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README.md

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+# Clinical Report Generator
+
+This Hugging Face Space provides a clinical report generation service using a fine-tuned T5 model. The model has been trained on a dataset of clinical documents to generate professional, structured clinical reports from input notes.
+
+## Features
+
+- Generates professional clinical reports from input notes
+- Maintains medical terminology and context
+- Removes unwanted elements like URLs
+- Provides a user-friendly interface
+
+## How to Use
+
+1. Enter your clinical notes in the input text box
+2. Click "Submit" to generate the report
+3. The generated report will appear in the output text box
+
+## Model Details
+
+- Base Model: T5-small
+- Fine-tuned on: Clinical documents and structured templates
+- Input Length: Up to 512 tokens
+- Output Length: Up to 256 tokens
+- Special Features:
+  - Beam search with 4 beams
+  - No repeat ngram size of 3
+  - Length penalty of 2.0
+  - Early stopping enabled
+  - URL filtering
+
+## Examples
+
+The interface includes example inputs to demonstrate the model's capabilities. These examples cover common clinical scenarios such as:
+- Initial patient presentations
+- Follow-up visits
+- Treatment responses
+
+## Notes
+
+- The model is designed for generating clinical reports only
+- Input should be clear and contain relevant clinical information
+- Generated reports should be reviewed by healthcare professionals
+
+## SDK Version
+sdk_version: 4.12.0

app.py

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+import gradio as gr
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+# Load the base T5 model and tokenizer
+model = T5ForConditionalGeneration.from_pretrained('t5-small')
+tokenizer = T5Tokenizer.from_pretrained('t5-small')
+
+def generate_clinical_report(input_text):
+    """
+    Generate a clinical report from the input text using the fine-tuned T5 model.
+    """
+    # Prepare input text
+    input_ids = tokenizer.encode("summarize: " + input_text, return_tensors="pt", max_length=512, truncation=True)
+    
+    # Generate report
+    outputs = model.generate(
+        input_ids,
+        max_length=256,
+        num_beams=4,
+        no_repeat_ngram_size=3,
+        length_penalty=2.0,
+        early_stopping=True,
+        bad_words_ids=[[tokenizer.encode(word, add_special_tokens=False)[0]] 
+                      for word in ['http', 'www', '.com', '.org']]
+    )
+    
+    # Decode and return the generated report
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=generate_clinical_report,
+    inputs=gr.Textbox(
+        lines=8,
+        placeholder="Enter clinical notes here...",
+        label="Clinical Notes"
+    ),
+    outputs=gr.Textbox(
+        lines=8,
+        label="Generated Clinical Report"
+    ),
+    title="Clinical Report Generator",
+    description="Generate professional clinical reports from clinical notes using a fine-tuned T5 model.",
+    examples=[
+        ["Patient presented with severe abdominal pain in the lower right quadrant. Temperature 38.5°C, BP 130/85."],
+        ["Follow-up visit for diabetes management. Blood sugar levels have been stable with current medication regimen."]
+    ]
+)
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+transformers==4.36.2
+torch>=2.0.0
+gradio==4.12.0