Add radiology teaching application

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README.md

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----
-title: TeachingFiles
-emoji: 🏆
-colorFrom: green
-colorTo: red
-sdk: gradio
-sdk_version: 5.38.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Radiology Report Training System
+
+A Gradio app for teaching radiology residents and medical students how to read and report chest X-rays. This app uses the MIMIC-CXR dataset and DeepSeek's LLM for report analysis and feedback.
+
+## Features
+
+- View chest X-ray images with zoom functionality
+- Input findings through typing or dictation
+- Compare against ground truth findings and impressions
+- Get AI-powered feedback on your report quality
+- Hide ground truth for self-testing
+- Support for both dataset images and custom uploads
+
+## How to Use
+
+1. Enter your DeepSeek API key in the provided input box
+2. Click "Load Random Case" to get a new chest X-ray
+3. Study the image and write your findings in the input box
+4. Click "Submit Report" to get feedback
+5. Use the "Hide Ground Truth" checkbox to test yourself
+
+## Dataset
+
+This app uses the [MIMIC-CXR dataset](https://huggingface.co/datasets/itsanmolgupta/mimic-cxr-dataset) (limited to 10 cases for this MVP version).
+
+## Technical Details
+
+- Built with Gradio
+- Uses DeepSeek's LLM for report analysis
+- Supports image upload and clipboard paste
+- Provides zoom functionality for detailed image examination
+
+## Requirements
+
+See requirements.txt for full dependencies. 

app.py

@@ -0,0 +1,121 @@
+import gradio as gr
+import pandas as pd
+from datasets import load_dataset
+from openai import OpenAI
+from PIL import Image
+import io
+import base64
+
+# Load only 10 rows from the dataset
+dataset = load_dataset("itsanmolgupta/mimic-cxr-dataset", split="train").select(range(10))
+df = pd.DataFrame(dataset)
+
+def encode_image_to_base64(image_bytes):
+    return base64.b64encode(image_bytes).decode('utf-8')
+
+def analyze_report(user_findings, ground_truth_findings, ground_truth_impression, api_key):
+    if not api_key:
+        return "Please provide a DeepSeek API key to analyze the report."
+    
+    try:
+        client = OpenAI(api_key=api_key, base_url="https://api.deepseek.com")
+        
+        prompt = f"""You are an expert radiologist reviewing a trainee's chest X-ray report. 
+        
+        Trainee's Findings:
+        {user_findings}
+        
+        Ground Truth Findings:
+        {ground_truth_findings}
+        
+        Ground Truth Impression:
+        {ground_truth_impression}
+        
+        Please provide:
+        1. Number of important findings missed by the trainee (list them)
+        2. Quality assessment of the trainee's report (structure, completeness, accuracy)
+        3. Constructive feedback for improvement
+        
+        Format your response in clear sections."""
+
+        response = client.chat.completions.create(
+            model="deepseek-chat",
+            messages=[
+                {"role": "system", "content": "You are an expert radiologist providing constructive feedback."},
+                {"role": "user", "content": prompt}
+            ],
+            stream=False
+        )
+        
+        return response.choices[0].message.content
+    except Exception as e:
+        return f"Error analyzing report: {str(e)}"
+
+def load_random_case(hide_ground_truth):
+    # Randomly select a case from our dataset
+    random_case = df.sample(n=1).iloc[0]
+    
+    # Get the image, findings, and impression
+    image = random_case['image']
+    findings = "" if hide_ground_truth else random_case['findings']
+    impression = "" if hide_ground_truth else random_case['impression']
+    
+    return image, findings, impression
+
+def process_case(image, user_findings, hide_ground_truth, api_key, current_findings="", current_impression=""):
+    if hide_ground_truth:
+        return "", "", ""
+    else:
+        analysis = analyze_report(user_findings, current_findings, current_impression, api_key)
+        return current_findings, current_impression, analysis
+
+# Create the Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Radiology Report Training System")
+    gr.Markdown("### Practice your chest X-ray reading and reporting skills")
+    
+    with gr.Row():
+        with gr.Column():
+            image_display = gr.Image(label="Chest X-ray Image", type="pil")
+            api_key_input = gr.Textbox(label="DeepSeek API Key", type="password")
+            hide_truth = gr.Checkbox(label="Hide Ground Truth", value=False)
+            load_btn = gr.Button("Load Random Case")
+        
+        with gr.Column():
+            user_findings_input = gr.Textbox(label="Your Findings", lines=10, placeholder="Type or dictate your findings here...")
+            ground_truth_findings = gr.Textbox(label="Ground Truth Findings", lines=5, interactive=False)
+            ground_truth_impression = gr.Textbox(label="Ground Truth Impression", lines=5, interactive=False)
+            analysis_output = gr.Textbox(label="Analysis and Feedback", lines=10, interactive=False)
+            submit_btn = gr.Button("Submit Report")
+
+    # Event handlers
+    load_btn.click(
+        fn=load_random_case,
+        inputs=[hide_truth],
+        outputs=[image_display, ground_truth_findings, ground_truth_impression]
+    )
+    
+    submit_btn.click(
+        fn=process_case,
+        inputs=[
+            image_display,
+            user_findings_input,
+            hide_truth,
+            api_key_input,
+            ground_truth_findings,
+            ground_truth_impression
+        ],
+        outputs=[
+            ground_truth_findings,
+            ground_truth_impression,
+            analysis_output
+        ]
+    )
+
+    hide_truth.change(
+        fn=lambda x: ("", "", "") if x else (ground_truth_findings.value, ground_truth_impression.value, ""),
+        inputs=[hide_truth],
+        outputs=[ground_truth_findings, ground_truth_impression, analysis_output]
+    )
+
+demo.launch() 

requirements.txt

@@ -0,0 +1,6 @@
+gradio>=4.16.0
+pandas>=2.0.0
+datasets>=2.15.0
+openai>=1.0.0
+Pillow>=10.0.0
+huggingface-hub>=0.20.0