Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+from rdkit import Chem
+from rdkit.Chem import AllChem
+from rdkit.Chem.Draw import rdMolDraw2D
+import base64
+import re
+import py3Dmol
+
+# Drug discovery function
+
+def drug_discovery(disease, symptoms):
+    bio_gpt = pipeline("text-generation", model="microsoft/BioGPT-Large")
+
+    # Detailed medical prompt
+    prompt = (
+        f"Act as a biomedical researcher. For the disease '{disease}' with symptoms '{symptoms}', provide a detailed summary of:\n"
+        "- Causes\n- Diagnosis methods\n- Treatment options\n- Common medications (include drug names)\n"
+        "- Any FDA-approved therapies or hospital protocols\n\nKeep it concise but detailed."
+    )
+
+    try:
+        result = bio_gpt(prompt, max_length=512, do_sample=True, temperature=0.7)[0]['generated_text']
+    except Exception as e:
+        result = f"Could not generate literature due to an error: {e}"
+
+    result = re.sub(r"<\s*/?\s*(TITLE|FREETEXT)\s*>", "", result)
+    result = re.sub(r"^.*?(?=Causes|Diagnosis|Treatment|Common medications)", "", result, flags=re.IGNORECASE)
+
+    # Generate SMILES
+    molecule_prompt = f"Give 5 different valid drug-like SMILES strings that can treat {disease} with symptoms: {symptoms}. Only list SMILES separated by spaces."
+    try:
+        smiles_result = bio_gpt(molecule_prompt, max_length=100)[0]['generated_text']
+    except Exception as e:
+        smiles_result = "C1=CC=CC=C1"
+
+    smiles_matches = re.findall(r"(?<![A-Za-z0-9])[A-Za-z0-9@+\-\[\]\(\)=#$]{5,}(?![A-Za-z0-9])", smiles_result)
+    smiles = None
+    for match in smiles_matches:
+        mol_test = Chem.MolFromSmiles(match)
+        if mol_test:
+            smiles = match
+            break
+    if not smiles:
+        smiles = "C1=CC=CC=C1"
+
+    mol = Chem.MolFromSmiles(smiles)
+    if not mol:
+        return "Invalid SMILES generated", smiles, "", ""
+
+    AllChem.Compute2DCoords(mol)
+    drawer = rdMolDraw2D.MolDraw2DCairo(300, 300)
+    drawer.DrawMolecule(mol)
+    drawer.FinishDrawing()
+    img_data = drawer.GetDrawingText()
+    img_base64 = base64.b64encode(img_data).decode("utf-8")
+    img_html = f'''<div style="text-align:center; margin-top: 10px; animation: fadeIn 2s ease-in-out;">
+        <img src="data:image/png;base64,{img_base64}" alt="2D Molecule" 
+             style="border-radius: 16px; box-shadow: 0 6px 20px rgba(0,255,255,0.3); border: 1px solid #444;">
+        <div style='font-family: Arial, sans-serif; color: #eeeeee; margin-top: 8px; animation: slideUp 1.5s ease-in-out;'>💊 Visualized Drug Molecule (2D)</div>
+    </div>'''
+
+    mol3d = Chem.AddHs(mol)
+    AllChem.EmbedMolecule(mol3d)
+    AllChem.UFFOptimizeMolecule(mol3d)
+    mb = Chem.MolToMolBlock(mol3d)
+
+    viewer = py3Dmol.view(width=420, height=420)
+    viewer.addModel(mb, "mol")
+    viewer.setStyle({"stick": {"colorscheme": "cyanCarbon"}})
+    viewer.setBackgroundColor("black")
+    viewer.zoomTo()
+    viewer.spin(True)
+    viewer_html_raw = viewer._make_html()
+
+    viewer_html = f'''
+    <div style="text-align:center; margin-top: 20px; animation: zoomIn 2s ease-in-out;">
+        <iframe srcdoc="{viewer_html_raw.replace('"', '&quot;')}" 
+                width="440" height="440" frameborder="0" 
+                style="border-radius: 16px; box-shadow: 0 8px 30px rgba(0,255,255,0.35);"></iframe>
+        <div style='font-family: Arial, sans-serif; color: #eeeeee; margin-top: 8px; animation: slideUp 1.5s ease-in-out;'>🧬 Animated 3D Molecule (Stick View)</div>
+    </div>'''
+
+    return result.strip(), smiles, img_html, viewer_html
+
+# Gradio UI
+
+disease_input = gr.Textbox(label="🏥 Enter Disease (e.g., lung cancer)", value="lung cancer")
+symptom_input = gr.Textbox(label="📈 Enter Symptoms (e.g., cough, weight loss)", value="shortness of breath, weight loss")
+lit_output = gr.Textbox(label="🔖 Literature Insights from BioGPT")
+smiles_output = gr.Textbox(label="🧪 SMILES Representation")
+img_output = gr.HTML(label="🖼️ Molecule 2D Visualization")
+viewer_output = gr.HTML(label="🔬 3D Drug Molecule Animation")
+
+custom_css = """
+@keyframes fadeIn {
+    from {opacity: 0;}
+    to {opacity: 1;}
+}
+
+@keyframes slideUp {
+    from {transform: translateY(40px); opacity: 0;}
+    to {transform: translateY(0); opacity: 1;}
+}
+
+@keyframes zoomIn {
+    from {transform: scale(0.5); opacity: 0;}
+    to {transform: scale(1); opacity: 1;}
+}
+
+body {
+    background: linear-gradient(to right, #0f0f0f, #1a1a1a, #000000);
+    color: #eeeeee;
+    font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+}
+
+.gradio-container {
+    animation: fadeIn 1.5s ease-in-out;
+}
+
+.gradio-container .block-label {
+    color: #ffffff;
+}
+"""
+
+iface = gr.Interface(
+    fn=drug_discovery,
+    inputs=[disease_input, symptom_input],
+    outputs=[lit_output, smiles_output, img_output, viewer_output],
+    title="🏥 AI-Powered Drug Discovery for Hospitals",
+    description="This hospital-themed platform takes a disease and symptoms as input, retrieves biomedical insights using BioGPT, and visualizes potential drug molecules in 2D and animated 3D. Ideal for clinical research and pharma innovation.",
+    theme="default",
+    css=custom_css
+)
+
+iface.launch(share=True)