---
title: MedGemma Radiology Report Generator
emoji: 🩻
colorFrom: purple
colorTo: indigo
sdk: gradio
python_version: '3.10'
app_file: app.py
sdk_version: 5.39.0
---

# 🏥 MedGemma Radiology Report Generator
### Created by **CultriX**

This Hugging Face Space demonstrates the capabilities of Google's **MedGemma 4b-it** model, a medical-focused LLM designed for both image comprehension and medical text generation.

The application is accelerated by **ZeroGPU** for fast inference and features a dual-tab interface for different clinical workflows.

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### ✨ Features

1.  **🩻 X-Ray Analysis Tab**
    * **Multimodal Analysis:** Upload an X-ray to receive a structured radiology report (Findings, Impression, Recommendations).
    * **Clinical Context:** Optionally provide patient history (e.g., "65M, cough for 3 weeks") to guide the model's interpretation.
    * **Interactive Chat:** Ask follow-up questions about specific findings directly in the chat window after the report is generated.
    * **Token Management:** Real-time token counting ensures your input stays within the model's context window.

2.  **💬 Medical Assistant Tab**
    * **Text-Only Mode:** Chat with the MedGemma model about general medical concepts, differential diagnoses, or terminology without uploading an image.

---

### 🕹️ How to Use

#### For X-Ray Analysis:
1.  Go to the **"🩻 X-Ray Analysis"** tab.
2.  **Upload** a chest X-ray image (PNG or JPEG).
3.  *(Optional)* Enter relevant **Clinical Information** in the text box.
4.  Click **"🔬 Generate Report"**.
5.  Once the report appears, use the chat box below it to ask **follow-up questions** (e.g., "Can you explain the pleural effusion finding?").

#### For General Questions:
1.  Switch to the **"💬 Medical Assistant"** tab.
2.  Type your medical question and hit Enter.

---

### 🧠 Model Information

-   **Model:** `google/medgemma-4b-it`
-   **Architecture:** MedGemma is built on top of Gemma 3, fine-tuned with medical instruction data.
-   **Hardware:** Powered by Hugging Face **ZeroGPU** (dynamic H100/A100 allocation) for efficient inference.
-   **Requirements:** This Space uses `sentencepiece` and `protobuf` for tokenizer handling.

---

### ⚠️ Disclaimer

This application is intended for **research and demonstration purposes only**. 
* It should **not** be used for clinical decision-making. 
* The model may hallucinate findings or miss critical anomalies.
* All generated reports must be reviewed and validated by a qualified medical professional.

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### 🔧 Known Limitations

-   **Image Formats:** DICOM files are not yet supported — please convert to PNG or JPEG before uploading.
-   **Model Size:** This demo uses the **4B** parameter version. While fast, it may be less accurate than the larger 27B variant.
-   **ZeroGPU Quotas:** Inference speed and availability depend on the current load of the ZeroGPU cluster.

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Feel free to fork this Space to customize the system prompts or integrate it into your own clinical AI research workflows!