Update app.py

app.py

@@ -1,24 +1,61 @@
 from fastai.text.all import *
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
 import gradio as gr
 
-
-learn = load_learner('model.pkl')
-
-
-description = "Medical Diagnosis"
-categories = (['Allergy', 'Anemia', 'Bronchitis', 'Diabetes', 'Diarrhea', 'Fatigue', 'Flu', 'Malaria', 'Stress'])
-
-
-
-def classify_text(txt):
-    pred,idx,probs = learn.predict(txt)
-    return dict(zip(categories, map(float,probs)))
-
-
-
-text = gr.Textbox(lines=2, label='Describe how you feel in great detail')
-label = gr.Label()
-examples = ['I have no intrest in physical activity. i am always thirsty', 'I am freezing', 'My eyes are pale']
-
-intf = gr.Interface(fn=classify_text, inputs=text, outputs=label, examples=examples, description=description)
-intf.launch(inline=False)
+# Load the medical model
+medical_learn = load_learner('model.pkl')
+
+# Medical model configuration
+medical_description = "Medical Diagnosis"
+medical_categories = ['Allergy', 'Anemia', 'Bronchitis', 'Diabetes', 'Diarrhea', 'Fatigue', 'Flu', 'Malaria', 'Stress']
+
+def classify_medical_text(txt):
+    pred, idx, probs = medical_learn.predict(txt)
+    return dict(zip(medical_categories, map(float, probs)))
+
+# Load the psychiatric model from Hugging Face
+psychiatric_model_name = "nlp4good/psych-search"  # Replace with the appropriate model
+psychiatric_tokenizer = AutoTokenizer.from_pretrained(psychiatric_model_name)
+psychiatric_model = AutoModelForSequenceClassification.from_pretrained(psychiatric_model_name)
+
+# Psychiatric model configuration
+psychiatric_description = "Psychiatric Analysis"
+psychiatric_labels = ['Depression', 'Anxiety', 'Bipolar Disorder', 'PTSD', 'OCD', 'Stress', 'Schizophrenia']  # Adjust based on the model
+
+def classify_psychiatric_text(txt):
+    inputs = psychiatric_tokenizer(txt, return_tensors="pt", truncation=True, padding=True)
+    with torch.no_grad():
+        outputs = psychiatric_model(**inputs)
+    logits = outputs.logits
+    probabilities = torch.softmax(logits, dim=1).squeeze().tolist()
+    return dict(zip(psychiatric_labels, probabilities))
+
+# Gradio Interfaces
+medical_text = gr.Textbox(lines=2, label='Describe your symptoms in detail')
+medical_label = gr.Label()
+medical_examples = ['I feel short of breath and have a high fever.', 'My throat hurts and I keep sneezing.', 'I am always thirsty.']
+
+psychiatric_text = gr.Textbox(lines=2, label='Describe your mental health concerns in detail')
+psychiatric_label = gr.Label()
+psychiatric_examples = ['I feel hopeless and have no energy.', 'I am unable to concentrate and feel anxious all the time.', 'I have recurring intrusive thoughts.']
+
+medical_interface = gr.Interface(
+    fn=classify_medical_text,
+    inputs=medical_text,
+    outputs=medical_label,
+    examples=medical_examples,
+    description=medical_description,
+)
+
+psychiatric_interface = gr.Interface(
+    fn=classify_psychiatric_text,
+    inputs=psychiatric_text,
+    outputs=psychiatric_label,
+    examples=psychiatric_examples,
+    description=psychiatric_description,
+)
+
+# Combine interfaces using Tabs
+app = gr.TabbedInterface([medical_interface, psychiatric_interface], ["Medical Diagnosis", "Psychiatric Analysis"])
+app.launch(inline=False)