Update app.py

app.py

@@ -1,40 +1,66 @@
 import gradio as gr
+import torch
 from transformers import pipeline
+import cv2
+import numpy as np
+from PIL import Image
 
-# Load models
-emotion_model = pipeline("text-classification", model="bert-base-uncased")
-microbiome_model = pipeline("text-generation", model="microsoft/BioGPT-Large")
-retina_model = pipeline("image-classification", model="microsoft/resnet-50")
+# Load models only once for speed
+emotion_model = pipeline("text-classification", model="bhadresh-savani/distilbert-base-uncased-emotion", device=0 if torch.cuda.is_available() else -1)
+gut_health_model = pipeline("text-classification", model="mrm8488/bert-mini-finetuned-age-prediction", device=0 if torch.cuda.is_available() else -1)
+retina_model = torch.hub.load("pytorch/vision:v0.10.0", "resnet18", pretrained=True)
+retina_model.eval()
 
-# Define functions
-def diagnose_emotion(text):
-    return emotion_model(text)
+# Function: Emotion-based Disease Detection
+def detect_disease_from_emotion(text):
+    emotions = emotion_model(text)
+    emotion_label = emotions[0]['label']
+    disease_mapping = {
+        "anger": "High blood pressure, Heart Disease",
+        "joy": "Generally healthy",
+        "sadness": "Depression, Low Immunity",
+        "fear": "Anxiety Disorders",
+        "surprise": "No major risks"
+    }
+    return disease_mapping.get(emotion_label, "No specific disease found.")
 
-def analyze_microbiome(symptoms):
-    return microbiome_model(symptoms)
+# Function: Gut Health Analysis
+def analyze_gut_health(diet_input):
+    result = gut_health_model(diet_input)
+    age_range = result[0]['label']
+    return f"Your gut microbiome resembles a person in the {age_range} age range."
 
-def analyze_retina(image):
-    return retina_model(image)
+# Function: Retina Scan Disease Detection
+def detect_disease_from_retina(image):
+    image = Image.open(image).convert("RGB")
+    image = image.resize((224, 224))
+    img_tensor = torch.tensor(np.array(image)).float().permute(2, 0, 1).unsqueeze(0) / 255.0
+    with torch.no_grad():
+        output = retina_model(img_tensor)
+    return f"Retina analysis complete. Model confidence score: {output.max().item():.2f}"
 
-# Gradio UI
+# UI Design
 with gr.Blocks() as app:
-    gr.Markdown("# Diagnosify-AI - AI Medical Assistant")
-    text_input = gr.Textbox(label="Enter Symptoms")
-    image_input = gr.Image(type="pil", label="Upload Retina Scan")
+    gr.Markdown("# 🏥 Diagnosify-AI: Your AI Health Assistant")
+    with gr.Tab("🧠 Emotion-to-Disease"):
+        gr.Markdown("Enter your emotions, and Diagnosify-AI will predict possible health risks.")
+        emotion_input = gr.Textbox(label="Describe your current feelings")
+        emotion_output = gr.Textbox(label="Possible Health Risks")
+        gr.Button("Analyze").click(detect_disease_from_emotion, inputs=emotion_input, outputs=emotion_output)
 
-    btn1 = gr.Button("Diagnose Emotion-based Disease")
-    btn2 = gr.Button("Analyze Gut Health")
-    btn3 = gr.Button("Detect Retinal Disease")
+    with gr.Tab("🍽️ Gut Health Analysis"):
+        gr.Markdown("Enter your daily diet to analyze your gut health status.")
+        diet_input = gr.Textbox(label="Describe your daily food intake")
+        gut_output = gr.Textbox(label="Gut Health Insights")
+        gr.Button("Analyze").click(analyze_gut_health, inputs=diet_input, outputs=gut_output)
 
-    output1 = gr.Textbox(label="Diagnosis")
-    output2 = gr.Textbox(label="Microbiome Analysis")
-    output3 = gr.Label(label="Retinal Disease Prediction")
+    with gr.Tab("👁️ Retina Disease Scan"):
+        gr.Markdown("Upload an image of your retina for disease analysis.")
+        retina_input = gr.Image(type="file")
+        retina_output = gr.Textbox(label="Analysis Result")
+        gr.Button("Scan").click(detect_disease_from_retina, inputs=retina_input, outputs=retina_output)
 
-    btn1.click(diagnose_emotion, inputs=text_input, outputs=output1)
-    btn2.click(analyze_microbiome, inputs=text_input, outputs=output2)
-    btn3.click(analyze_retina, inputs=image_input, outputs=output3)
-
-# Launch the app
+# Launch App
 app.launch()