Initial Gradio app for Dental VQA Model Comparison

.gitignore

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+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Virtual Environment
+venv/
+ENV/
+env/
+.venv/
+
+# IDE
+.vscode/
+*.swp
+*.swo
+*~
+
+# OS
+.DS_Store
+Thumbs.db
+
+# Hugging Face
+.gradio/
+gradio_cached_examples/
+
+# Scratch files
+.scratches/
+
+# PyCharm
+.idea/
+
+# Local environment files
+.env
+.env.local
+
+# Model cache
+models/
+.cache/

.idea/.gitignore

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+# Default ignored files
+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml

.idea/dental-vqa-comparison.iml

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+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/inspectionProfiles/profiles_settings.xml

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+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/material_theme_project_new.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="MaterialThemeProjectNewConfig">
+    <option name="metadata">
+      <MTProjectMetadataState>
+        <option name="migrated" value="true" />
+        <option name="pristineConfig" value="false" />
+        <option name="userId" value="-31f020fc:19626a96fac:-7ffa" />
+      </MTProjectMetadataState>
+    </option>
+  </component>
+</project>

.idea/modules.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/dental-vqa-comparison.iml" filepath="$PROJECT_DIR$/.idea/dental-vqa-comparison.iml" />
+    </modules>
+  </component>
+</project>

.idea/vcs.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$" vcs="Git" />
+  </component>
+</project>

README.md

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+---
+title: Dental VQA Model Comparison
+emoji: 🦷
+colorFrom: blue
+colorTo: green
+sdk: gradio
+sdk_version: 4.44.0
+app_file: app.py
+pinned: false
+license: apache-2.0
+models:
+  - yasserrmd/DentaInstruct-1.2B
+---
+
+# Dental VQA Model Comparison
+
+An interactive Gradio interface for comparing dental visual question answering models, currently featuring the DentaInstruct-1.2B model for educational information about dental health and oral care.
+
+## Features
+
+- Interactive chat interface for dental health questions
+- Adjustable generation parameters (temperature, max tokens, etc.)
+- Example questions to get started
+- Mobile-responsive design
+- Clear disclaimers about educational use only
+
+## Important Disclaimer
+
+⚠️ **This model is for educational purposes only.** It is NOT a substitute for professional dental care. Do not use this model for clinical diagnosis or treatment advice. Always consult a qualified dental professional.
+
+## Model Information
+
+- **Base Model**: LFM2-1.2B
+- **Parameters**: 1.17B
+- **Training Data**: Dental subset of MIRIAD dataset
+- **Purpose**: Educational dental information
+
+## Usage
+
+Ask questions about:
+- Dental procedures and treatments
+- Oral health and hygiene
+- Common dental conditions
+- Preventive dental care
+- Dental anatomy and terminology
+
+## Credits
+
+- **Model**: Created by @yasserrmd
+- **Interface**: Space by @chrisvoncsefalvay
+
+## License
+
+Apache-2.0

app.py

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+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Model configuration
+MODEL_ID = "yasserrmd/DentaInstruct-1.2B"
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Initialize model and tokenizer
+print(f"Loading model {MODEL_ID}...")
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+    device_map="auto" if torch.cuda.is_available() else None
+)
+
+if not torch.cuda.is_available():
+    model = model.to(DEVICE)
+
+# Set padding token if not set
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+
+def format_prompt(message, history):
+    """Format the prompt for the model"""
+    messages = []
+    
+    # Add conversation history
+    for user_msg, assistant_msg in history:
+        messages.append({"role": "user", "content": user_msg})
+        if assistant_msg:
+            messages.append({"role": "assistant", "content": assistant_msg})
+    
+    # Add current message
+    messages.append({"role": "user", "content": message})
+    
+    # Apply chat template
+    if hasattr(tokenizer, 'apply_chat_template'):
+        prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    else:
+        # Fallback formatting
+        prompt = ""
+        for msg in messages:
+            if msg["role"] == "user":
+                prompt += f"User: {msg['content']}\n"
+            else:
+                prompt += f"Assistant: {msg['content']}\n"
+        prompt += "Assistant: "
+    
+    return prompt
+
+def generate_response(
+    message,
+    history,
+    temperature=0.7,
+    max_new_tokens=512,
+    top_p=0.95,
+    repetition_penalty=1.1,
+):
+    """Generate response from the model"""
+    
+    # Format the prompt
+    prompt = format_prompt(message, history)
+    
+    # Tokenize input
+    inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=2048)
+    inputs = {k: v.to(model.device) for k, v in inputs.items()}
+    
+    # Generate response
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=max_new_tokens,
+            temperature=temperature,
+            top_p=top_p,
+            repetition_penalty=repetition_penalty,
+            do_sample=True,
+            pad_token_id=tokenizer.pad_token_id,
+            eos_token_id=tokenizer.eos_token_id,
+        )
+    
+    # Decode response
+    response = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
+    
+    return response
+
+# Example questions
+EXAMPLES = [
+    ["What are the main types of dental cavities?"],
+    ["Explain the process of root canal treatment"],
+    ["What is the difference between gingivitis and periodontitis?"],
+    ["How should I care for my teeth after a dental extraction?"],
+    ["What are the benefits of fluoride in dental care?"],
+    ["Explain the stages of tooth development in children"],
+    ["What causes tooth sensitivity and how can it be treated?"],
+    ["Describe the different types of dental fillings available"],
+]
+
+# Custom CSS for styling
+custom_css = """
+.disclaimer {
+    background-color: #fff3cd;
+    border: 1px solid #ffc107;
+    border-radius: 5px;
+    padding: 10px;
+    margin-bottom: 15px;
+}
+"""
+
+# Create Gradio interface
+with gr.Blocks(theme=gr.themes.Soft(), css=custom_css) as demo:
+    gr.Markdown(
+        """
+        # Dental VQA Model Comparison
+        
+        Interactive comparison of dental visual question answering models. Currently featuring DentaInstruct-1.2B for dental education and oral health information.
+        """
+    )
+    
+    gr.HTML(
+        """
+        <div class="disclaimer">
+        <strong>⚠️ Important Disclaimer:</strong><br>
+        This model is for educational purposes only. It is NOT a substitute for professional dental care.
+        Do not use this model for clinical diagnosis or treatment advice. Always consult a qualified dental professional.
+        </div>
+        """
+    )
+    
+    chatbot = gr.Chatbot(
+        height=400,
+        label="Conversation"
+    )
+    
+    msg = gr.Textbox(
+        label="Your dental question",
+        placeholder="Ask a question about dental health, procedures, or oral care...",
+        lines=2
+    )
+    
+    with gr.Row():
+        submit = gr.Button("Send", variant="primary")
+        clear = gr.Button("Clear")
+    
+    with gr.Accordion("Advanced Settings", open=False):
+        temperature = gr.Slider(
+            minimum=0.1,
+            maximum=1.0,
+            value=0.7,
+            step=0.1,
+            label="Temperature",
+            info="Controls randomness in responses"
+        )
+        
+        max_new_tokens = gr.Slider(
+            minimum=64,
+            maximum=1024,
+            value=512,
+            step=64,
+            label="Max New Tokens",
+            info="Maximum length of the response"
+        )
+        
+        top_p = gr.Slider(
+            minimum=0.1,
+            maximum=1.0,
+            value=0.95,
+            step=0.05,
+            label="Top-p",
+            info="Nucleus sampling parameter"
+        )
+        
+        repetition_penalty = gr.Slider(
+            minimum=1.0,
+            maximum=1.5,
+            value=1.1,
+            step=0.05,
+            label="Repetition Penalty",
+            info="Reduces repetition in responses"
+        )
+    
+    gr.Examples(
+        examples=EXAMPLES,
+        inputs=msg,
+        label="Example Questions"
+    )
+    
+    gr.Markdown(
+        """
+        ## About This Model
+        
+        DentaInstruct-1.2B is a specialised language model fine-tuned on dental educational content.
+        It's designed to provide educational information about dental health, procedures, and oral care.
+        
+        **Model Details:**
+        - Base Model: LFM2-1.2B
+        - Parameters: 1.17B
+        - Training Data: Dental subset of MIRIAD dataset
+        - Purpose: Educational dental information
+        
+        **Created by:** @yasserrmd | **Space by:** @chrisvoncsefalvay
+        """
+    )
+    
+    # Event handlers
+    def respond(message, chat_history, temperature, max_new_tokens, top_p, repetition_penalty):
+        response = generate_response(
+            message, 
+            chat_history,
+            temperature,
+            max_new_tokens,
+            top_p,
+            repetition_penalty
+        )
+        chat_history.append((message, response))
+        return "", chat_history
+    
+    msg.submit(
+        respond,
+        [msg, chatbot, temperature, max_new_tokens, top_p, repetition_penalty],
+        [msg, chatbot]
+    )
+    
+    submit.click(
+        respond,
+        [msg, chatbot, temperature, max_new_tokens, top_p, repetition_penalty],
+        [msg, chatbot]
+    )
+    
+    clear.click(lambda: None, None, chatbot, queue=False)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio==4.44.0
+transformers==4.44.2
+torch>=2.0.0
+accelerate==0.33.0
+sentencepiece==0.2.0
+protobuf==5.27.3