Update app.py

app.py

@@ -1,61 +1,24 @@
 from fastai.text.all import *
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-import torch
 import gradio as gr
 
-# 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 = "mental/mental-bert-base-uncased"  # 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)
+
+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)