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app.py

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+# -*- coding: utf-8 -*-
+"""heai.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1CPgKNfxzP9sPf9nsHmsct1wlUuZL3XpL
+"""
+
+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load model and tokenizer
+model_name = "ibm-granite/granite-3.2-2b-instruct"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+    device_map="auto" if torch.cuda.is_available() else None
+)
+
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+
+# Function to generate LLM response
+def generate_response(prompt, max_length=1024):
+    inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512)
+    if torch.cuda.is_available():
+        inputs = {k: v.to(model.device) for k, v in inputs.items()}
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_length=max_length,
+            temperature=0.7,
+            do_sample=True,
+            pad_token_id=tokenizer.eos_token_id
+        )
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    response = response.replace(prompt, "").strip()
+    return response
+
+# Function for disease prediction
+def disease_prediction(symptoms):
+    prompt = f"""Based on the following symptoms, provide possible medical conditions and general medication suggestions.
+Always emphasize the importance of consulting a doctor for proper diagnosis.
+
+Symptoms: {symptoms}
+
+Possible conditions and recommendations:
+
+**IMPORTANT: This is for informational purposes only. Please consult a healthcare professional for proper diagnosis and treatment.**
+
+Analysis:"""
+    return generate_response(prompt, max_length=1200)
+
+# Function for treatment plan
+def treatment_plan(condition, age, gender, medical_history):
+    prompt = f"""Generate personalized treatment suggestions for the following patient information. Include home remedies and general medication guidelines.
+
+Medical Condition: {condition}
+Age: {age}
+Gender: {gender}
+Medical History: {medical_history}
+
+Personalized treatment plan including home remedies and medication guidelines:
+
+**IMPORTANT: This is for informational purposes only. Please consult a healthcare professional for proper treatment.**
+
+Treatment Plan:"""
+    return generate_response(prompt, max_length=1200)
+
+# Function for chat with patient
+def patient_chat(chat_history, user_input):
+    conversation = chat_history + f"\nPatient: {user_input}\nAI:"
+    response = generate_response(conversation, max_length=800)
+    chat_history += f"\nPatient: {user_input}\nAI: {response}"
+    return chat_history, chat_history
+
+# Build Gradio app
+with gr.Blocks() as app:
+    gr.Markdown("# Medical AI Assistant")
+    gr.Markdown("**Disclaimer: This is for informational purposes only. Always consult healthcare professionals for medical advice.**")
+
+    with gr.Tabs():
+        with gr.TabItem("Patient Chat"):
+            chat_history = gr.Textbox(label="Conversation", lines=15, value="", interactive=False)
+            user_input = gr.Textbox(label="Your Message", placeholder="Describe your symptoms or ask questions...", lines=2)
+            send_btn = gr.Button("Send")
+            send_btn.click(patient_chat, inputs=[chat_history, user_input], outputs=[chat_history, chat_history])
+
+        with gr.TabItem("Disease Prediction"):
+            with gr.Row():
+                with gr.Column():
+                    symptoms_input = gr.Textbox(
+                        label="Enter Symptoms",
+                        placeholder="e.g., fever, headache, cough, fatigue...",
+                        lines=4
+                    )
+                    predict_btn = gr.Button("Analyze Symptoms")
+                with gr.Column():
+                    prediction_output = gr.Textbox(label="Possible Conditions & Recommendations", lines=20)
+            predict_btn.click(disease_prediction, inputs=symptoms_input, outputs=prediction_output)
+
+        with gr.TabItem("Treatment Plans"):
+            with gr.Row():
+                with gr.Column():
+                    condition_input = gr.Textbox(
+                        label="Medical Condition",
+                        placeholder="e.g., diabetes, hypertension, migraine...",
+                        lines=2
+                    )
+                    age_input = gr.Number(label="Age", value=30)
+                    gender_input = gr.Dropdown(
+                        choices=["Male", "Female", "Other"],
+                        label="Gender",
+                        value="Male"
+                    )
+                    history_input = gr.Textbox(
+                        label="Medical History",
+                        placeholder="Previous conditions, allergies, medications or None",
+                        lines=3
+                    )
+                    plan_btn = gr.Button("Generate Treatment Plan")
+                with gr.Column():
+                    plan_output = gr.Textbox(label="Personalized Treatment Plan", lines=20)
+            plan_btn.click(
+                treatment_plan,
+                inputs=[condition_input, age_input, gender_input, history_input],
+                outputs=plan_output
+            )
+
+    app.launch(server_name="0.0.0.0", server_port=7860, share=True)

requirements.txt

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+gradio
+torch
+transformers
+numpy
+sentencepiece
+