Update app.py

app.py

@@ -1,4 +1,5 @@
-import gradio as gr
+from fastapi import FastAPI
+from pydantic import BaseModel
 import google.generativeai as genai
 import os
 from dotenv import load_dotenv
@@ -6,7 +7,7 @@ from dotenv import load_dotenv
 # Load environment variables
 load_dotenv()
 
-# Set API Key securely
+# Set up Gemini API Key
 API_KEY = os.getenv("GEMINI_API_KEY")
 if not API_KEY:
     raise ValueError("❌ Missing GEMINI_API_KEY. Please set it in the .env file.")
@@ -16,16 +17,29 @@ genai.configure(api_key=API_KEY)
 # Load Gemini Model
 model = genai.GenerativeModel("gemini-2.0-flash")
 
-# Function for AI-powered injury risk and recovery analysis
-def injury_recovery(injury_type, age, past_injuries, sport, mobility, pressure, weight_bearing):
-    severity_level = max(mobility, pressure, weight_bearing)
+# Initialize FastAPI app
+app = FastAPI(title="APTS Injury Recovery AI API")
+
+# Define input structure
+class InjuryInput(BaseModel):
+    injury_type: str
+    age: int
+    past_injuries: str
+    sport: str
+    mobility: int  # 1-3 scale
+    pressure: int  # 1-3 scale
+    weight_bearing: int  # 1-3 scale
+
+# Function to analyze injury and suggest recovery
+def generate_recovery_plan(data: InjuryInput):
+    severity_level = max(data.mobility, data.pressure, data.weight_bearing)
 
     recovery_prompt = f"""
     Generate a **personalized recovery plan** for an athlete with the following details:
-    - **Injury Type**: {injury_type}
-    - **Age**: {age} years
-    - **Sport**: {sport}
-    - **Past Injuries**: {past_injuries}
+    - **Injury Type**: {data.injury_type}
+    - **Age**: {data.age} years
+    - **Sport**: {data.sport}
+    - **Past Injuries**: {data.past_injuries}
     - **Injury Severity Level**: {severity_level} (1 = Low, 2 = Medium, 3 = High)
 
     Provide:
@@ -36,23 +50,17 @@ def injury_recovery(injury_type, age, past_injuries, sport, mobility, pressure,
     """
 
     recovery_response = model.generate_content(recovery_prompt)
-    return recovery_response.text
-
-# Gradio UI
-iface = gr.Interface(
-    fn=injury_recovery,
-    inputs=[
-        gr.Textbox(label="Injury Type"),
-        gr.Number(label="Age"),
-        gr.Textbox(label="Past Injuries"),
-        gr.Textbox(label="Sport"),
-        gr.Slider(1, 3, step=1, label="Mobility Test (1 = Low, 3 = High)"),
-        gr.Slider(1, 3, step=1, label="Pressure Test (1 = Low, 3 = High)"),
-        gr.Slider(1, 3, step=1, label="Weight-Bearing Test (1 = Low, 3 = High)"),
-    ],
-    outputs=gr.Textbox(label="AI-Powered Recovery Plan"),
-    title="AI-Powered Injury Recovery Assistant",
-    description="Enter injury details and severity test results to get a personalized recovery plan.",
-)
-
-iface.launch()
+
+    return {
+        "injury_type": data.injury_type,
+        "age": data.age,
+        "sport": data.sport,
+        "severity_level": severity_level,
+        "recovery_plan": recovery_response.text
+    }
+
+# API Endpoint
+@app.post("/analyze-injury")
+async def analyze_injury(data: InjuryInput):
+    response = generate_recovery_plan(data)
+    return response