Create app.py

app.py

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+import gradio as gr
+import random
+import base64
+from io import BytesIO
+from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
+from rdkit import Chem
+from rdkit.Chem import Draw, AllChem
+import torch
+import py3Dmol
+
+# Load NLP & ML models
+bio_gpt = pipeline("text-generation", model="microsoft/BioGPT-Large")
+chemberta_tokenizer = AutoTokenizer.from_pretrained("seyonec/ChemBERTa-zinc-base-v1")
+chemberta_model = AutoModelForCausalLM.from_pretrained("seyonec/ChemBERTa-zinc-base-v1")
+compliance_qa = pipeline("question-answering", model="nlpaueb/legal-bert-base-uncased")
+
+def extract_insights(disease, symptoms):
+    prompt = f"Recent treatments for {disease} with symptoms: {symptoms}"
+    try:
+        result = bio_gpt(prompt, max_length=200, do_sample=True)
+        return result[0]['generated_text']
+    except Exception as e:
+        return f"Error extracting insights: {str(e)}"
+
+def generate_molecule():
+    sample_smiles = ["CCO", "CCN", "C1=CC=CC=C1", "C(C(=O)O)N", "CC(C)CC"]
+    return random.choice(sample_smiles)
+
+def predict_properties(smiles):
+    try:
+        inputs = chemberta_tokenizer(smiles, return_tensors="pt")
+        with torch.no_grad():
+            outputs = chemberta_model(**inputs)
+        return round(outputs.logits.mean().item(), 3)
+    except Exception as e:
+        return f"Error predicting properties: {str(e)}"
+
+def visualize_molecule(smiles):
+    try:
+        mol = Chem.MolFromSmiles(smiles)
+        img = Draw.MolToImage(mol, size=(300, 300))
+        buf = BytesIO()
+        img.save(buf, format="PNG")
+        return buf.getvalue()
+    except Exception as e:
+        return f"Error visualizing molecule: {str(e)}"
+
+def generate_3d_structure(smiles):
+    try:
+        mol = Chem.MolFromSmiles(smiles)
+        mol = Chem.AddHs(mol)
+        AllChem.EmbedMolecule(mol, AllChem.ETKDG())
+        AllChem.UFFOptimizeMolecule(mol)
+        mol_block = Chem.MolToMolBlock(mol)
+        viewer = py3Dmol.view(width=400, height=400)
+        viewer.addModel(mol_block, "mol")
+        viewer.setStyle({"stick": {}})
+        viewer.zoomTo()
+        return viewer._make_html()
+    except Exception as e:
+        return f"Error generating 3D molecule: {str(e)}"
+
+def check_compliance():
+    try:
+        question = "What does FDA require for drug testing?"
+        context = "FDA requires extensive testing for new drug candidates including Phase I, II, and III clinical trials."
+        return compliance_qa(question=question, context=context)['answer']
+    except Exception as e:
+        return f"Error checking compliance: {str(e)}"
+
+def full_pipeline(disease, symptoms):
+    insights = extract_insights(disease, symptoms)
+    smiles = generate_molecule()
+    mol_img = visualize_molecule(smiles)
+    score = predict_properties(smiles)
+    mol_3d_html = generate_3d_structure(smiles)
+    compliance = check_compliance()
+    return insights, mol_img, f"{smiles} | Score: {score}", mol_3d_html, compliance
+
+demo = gr.Interface(
+    fn=full_pipeline,
+    inputs=[
+        gr.Textbox(label="Disease", value="lung cancer"),
+        gr.Textbox(label="Symptoms", value="shortness of breath, weight loss")
+    ],
+    outputs=[
+        gr.Textbox(label="Literature Insights"),
+        gr.Image(label="2D Molecule"),
+        gr.Textbox(label="Molecule Info"),
+        gr.HTML(label="3D Molecule Structure"),
+        gr.Textbox(label="Compliance Info")
+    ],
+    title="🧬 AI-Driven Drug Discovery",
+    description="Enter a disease and symptoms to generate and analyze potential drug candidates using AI."
+)
+
+if __name__ == "__main__":
+    demo.launch()