and seek therapy if needed.",
-    "Skin Infection": "Keep the area clean, use topical creams, and consult a dermatologist.",
-    # Add more conditions and tips...
-}
-
-def get_lifestyle_advice(condition: str):
-    return LIFESTYLE_TIPS.get(condition, "Consult a healthcare professional for guidance.")
-
-# Interactive Health Visualization (SHAP)
-def explain_prediction(text: str):
-    explainer = shap.Explainer(lambda x: predict_condition(x), tokenizer)
-    shap_values = explainer([text])
-    return shap.plots.text(shap_values, display=False)
-
-# Symptom Tracker (Simple Implementation)
-symptom_history = []
-
-def log_symptom(symptom: str):
-    symptom_history.append(symptom)
-    return f"Logged: {symptom}. Total symptoms logged: {len(symptom_history)}"
-
-def display_symptom_trends():
-    return "\n".join(symptom_history[-10:])  # Last 10 logged symptoms
-
-# Gradio UI Design
-css = """
-textarea { background-color: transparent; border: 1px solid #6366f1; }
-"""
-with gr.Blocks(title="MedAI Compass", css=css, theme=gr.themes.Soft()) as app:
-    # Header
-    gr.HTML("<h1>MedAI Compass: Comprehensive Symptom and Health Guide</h1>")
-
-    # Section: Symptom Diagnosis
-    with gr.Row():
-        gr.Markdown("## Symptom Diagnosis")
-        input_description = gr.Textbox(label="Describe your symptom")
-        diagnose_btn = gr.Button("Diagnose")
-        diagnosis_output = gr.Label(label="Possible Conditions")
-        diagnose_btn.click(predict_condition, inputs=input_description, outputs=diagnosis_output)
-
-    # Section: SHAP Analysis
-    with gr.Row():
-        gr.Markdown("## Explain Predictions")
-        shap_text_input = gr.Textbox(label="Enter Symptom Description for Analysis")
-        shap_btn = gr.Button("Generate Explanation")
-        shap_output = gr.HTML()
-        shap_btn.click(explain_prediction, inputs=shap_text_input, outputs=shap_output)
-
-    # Section: Personalized Advice
-    with gr.Row():
-        gr.Markdown("## Personalized Health Advice")
-        condition_input = gr.Dropdown(choices=CONDITIONS, label="Select a Condition")
-        advice_output = gr.Textbox(label="Advice")
-        advice_btn = gr.Button("Get Advice")
-        advice_btn.click(get_lifestyle_advice, inputs=condition_input, outputs=advice_output)
-
-    # Section: Symptom Tracker
-    with gr.Row():
-        gr.Markdown("## Symptom Tracker")
-        tracker_input = gr.Textbox(label="Log a Symptom")
-        tracker_btn = gr.Button("Log Symptom")
-        tracker_output = gr.Textbox(label="Logged Symptoms")
-        tracker_btn.click(log_symptom, inputs=tracker_input, outputs=tracker_output)
-        tracker_display_btn = gr.Button("Display Trends")
-        tracker_trends_output = gr.Textbox(label="Symptom Trends")
-        tracker_display_btn.click(display_symptom_trends, outputs=tracker_trends_output)
-
-    # Footer
-    gr.HTML("<p>© 2024 MedAI Compass. All Rights Reserved.</p>")
-
-app.launch()
+# Preprocess input data
+def preprocess_input(text):
+    tokens = tokenizer(text, max_length=128, truncation=True, padding="max_length", return_tensors="tf")
+    return {"input_ids": tokens["input_ids"], "attention_mask": tokens["attention_mask"]}
+
+# Predict from input text
+def predict_specialist(text):
+    inputs = preprocess_input(text)
+    predictions = model.predict(inputs)
+    return {LABELS[i]: float(predictions[0][i]) for i in range(len(LABELS))}
+
+# Gradio UI
+def build_interface():
+    with gr.Blocks() as demo:
+        gr.Markdown("## Welcome to FlinShaHealth")
+        text_input = gr.Textbox(label="Describe your symptoms:")
+        output_label = gr.Label(label="Predicted Specialist")
+        submit_btn = gr.Button("Predict")
+        submit_btn.click(predict_specialist, inputs=text_input, outputs=output_label)
+    return demo
+
+if __name__ == "__main__":
+    app = build_interface()
+    app.launch()