Update app.py

app.py

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+from transformers import pipeline
+from fastapi import FastAPI, Request
+import uvicorn
+
+from uagents import Agent, Context, Bureau
+
+# Load emotion detection model
+emotion_model = pipeline("text-classification", model="bhadresh-savani/distilbert-base-uncased-emotion")
+
+def analyze_text_metrics(text):
+    results = emotion_model(text)
+    text_lower = text.lower()
+
+    suicide_keywords = ["kill myself", "suicidal", "die", "ending it", "pills", "overdose", "no way out"]
+    psychosis_keywords = ["voices", "hallucinate", "not real", "they’re watching me", "i’m not me"]
+
+    metrics = {
+        "self_harm": 0,
+        "homicidal": 0,
+        "distress": 0,
+        "psychosis": 0
+    }
+
+    for result in results:
+        label = result['label']
+        score = result['score']
+        if label == 'sadness':
+            metrics["self_harm"] += score
+            metrics["distress"] += score * 0.6
+        elif label in ['anger', 'fear']:
+            metrics["homicidal"] += score
+            metrics["distress"] += score * 0.5
+        elif label == 'joy':
+            metrics["psychosis"] += score * 0.3
+        elif label == 'surprise':
+            metrics["psychosis"] += score * 0.5
+
+    if any(word in text_lower for word in suicide_keywords):
+        metrics["self_harm"] = max(metrics["self_harm"], 0.8)
+    if any(word in text_lower for word in psychosis_keywords):
+        metrics["psychosis"] = max(metrics["psychosis"], 0.8)
+
+    for k in metrics:
+        metrics[k] = round(min(metrics[k] * 100, 100), 2)
+
+    return metrics
+
+# Define uAgent
+agent = Agent(name="sentiment_agent")
+
+@agent.on_message()
+async def handle_message(ctx: Context, sender: str, msg: str):
+    metrics = analyze_text_metrics(msg)
+    await ctx.send(sender, str(metrics))
+
+# FastAPI wrapper
+app = FastAPI()
+
+@app.post("/")
+async def analyze_text(request: Request):
+    data = await request.json()
+    text = data.get("text", "")
+    result = analyze_text_metrics(text)
+    return result
+
+# Run both FastAPI and agent
+if __name__ == "__main__":
+    bureau = Bureau()
+    bureau.add(agent)
+    bureau.run_in_thread()
+    uvicorn.run(app, host="0.0.0.0", port=8000)