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app.py

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+# -*- coding: utf-8 -*-
+"""LookingSoft Radiology Assistant
+
+Automatically generated by Colab.
+
+This file is adapted from:
+    https://colab.research.google.com/#fileId=https%3A//storage.googleapis.com/kaggle-colab-exported-notebooks/rishirajacharya/i-o-25-radiology-with-medgemma-gemini-native-tts.b5cf5dca-3453-45b1-b7c0-ec7c22aedf1b.ipynb
+
+# LookingSoft Radiology Assistant: MedGemma + Gemini TTS
+## Developed by LookingSoft Team
+
+This demo showcases an AI-powered radiology assistant that leverages **MedGemma** for medical image interpretation and **Gemini’s native text-to-speech (TTS)** for natural voice output. The assistant transforms complex radiology reports into easy-to-understand language and delivers it through a user-friendly voice-driven experience—highlighting key areas in radiology images and making insights more accessible.
+
+### 🔐 Securing API Keys
+
+We use secure tokens to authenticate with Hugging Face and Google’s Gemini APIs, ensuring safe and authorized access.
+"""
+
+import spaces
+from google import genai
+from google.genai import types
+import os
+
+gemini_api_key = os.getenv('GEMINI_API_KEY')
+client = genai.Client(api_key=gemini_api_key)
+
+"""### 🧠 Loading MedGemma for Radiology Insights
+
+Here, we load the **MedGemma** model—an image-text model optimized for medical contexts. We apply 4-bit quantization to enhance performance and reduce memory usage on GPUs.
+"""
+
+from transformers import pipeline, BitsAndBytesConfig
+import torch
+
+model_kwargs = dict(torch_dtype=torch.bfloat16, device_map="cuda:0", quantization_config=BitsAndBytesConfig(load_in_4bit=True))
+pipe = pipeline("image-text-to-text", model="google/medgemma-4b-it", model_kwargs=model_kwargs)
+pipe.model.generation_config.do_sample = False
+
+"""### 🩻 Radiology Image Interpretation Logic
+
+This function uses MedGemma to generate a plain-language report based on a given prompt and medical image. It formats the input and passes it to the model for inference.
+"""
+
+from PIL import Image
+
+@spaces.GPU
+def infer(prompt: str, image: Image.Image, system: str = None) -> str:
+    image_filename = "image.png"
+    image.save(image_filename)
+
+    messages = []
+    if system:
+        messages.append({
+            "role": "system",
+            "content": [{"type": "text", "text": system}]
+        })
+    messages.append({
+        "role": "user",
+        "content": [
+            {"type": "text", "text": prompt},
+            {"type": "image", "image": image}
+        ]
+    })
+
+    output = pipe(text=messages, max_new_tokens=2048)
+    response = output[0]["generated_text"][-1]["content"]
+
+    return response
+
+"""### 🔊 Prepare for Gemini's Native TTS
+
+This helper function converts Gemini’s audio output into a `.wav` file—enabling the assistant to speak its reports in a natural-sounding voice.
+"""
+
+import wave
+
+def wave_file(filename, pcm, channels=1, rate=24000, sample_width=2):
+   with wave.open(filename, "wb") as wf:
+      wf.setnchannels(channels)
+      wf.setsampwidth(sample_width)
+      wf.setframerate(rate)
+      wf.writeframes(pcm)
+
+"""### 🤖 Integrating Image Analysis and Voice Output
+
+This function combines the MedGemma analysis with Gemini’s TTS to produce both text and audio responses.
+"""
+
+import gradio as gr
+import requests
+
+def _do_predictions(text, image_file, image_url, source_type):
+    if source_type == "url":
+        image = Image.open(requests.get(image_url, headers={"User-Agent": "example"}, stream=True).raw)
+    else:
+        image = image_file
+    report = infer(text, image)
+
+    response = client.models.generate_content(
+       model="gemini-2.5-flash-preview-tts",
+       contents=report,
+       config=types.GenerateContentConfig(
+          response_modalities=["AUDIO"],
+          speech_config=types.SpeechConfig(
+             voice_config=types.VoiceConfig(
+                prebuilt_voice_config=types.PrebuiltVoiceConfig(
+                   voice_name='Kore',
+                )
+             )
+          ),
+       )
+    )
+
+    data = response.candidates[0].content.parts[0].inline_data.data
+    file_name='out.wav'
+    wave_file(file_name, data)
+
+    return report, file_name
+
+"""### 🖼️ Interactive Web UI with Gradio
+
+A user-friendly interface built with Gradio. Users can upload an image or provide a URL, enter a prompt, and receive both a text report and an audio explanation—powered by **MedGemma + Gemini TTS**.
+"""
+
+def toggle_image_src(choice):
+    if choice == "url":
+        return gr.update(visible=False), gr.update(visible=True)
+    else:
+        return gr.update(visible=True), gr.update(visible=False)
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """
+        # LookingSoft Radiology Assistant: MedGemma + Gemini TTS
+        ## Developed by the LookingSoft Team
+
+        This assistant demonstrates the integration of **MedGemma** for medical image interpretation with **Gemini’s native text-to-speech (TTS)**. It simplifies complex radiology reports into clear, spoken language, making insights more accessible and understandable for both professionals and patients.
+        """
+    )
+    with gr.Row():
+        with gr.Column():
+            with gr.Row():
+                text = gr.Text(label="Instructions", lines=2, interactive=True)
+                with gr.Column():
+                    radio = gr.Radio(["file", "url"], value="file", label="Input Image Source")
+                    image_file = gr.Image(label="File", type="pil", visible=True)
+                    image_url = gr.Textbox(label="URL", visible=False)
+            with gr.Row():
+                submit = gr.Button("Generate")
+        with gr.Column():
+            output = gr.Textbox(label="Generated Report")
+            audio_output = gr.Audio(label="Generated Report (wav)")
+    submit.click(_do_predictions, inputs=[text, image_file, image_url, radio],
+                    outputs=[output, audio_output])
+    radio.change(toggle_image_src, radio, [image_file, image_url], queue=False, show_progress=False)
+    gr.Examples(
+        fn=_do_predictions,
+        examples=[
+                ["Describe this X-ray", Image.open(requests.get("https://google-rad-explain.hf.space/static/images/Effusion2.jpg", headers={"User-Agent": "example"}, stream=True).raw), None, "file"],
+                ["Describe this CT",  None, "https://google-rad-explain.hf.space/static/images/CT-Tumor.jpg", "url"],
+            ],
+        inputs=[text, image_file, image_url, radio],
+        outputs=[output, audio_output]
+    )
+    gr.Markdown("""
+    ### Disclaimer
+    This demonstration is for educational purposes only. It is not intended to diagnose or treat any disease or condition and should not be considered medical advice.
+    """)
+
+    demo.queue(max_size=8 * 4).launch(share=True)

requirements.txt

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+accelerate
+bitsandbytes
+transformers
+gradio
+google-genai