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README.md

@@ -1,13 +1,61 @@
----
-title: Mental Health Sentiment
-emoji: 💬
-colorFrom: yellow
-colorTo: purple
-sdk: gradio
-sdk_version: 5.0.1
-app_file: app.py
-pinned: false
-license: mit
----
-
-An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).
+# Mental Health Sentiment Analysis API
+
+This Hugging Face Space provides an API endpoint for mental health-related sentiment analysis using the RoBERTa model fine-tuned on emotion detection.
+
+## Model Details
+- Base Model: RoBERTa
+- Fine-tuned Version: SamLowe/roberta-base-go_emotions
+- Task: Multi-label emotion classification
+- Input: Text
+- Output: Emotion probabilities and processed mental health context
+
+## API Usage
+
+### Using Python
+```python
+import requests
+
+API_URL = "https://your-space-name.hf.space/api/predict"
+response = requests.post(API_URL, json={"inputs": "your text here"})
+print(response.json())
+```
+
+### Using JavaScript/Node.js
+```javascript
+const response = await fetch("https://your-space-name.hf.space/api/predict", {
+    method: "POST",
+    headers: { "Content-Type": "application/json" },
+    body: JSON.stringify({ inputs: "your text here" })
+});
+const result = await response.json();
+```
+
+## Response Format
+```json
+{
+    "emotions": {
+        "joy": 0.8,
+        "sadness": 0.1,
+        "anger": 0.05,
+        // ... other emotions
+    },
+    "primaryEmotion": "joy",
+    "emotionalState": {
+        "state": "joy",
+        "intensity": "High",
+        "needsAttention": false,
+        "description": "Detected joy with 80% confidence"
+    },
+    "success": true,
+    "needsAttention": false
+}
+```
+
+## Error Handling
+If there's an error, the API will return:
+```json
+{
+    "error": "Error message here",
+    "success": false
+}
+```

app.py

@@ -1,64 +1,82 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+from transformers import pipeline
+import numpy as np
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+# Initialize the sentiment classifier
+classifier = pipeline(
+    "text-classification",
+    model="SamLowe/roberta-base-go_emotions",
+    top_k=None  # Return all emotions
+)
 
-        response += token
-        yield response
+def process_emotions(emotions):
+    """Convert raw emotions to our application's format"""
+    # Convert to dictionary format
+    emotion_dict = {item['label']: float(item['score']) for item in emotions}
+    
+    # Find primary emotion
+    primary_emotion = max(emotion_dict.items(), key=lambda x: x[1])
+    
+    # Calculate intensity
+    max_score = primary_emotion[1]
+    intensity = 'High' if max_score > 0.66 else 'Medium' if max_score > 0.33 else 'Low'
+    
+    # Check if needs attention
+    needs_attention = (
+        primary_emotion[0] in ['anger', 'anxiety', 'depression'] 
+        and max_score > 0.5
+    )
+    
+    return {
+        'emotions': emotion_dict,
+        'primaryEmotion': primary_emotion[0],
+        'emotionalState': {
+            'state': primary_emotion[0],
+            'intensity': intensity,
+            'needsAttention': needs_attention,
+            'description': f"Detected {primary_emotion[0]} with {round(max_score * 100)}% confidence"
+        },
+        'success': True,
+        'needsAttention': needs_attention
+    }
 
+def analyze_text(text):
+    """Analyze text and return processed emotions"""
+    if not text or not text.strip():
+        return {
+            'error': 'Please provide some text to analyze',
+            'success': False
+        }
+    
+    try:
+        # Get raw emotions from model
+        emotions = classifier(text)
+        
+        # Process emotions into our format
+        result = process_emotions(emotions[0])
+        
+        return result
+    except Exception as e:
+        return {
+            'error': str(e),
+            'success': False
+        }
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
+# Create Gradio interface
+demo = gr.Interface(
+    fn=analyze_text,
+    inputs=gr.Textbox(label="Enter text to analyze", lines=3),
+    outputs=gr.JSON(label="Sentiment Analysis Results"),
+    title="Mental Health Sentiment Analysis",
+    description="Analyzes text for emotions related to mental health using the RoBERTa model.",
+    examples=[
+        ["I'm feeling really anxious about my upcoming presentation"],
+        ["Today was a great day, I accomplished all my goals!"],
+        ["I've been feeling down and unmotivated lately"],
     ],
+    allow_flagging="never"
 )
 
-
+# Launch the app
 if __name__ == "__main__":
     demo.launch()

requirements.txt

@@ -1 +1,4 @@
-huggingface_hub==0.25.2
+transformers==4.36.2
+torch==2.1.2
+gradio==4.12.0
+numpy==1.26.3