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app.py

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+import gradio as gr
+from transformers import pipeline
+
+def medical_chatbot(question):
+    model_name = "AventIQ-AI/t5-medical-chatbot"
+    generator = pipeline("text2text-generation", model=model_name)
+    
+    instruction = "As a medical expert, provide a detailed and accurate diagnosis based on the patient's symptoms."
+    input_text = f"{instruction} {question}"
+    
+    response = generator(input_text, max_length=256)[0]['generated_text']
+    
+    return response
+
+iface = gr.Interface(
+    fn=medical_chatbot,
+    inputs=gr.Textbox(label="🩺 Ask a Medical Question", placeholder="Describe your symptoms or ask a medical query...", lines=3),
+    outputs=gr.Textbox(label="💡 Expert Diagnosis", interactive=True),
+    title="🔬 AI-Powered Medical Chatbot",
+    description="🤖 Enter a medical question, and the chatbot will generate a detailed response based on expert knowledge.",
+    theme="compact",
+    allow_flagging="never",
+    examples=[
+        ["I have a fever and sore throat. What could it be?"],
+        ["What are the symptoms of diabetes?"],
+        ["How can I manage high blood pressure naturally?"]
+    ],
+)
+
+if __name__ == "__main__":
+    iface.launch()

requirements.txt

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+torch
+transformers
+gradio
+sentencepiece