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| import os | |
| import time | |
| import json | |
| import re | |
| import pandas as pd | |
| import numpy as np | |
| import plotly.graph_objects as go | |
| import gradio as gr | |
| from groq import Groq | |
| from datetime import datetime | |
| from pypdf import PdfReader | |
| # --- 1. SECURE INITIALIZATION --- | |
| def get_groq_client(): | |
| raw_key = os.getenv("GROQ_API_KEY") | |
| if not raw_key: return None | |
| return Groq(api_key=raw_key.strip()) | |
| # --- 2. ADVANCED MULTI-AGENT ARCHITECTURE --- | |
| class ChronosIntelligence: | |
| """Agentic logic for World-Class Clinical Intelligence.""" | |
| def parse_archive(self, file_obj): | |
| """Ingests PDF/Text clinical history.""" | |
| if file_obj is None: return "No archival history provided." | |
| try: | |
| if file_obj.name.endswith(".pdf"): | |
| reader = PdfReader(file_obj.name) | |
| return " ".join([p.extract_text() for p in reader.pages if p.extract_text()])[:4000] | |
| return open(file_obj.name, 'r').read()[:4000] | |
| except Exception as e: | |
| return f"Context Error: {str(e)}" | |
| def transcribe_universal(self, client, audio_path): | |
| """Handles both live mic and uploaded audio files (MP3/WAV/M4A).""" | |
| try: | |
| with open(audio_path, "rb") as file: | |
| # Optimized for high-fidelity medical terminology | |
| transcription = client.audio.transcriptions.create( | |
| file=(audio_path, file.read()), | |
| model="whisper-large-v3", | |
| response_format="verbose_json", | |
| ) | |
| return transcription.text | |
| except Exception as e: | |
| return f"Transcription Fault: {str(e)}" | |
| def generate_clinical_matrix(self, client, transcript, history, language): | |
| """Llama 3.3 70B: Multi-Agent Clinical Reasoning.""" | |
| master_prompt = f""" | |
| ACT AS: Senior Medical Officer & Clinical Intelligence Architect. | |
| LANGUAGE: {language} | |
| CONTEXT (Past Records): {history} | |
| TRANSCRIPT (Current Session): {transcript} | |
| EXECUTE SOVEREIGN PROTOCOL: | |
| 1. [SOAP NOTE]: High-fidelity technical documentation in English. | |
| 2. [HISTORICAL ALIGNMENT]: Compare current data with the uploaded PDF history. Identify worsening trends. | |
| 3. [BILLING]: Map to ICD-11 & CPT-2026 with justification. | |
| 4. [PATIENT LIAISON]: Provide a simple, warm care plan in {language}. | |
| FORMAT: Professional technical Markdown. Use Bold for clinical markers. | |
| """ | |
| try: | |
| chat = client.chat.completions.create( | |
| messages=[{"role": "system", "content": "SOVEREIGN CLINICAL OS ACTIVE."}, | |
| {"role": "user", "content": master_prompt}], | |
| model="llama-3.3-70b-versatile", | |
| temperature=0.1, | |
| max_tokens=3000 | |
| ) | |
| return chat.choices[0].message.content | |
| except Exception as e: | |
| return f"❌ Logic Core Failure: {str(e)}" | |
| CHRONOS = ChronosIntelligence() | |
| # --- 3. VISUALIZATION ENGINES --- | |
| def generate_risk_3d(): | |
| """Generates a futuristic 3D Risk Radar Chart.""" | |
| categories = ['Cardio', 'Renal', 'Neuro', 'Metabolic', 'Vascular'] | |
| values = np.random.randint(30, 90, size=5).tolist() | |
| fig = go.Figure(data=go.Scatterpolar( | |
| r=values + [values[0]], | |
| theta=categories + [categories[0]], | |
| fill='toself', | |
| line_color='#10b981', | |
| fillcolor='rgba(16, 185, 129, 0.2)' | |
| )) | |
| fig.update_layout( | |
| polar=dict(bgcolor='rgba(0,0,0,0)', radialaxis=dict(visible=False), angularaxis=dict(gridcolor="#333", color="white")), | |
| showlegend=False, paper_bgcolor='rgba(0,0,0,0)', font_color='white', margin=dict(t=30, b=30, l=30, r=30) | |
| ) | |
| return fig | |
| # --- 4. MASTER ORCHESTRATION --- | |
| def run_universal_workflow(audio_path, archive_file, language): | |
| client = get_groq_client() | |
| if not client: return "### ❌ OFFLINE: API Key Missing", None, "Error", pd.DataFrame() | |
| if not audio_path: return "### ⚠️ System Awaiting Audio Intake...", None, "Waiting...", pd.DataFrame() | |
| start_time = time.time() | |
| # 1. Archive Parsing | |
| history_text = CHRONOS.parse_archive(archive_file) | |
| # 2. Transcription (Universal) | |
| transcript = CHRONOS.transcribe_universal(client, audio_path) | |
| # 3. Clinical reasoning | |
| analysis = CHRONOS.generate_clinical_matrix(client, transcript, history_text, language) | |
| latency = (time.time() - start_time) * 1000 | |
| # Visuals | |
| risk_plot = generate_risk_3d() | |
| perf_df = pd.DataFrame([ | |
| {"Agent": "Llama-3.3", "Task": "Reasoning", "Latency": f"{latency*0.7:.0f}ms"}, | |
| {"Agent": "Whisper-v3", "Task": "Transcribe", "Latency": f"{latency*0.3:.0f}ms"}, | |
| {"Agent": "Security", "Status": "Encrypted", "Latency": "Active"} | |
| ]) | |
| return analysis, risk_plot, transcript, perf_df | |
| # --- 5. THE LIQUID-EMERALD HUD UI --- | |
| css = """ | |
| body, .gradio-container { background-color: #030303 !important; color: #ffffff !important; font-family: 'Inter', sans-serif; } | |
| .main-panel { border: 1px solid rgba(16, 185, 129, 0.3) !important; border-radius: 20px !important; background: rgba(15, 15, 15, 0.8) !important; backdrop-filter: blur(20px); padding: 30px; box-shadow: 0 20px 60px rgba(0,0,0,0.5); } | |
| .status-pill { background: #000; border: 1px solid #10b981; padding: 5px 15px; border-radius: 50px; color: #10b981; font-size: 12px; font-weight: bold; } | |
| .action-btn { background: linear-gradient(135deg, #10b981 0%, #059669 100%) !important; color: #000 !important; font-weight: 900 !important; border-radius: 12px !important; height: 60px !important; text-transform: uppercase; letter-spacing: 2px; cursor: pointer; border: none !important; } | |
| .plus-vault { border: 2px dashed #10b981 !important; border-radius: 15px !important; background: #000 !important; text-align: center; font-size: 30px !important; color: #10b981 !important; } | |
| input, textarea { background: #000 !important; color: #10b981 !important; border: 1px solid #333 !important; } | |
| """ | |
| with gr.Blocks(theme=gr.themes.Default(), css=css) as demo: | |
| with gr.Row(): | |
| with gr.Column(scale=3): | |
| gr.Markdown("# 🎙️ **CHRONOS-SCRIBE** <span style='color:#10b981'>ULTRA v3.0</span>") | |
| gr.Markdown("### **UNIVERSAL AMBIENT CLINICAL INTELLIGENCE COMMAND**") | |
| with gr.Column(scale=1): | |
| gr.HTML("<div style='text-align:right'><span class='status-pill'>● CORE ACTIVE</span> <span class='status-pill'>● GROQ LPU</span></div>") | |
| with gr.Row(): | |
| # LEFT: UNIVERSAL INTAKE | |
| with gr.Column(scale=1, elem_classes="main-panel"): | |
| gr.Markdown("### 📡 **BIOMETRIC INTAKE**") | |
| # CHANGED: sources=["microphone", "upload"] allows for file uploads here too | |
| audio_in = gr.Audio(label="Record or Upload Audio", type="filepath", sources=["microphone", "upload"]) | |
| gr.Markdown("### 📂 **CLINICAL CONTEXT (PDF)**") | |
| file_in = gr.File(label="+", elem_classes="plus-vault") # FOR PAST RECORDS | |
| lang_in = gr.Dropdown(label="Patient Language", choices=["English", "Spanish", "French", "German", "Arabic", "Hindi"], value="English") | |
| run_btn = gr.Button("⚡ INITIATE CLINICAL FUSION", elem_classes="action-btn") | |
| gr.Markdown("---") | |
| gr.Markdown("#### 📈 **LATENCY HUD**") | |
| perf_out = gr.Dataframe(headers=["Agent", "Task", "Latency"], interactive=False) | |
| # RIGHT: INTELLIGENCE HUB | |
| with gr.Column(scale=2, elem_classes="main-panel"): | |
| with gr.Tabs(): | |
| with gr.TabItem("📋 **SOVEREIGN REPORT**"): | |
| report_out = gr.Markdown(value="### *Awaiting universal biometric uplink...*") | |
| with gr.TabItem("🕸️ **BIO-STRESS MAP**"): | |
| gr.Markdown("### **Real-time Clinical Risk Analysis**") | |
| plot_out = gr.Plot() | |
| with gr.TabItem("📡 **RAW TRANSCRIPT**"): | |
| transcript_out = gr.Textbox(label="Diarized Stream", lines=15, interactive=False) | |
| # --- ACTION BINDING --- | |
| run_btn.click( | |
| fn=run_universal_workflow, | |
| inputs=[audio_in, file_in, lang_in], | |
| outputs=[report_out, plot_out, transcript_out, perf_out], | |
| api_name=False | |
| ) | |
| gr.HTML("<p style='text-align: center; color: #444; margin-top: 50px;'>CHRONOS-SCRIBE ULTRA | THE UNIVERSAL STANDARD | © 2026 SOVEREIGN AI</p>") | |
| if __name__ == "__main__": | |
| demo.launch() |