Create app.py

app.py

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+import gradio as gr
+import openai
+import requests
+from transformers import pipeline
+from langchain.llms import OpenAI as LangOpenAI
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
+from rdkit import Chem
+from rdkit.Chem import AllChem, Draw
+from rdkit.Chem.Draw import rdMolDraw2D
+import base64
+from io import BytesIO
+import py3Dmol
+import re
+
+# === Advanced LLM Configs ===
+openai.api_key = "your-openai-api-key"
+
+# BioGPT and OpenAI Coordinated Agents
+def get_literature_insights(disease, symptoms):
+    bio_gpt = pipeline("text-generation", model="microsoft/BioGPT-Large")
+    prompt = f"Recent drug research for {disease} with symptoms: {symptoms}."
+    return bio_gpt(prompt, max_length=200)[0]['generated_text']
+
+def get_openai_smiles(disease, symptoms):
+    prompt = f"Suggest 3 valid, drug-like SMILES strings that can potentially treat {disease} (symptoms: {symptoms}). Return only SMILES strings separated by space."
+    response = openai.Completion.create(
+        engine="text-davinci-003",
+        prompt=prompt,
+        max_tokens=100
+    )
+    return response.choices[0].text.strip()
+
+# === Multi-agent Molecular Pipeline ===
+def drug_discovery(disease, symptoms):
+    # Agent 1: Literature from BioGPT
+    literature = get_literature_insights(disease, symptoms)
+
+    # Agent 2: Molecule SMILES from OpenAI
+    smiles_result = get_openai_smiles(disease, symptoms)
+    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:
+        if Chem.MolFromSmiles(match):
+            smiles = match
+            break
+    if not smiles:
+        smiles = "C1=CC=CC=C1"  # fallback
+
+    # RDKit 2D Drawing
+    mol = Chem.MolFromSmiles(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);">
+        <div style='color: #eee; margin-top: 8px;'>💊 2D Drug Structure</div></div>'''
+
+    # RDKit 3D Molecule + py3Dmol
+    mol3d = Chem.AddHs(mol)
+    AllChem.EmbedMolecule(mol3d)
+    AllChem.UFFOptimizeMolecule(mol3d)
+    molblock = Chem.MolToMolBlock(mol3d)
+    viewer = py3Dmol.view(width=420, height=420)
+    viewer.addModel(molblock, "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"></iframe>
+        <div style='color: #eee; margin-top: 8px;'>🧬 3D Molecule</div></div>'''
+
+    return literature, smiles, img_html, viewer_html
+
+# === Gradio UI ===
+disease_input = gr.Textbox(label="🧬 Disease (e.g., glioblastoma)", value="glioblastoma")
+symptom_input = gr.Textbox(label="🩸 Symptoms (e.g., seizures, nausea)", value="seizures, nausea")
+lit_output = gr.Textbox(label="📚 Literature from BioGPT")
+smiles_output = gr.Textbox(label="🧪 SMILES Representation")
+img_output = gr.HTML(label="🔬 2D Structure")
+viewer_output = gr.HTML(label="🧬 3D Molecule")
+
+custom_css = """
+@keyframes fadeIn {
+    from {opacity: 0;}
+    to {opacity: 1;}
+}
+@keyframes zoomIn {
+    from {transform: scale(0.5); opacity: 0;}
+    to {transform: scale(1); opacity: 1;}
+}
+body {
+    background: linear-gradient(to right, #141e30, #243b55);
+    color: #ffffff;
+    font-family: 'Segoe UI', sans-serif;
+}
+.gradio-container {
+    animation: fadeIn 2s ease-in-out;
+}
+"""
+
+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 System (CS3235 Project)",
+    description="This real-time LLM-based platform suggests drugs for diseases without known treatments, generates 2D/3D molecules, and provides literature justifications using BioGPT + OpenAI + RDKit.",
+    css=custom_css
+)
+
+iface.launch(share=True)
+