Create app/main.py

app/main.py

@@ -0,0 +1,66 @@
+# entry point aka building FAST_API here
+
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+
+from app.predictor import classifier, guide_generator
+
+app = FastAPI(title="GitGud AI Service")
+
+
+# Data Model: Matches what NestJS (server-side[refer to visualization.services.ts]) sends
+class FileRequest(BaseModel):
+    fileName: str
+    content: str | None = None
+
+
+class GuideRequest(BaseModel):
+    repoName: str
+    filePaths: list[str]
+
+
+@app.get("/")
+def health_check():
+    """
+    Simple check to see if the server is alive and which GPU it's using.
+    """
+    return {
+        "status": "online",
+        "model": "microsoft/codebert-base",
+        "device": classifier.device,
+    }
+
+
+# first FAST_API with endpoint('/classify') called in [visualization.services.ts]
+# @param {*} file
+# @return {*} layerd based classified_info along with file-name
+@app.post("/classify")
+async def classify_file(request: FileRequest):
+    try:
+        # calling the predict function of our classifier to determine which layer it belongs
+        # returns { label, confidence, embedding }
+        result = classifier.predict(request.fileName, request.content)
+        return {
+            "fileName": request.fileName, 
+            "layer": result["label"],
+            "confidence": result["confidence"],
+            "embedding": result["embedding"]
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.post("/generate-guide")
+async def generate_guide(request: GuideRequest):
+    try:
+        markdown = guide_generator.generate_markdown(request.repoName, request.filePaths)
+        return {"markdown": markdown}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+if __name__ == "__main__":
+    import uvicorn
+
+    # Runs on localhost:8000
+    uvicorn.run(app, host="0.0.0.0", port=8000)