Update app.py

app.py

@@ -1,74 +1,83 @@
 import gradio as gr
+import torch
+from transformers import pipeline
+import tensorflow as tf
+import numpy as np
+from PIL import Image
 import random
 
-# Symptom-based diagnosis function
+# Load AI models
+symptom_model = pipeline("text-classification", model="nlp-ai/symptom-disease")  # Replace with trained model
+emotion_model = pipeline("sentiment-analysis")  # Pre-trained NLP for emotions
+retina_model = tf.keras.models.load_model("retina_disease_model.h5")  # Custom trained Retina AI
+stool_model = tf.keras.models.load_model("stool_microbiome_model.h5")  # Gut microbiome detection AI
+
+# Function for AI-powered diagnosis
 def diagnose(symptoms):
-    symptom_disease_mapping = {
-        "fever, cold, flu": "Possible conditions: Common Cold, Influenza.",
-        "cough, fever, difficulty breathing": "Possible conditions: COVID-19, Pneumonia.",
-        "headache, nausea, dizziness": "Possible conditions: Migraine, Dehydration.",
-        "stomach pain, nausea, vomiting": "Possible conditions: Food Poisoning, Gastritis."
-    }
+    result = symptom_model(symptoms)[0]
+    return f"Condition: {result['label']} (Confidence: {round(result['score'] * 100, 2)}%)"
+
+# Function for stool image microbiome analysis
+def analyze_stool(image):
+    img = Image.open(image).resize((224, 224))
+    img_array = np.array(img) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
     
-    return symptom_disease_mapping.get(symptoms.lower(), "Not enough data, please consult a doctor.")
+    prediction = stool_model.predict(img_array)
+    class_names = ["Healthy Gut", "Possible Dysbiosis", "Severe Gut Imbalance"]
+    return f"Result: {class_names[np.argmax(prediction)]}"
 
-# Gut health analysis function
-def gut_health_analysis(diet):
-    if "junk" in diet.lower():
-        return "Unhealthy gut! Try probiotic-rich foods like yogurt, kimchi, or kefir."
-    elif "fruits" in diet.lower() or "vegetables" in diet.lower():
-        return "Healthy gut! Your microbiome is balanced."
-    else:
-        return "Gut health status unclear. Maintain a fiber-rich diet."
+# Retina disease detection function
+def analyze_retina(image):
+    img = Image.open(image).resize((224, 224))
+    img_array = np.array(img) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
 
-# Retina scan function (Mock output)
-def retina_scan(image):
-    return f"Retina scan result: No abnormalities detected. (For real diagnosis, consult a specialist)"
+    prediction = retina_model.predict(img_array)
+    class_names = ["Normal Retina", "Diabetic Retinopathy", "Glaucoma Detected"]
+    return f"Retina Scan: {class_names[np.argmax(prediction)]}"
 
-# Simple chatbot function
-def chatbot(query):
-    responses = {
-        "hello": "Hello! How can I assist you?",
-        "what is ai": "AI (Artificial Intelligence) is the simulation of human intelligence by machines.",
-        "how to stay healthy": "Eat balanced meals, exercise daily, stay hydrated, and get enough sleep."
+# Emotion-to-disease analysis
+def emotion_to_disease(emotion_text):
+    emotions = {
+        "stress": "Long-term stress can cause high blood pressure, anxiety, and digestive issues.",
+        "anger": "Frequent anger can increase heart disease risk.",
+        "happiness": "A positive mindset improves overall well-being!"
     }
-    return responses.get(query.lower(), "I'm not sure about that. Please consult a doctor.")
+    detected_emotion = emotion_model(emotion_text)[0]['label']
+    return emotions.get(detected_emotion.lower(), "No specific health risks detected.")
 
-# Creating Gradio UI
+# Gradio UI
 with gr.Blocks(theme="soft") as demo:
-    gr.Markdown("# 🔬 Diagnosify-AI: Your AI-powered Health Assistant")
+    gr.Markdown("# 🏥 Diagnosify-AI: AI-powered Health & Disease Detection")
 
-    # Symptom-based diagnosis
+    # Symptom diagnosis
     with gr.Row():
-        symptoms_input = gr.Textbox(label="Enter symptoms (comma separated)")
-        symptoms_output = gr.Textbox(label="Diagnosis Result", interactive=False)
-        diagnose_button = gr.Button("Diagnose")
-        diagnose_button.click(diagnose, inputs=symptoms_input, outputs=symptoms_output)
+        symptom_input = gr.Textbox(label="Enter symptoms")
+        symptom_output = gr.Textbox(label="Diagnosis Result", interactive=False)
+        gr.Button("Diagnose").click(diagnose, inputs=symptom_input, outputs=symptom_output)
 
-    # Gut health analysis
+    # Gut microbiome (stool analysis)
     with gr.Row():
-        diet_input = gr.Textbox(label="Describe your diet")
-        gut_output = gr.Textbox(label="Gut Health Status", interactive=False)
-        gut_button = gr.Button("Analyze Gut Health")
-        gut_button.click(gut_health_analysis, inputs=diet_input, outputs=gut_output)
+        stool_input = gr.Image(label="Upload Stool Image")
+        stool_output = gr.Textbox(label="Microbiome Analysis", interactive=False)
+        gr.Button("Analyze Stool").click(analyze_stool, inputs=stool_input, outputs=stool_output)
 
     # Retina scan
     with gr.Row():
         retina_input = gr.Image(label="Upload Retina Image")
-        retina_output = gr.Textbox(label="Retina Scan Report", interactive=False)
-        retina_button = gr.Button("Analyze Retina Scan")
-        retina_button.click(retina_scan, inputs=retina_input, outputs=retina_output)
+        retina_output = gr.Textbox(label="Retina Disease Detection", interactive=False)
+        gr.Button("Analyze Retina").click(analyze_retina, inputs=retina_input, outputs=retina_output)
 
-    # Simple chatbot
+    # Emotion-to-Disease
     with gr.Row():
-        chat_input = gr.Textbox(label="Ask me anything")
-        chat_output = gr.Textbox(label="Bot Response", interactive=False)
-        chat_button = gr.Button("Ask")
-        chat_button.click(chatbot, inputs=chat_input, outputs=chat_output)
+        emotion_input = gr.Textbox(label="Describe your emotions")
+        emotion_output = gr.Textbox(label="Health Analysis", interactive=False)
+        gr.Button("Analyze Emotion").click(emotion_to_disease, inputs=emotion_input, outputs=emotion_output)
 
-    gr.Markdown("🔗 **Diagnosify-AI** - AI-powered medical insights at your fingertips!")
+    gr.Markdown("🔗 **Diagnosify-AI** - Bringing AI-powered diagnostics to your fingertips!")
 
-# Launching the app
+# Launch the app
 demo.launch()