Simplify to minimal backend API

- Remove unnecessary UI elements (examples, article, theme)
- Simplify interface for API-focused use
- Update README with API usage documentation
- Add API endpoint information and examples
- Remove unused imports

README.md

@@ -10,35 +10,53 @@ pinned: false
 license: apache-2.0
 ---
 
-# 🫀 ECG AI7 - Intelligent ECG Interpretation
+# 🫀 ECG AI7 Backend API
 
-An AI-powered ECG interpretation tool using Llama 3.2 11B Vision, fine-tuned specifically for electrocardiogram analysis.
+Backend API for ECG interpretation using Llama 3.2 11B Vision model.
 
-## Features
+## API Usage
 
-- **Advanced ECG Analysis**: Powered by Llama 3.2 11B Vision model fine-tuned on ECG data
-- **Bilingual Support**: Generate reports in both English and Farsi (Persian)
-- **Clinical Context**: Add patient information for more personalized interpretations
-- **User-Friendly Interface**: Simple upload and analyze workflow
+### Using Python Client
 
-## How to Use
+```python
+from gradio_client import Client, handle_file
 
-1. Upload an ECG image (12-lead ECG works best)
-2. Optionally add patient information or clinical notes
-3. Select your preferred output language (English or Farsi)
-4. Click Submit and wait for the AI analysis
+client = Client("AKSazgar/ECG-Instruct-Llama-3.2-11B-Vision")
 
-## Important Disclaimer
+result = client.predict(
+    handle_file("path/to/ecg.jpg"),  # ECG image
+    "55-year-old male with chest pain",  # Clinical note (optional)
+    "Farsi",  # Language: "English" or "Farsi"
+    api_name="/predict"
+)
 
-⚠️ This tool is for **educational and research purposes only**. AI-generated interpretations should be verified by licensed cardiologists. Always consult with qualified healthcare professionals for medical decisions.
+print(result)
+```
+
+### Using Command Line
+
+```bash
+python ecg_client_alt.py \
+    --url https://aksazgar-ecg-instruct-llama-3-2-11b-vision.hf.space \
+    --input ecg-images/example.jpg \
+    --text "Patient clinical notes" \
+    --lang Farsi
+```
+
+## API Endpoint
+
+- **Space URL**: `https://aksazgar-ecg-instruct-llama-3-2-11b-vision.hf.space`
+- **API Name**: `/predict`
+- **Inputs**:
+  - Image file (ECG)
+  - Text (clinical notes, optional)
+  - Language ("English" or "Farsi")
+- **Output**: Text (ECG interpretation report)
 
 ## Model
 
-This Space uses the [ECG-Instruct-Llama-3.2-11B-Vision](https://huggingface.co/convaiinnovations/ECG-Instruct-Llama-3.2-11B-Vision) model by ConvAI Innovations.
+Uses [ECG-Instruct-Llama-3.2-11B-Vision](https://huggingface.co/convaiinnovations/ECG-Instruct-Llama-3.2-11B-Vision) by ConvAI Innovations.
 
-## Technical Details
+## Disclaimer
 
-- **Model**: Llama 3.2 11B Vision (fine-tuned)
-- **Framework**: PyTorch + Transformers
-- **Interface**: Gradio
-- **Languages**: English, Farsi (Persian)
+⚠️ For educational and research purposes only. Not for clinical use.

app.py

@@ -8,7 +8,6 @@ import torch
 from transformers import MllamaForConditionalGeneration, AutoProcessor, TextStreamer
 from PIL import Image
 import gradio as gr
-import os
 
 # Model configuration
 MODEL_ID = "convaiinnovations/ECG-Instruct-Llama-3.2-11B-Vision"
@@ -169,62 +168,18 @@ def analyze_ecg_gradio(image, text_instruction="", language="Farsi"):
         return error_msg
 
 
-# Create Gradio interface
+# Create minimal Gradio interface for API backend
 demo = gr.Interface(
     fn=analyze_ecg_gradio,
     inputs=[
         gr.Image(type="filepath", label="ECG Image"),
-        gr.Textbox(
-            lines=2,
-            placeholder="Optional: Enter patient info or clinical notes (e.g., '55-year-old male with chest pain')",
-            label="Clinical Note"
-        ),
-        gr.Dropdown(
-            choices=["English", "Farsi"],
-            value="Farsi",
-            label="Output Language"
-        ),
+        gr.Textbox(lines=2, label="Clinical Note (Optional)"),
+        gr.Dropdown(choices=["English", "Farsi"], value="Farsi", label="Language"),
     ],
-    outputs=gr.Textbox(
-        label="AI ECG Report",
-        lines=20,
-        show_copy_button=True
-    ),
-    title="🫀 ECG AI7 - Intelligent ECG Interpretation",
-    description="""
-    Upload an ECG image to get an AI-powered interpretation.
-
-    **Features:**
-    - Advanced ECG analysis using Llama 3.2 11B Vision
-    - Support for English and Farsi (Persian) output
-    - Optional patient context for more personalized reports
-
-    **Note:** This is an AI assistant tool and should not replace professional medical diagnosis.
-    """,
-    examples=[
-        ["example_ecg.jpg", "55-year-old male with chest pain", "English"],
-        ["example_ecg.jpg", "بیمار 55 ساله مرد با درد قفسه سینه", "Farsi"],
-    ] if os.path.exists("example_ecg.jpg") else None,
-    article="""
-    ### About
-    This application uses a fine-tuned Llama 3.2 11B Vision model specifically trained for ECG interpretation.
-
-    ### How to Use
-    1. Upload an ECG image (12-lead ECG works best)
-    2. Optionally add patient information or clinical context
-    3. Select your preferred output language (English or Farsi)
-    4. Click Submit and wait for the AI analysis
-
-    ### Important Disclaimer
-    This tool is for educational and research purposes. Always consult with qualified healthcare
-    professionals for medical decisions. AI-generated interpretations should be verified by licensed
-    cardiologists.
-
-    ---
-    Model: [ECG-Instruct-Llama-3.2-11B-Vision](https://huggingface.co/convaiinnovations/ECG-Instruct-Llama-3.2-11B-Vision) by ConvAI Innovations
-    """,
-    theme=gr.themes.Soft(),
-    allow_flagging="never",
+    outputs=gr.Textbox(label="ECG Report", lines=15),
+    title="ECG AI7 Backend",
+    description="ECG interpretation API powered by Llama 3.2 11B Vision",
+    flagging_mode="never",
 )
 
 # Launch the app