Update space

app.py

@@ -1,64 +1,62 @@
+from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
+import tensorflow as tf
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
 
+# Load the tokenizer and model
+model_name = "Zabihin/Symptom_to_Diagnosis"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = TFAutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Clean the input text
+def clean_input(symptom_text):
+    # Remove unwanted characters or non-ASCII characters
+    symptom_text = ''.join([c for c in symptom_text if ord(c) < 128])
+    symptom_text = symptom_text.lower()  # Optional: Convert to lowercase
+    return symptom_text
+
+# Define the predict function
+def predict(symptom_text, chat_history=[]):
+    try:
+        # Clean the input
+        symptom_text = clean_input(symptom_text)
+
+        # Tokenize the input
+        inputs = tokenizer(symptom_text, return_tensors="tf", padding=True, truncation=True, max_length=512)
+
+        # Get model output
+        outputs = model(**inputs)
+        logits = outputs.logits
+        prediction = tf.argmax(logits, axis=-1).numpy()[0]
+
+        # Map the prediction to a label
+        labels = {
+            0: "Allergy", 1: "Arthritis", 2: "Bronchial Asthma", 3: "Cervical Spondylosis", 
+            4: "Chicken Pox", 5: "Common Cold", 6: "Dengue", 7: "Diabetes", 8: "Drug Reaction", 
+            9: "Fungal Infection", 10: "Gastroesophageal Reflux Disease", 11: "Hypertension", 
+            12: "Impetigo", 13: "Jaundice", 14: "Malaria", 15: "Migraine", 16: "Peptic Ulcer Disease", 
+            17: "Pneumonia", 18: "Psoriasis", 19: "Typhoid", 20: "Urinary Tract Infection", 21: "Varicose Veins"
+        }
+
+        diagnosis = labels.get(prediction, "Unknown diagnosis")
+
+        # Add conversation history
+        chat_history.append(("User", symptom_text))
+        chat_history.append(("AI", f"Predicted Diagnosis: {diagnosis}. Please consult a doctor for more accurate results."))
+
+    except Exception as e:
+        chat_history.append(("AI", f"Error: {str(e)}"))
+
+    return chat_history, ""
+
+# Gradio UI
+with gr.Blocks() as interface:
+    gr.Markdown("<h1 style='text-align: center; margin-top: 20px; margin-bottom: 20px; font-size: 36px;'>Medi Mind - Your AI Health Assistant</h1>")
+    chatbot = gr.Chatbot()
+    input_box = gr.Textbox(show_label=False, placeholder="Describe your symptoms here...")
+    send_button = gr.Button("Send")
+
+    input_box.submit(predict, [input_box, chatbot], [chatbot, input_box])
+    send_button.click(predict, [input_box, chatbot], [chatbot, input_box])
 
 if __name__ == "__main__":
-    demo.launch()
+    interface.launch(share=True)

requirements.txt

@@ -1 +1,4 @@
-huggingface_hub==0.25.2
+huggingface_hub==0.25.2
+gradio
+tensorflow
+transformers