Initial WorkWise backend merged main.py -> app.py

app copy 2.py

@@ -1,26 +0,0 @@
-import gradio as gr
-from fastapi import FastAPI
-import spaces
-
-app = FastAPI()
-
-@spaces.GPU
-def your_gpu_function(input_data):
-    # Your GPU computation
-    return result
-
-# FastAPI endpoint
-@app.get("/api/predict")
-async def predict(input: str):
-    result = your_gpu_function(input)
-    return {"result": result}
-
-# Gradio interface (required for ZeroGPU)
-with gr.Blocks() as demo:
-    gr.Interface(fn=your_gpu_function, inputs="text", outputs="text")
-
-# Mount FastAPI to Gradio
-app = gr.mount_gradio_app(app, demo, path="/")
-
-if __name__ == "__main__":
-    demo.launch()

app.py

@@ -1,86 +1,26 @@
 import gradio as gr
 from fastapi import FastAPI
-from fastapi.middleware.cors import CORSMiddleware
 import spaces
 
-# Import your existing routes and services
-from app.config import settings
-from app.routes import ingest_routes, ask_routes, metrics_routes
-from app.services.vector_store import vector_store
-from app.utils.logger import setup_logger
+app = FastAPI()
 
-logger = setup_logger(__name__)
-
-# Create FastAPI app
-app = FastAPI(
-    title="WorkWise Backend",
-    description="RAG-powered Jira analytics application",
-    version="1.0.0"
-)
-
-# CORS Middleware
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=settings.ALLOWED_ORIGINS,
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-# Include your existing routers
-app.include_router(ingest_routes.router, prefix="/api", tags=["Ingestion"])
-app.include_router(ask_routes.router, prefix="/api", tags=["Query"])
-app.include_router(metrics_routes.router, prefix="/api", tags=["Metrics"])
-
-@app.get("/")
-async def root():
-    return {
-        "status": "online",
-        "service": "WorkWise API",
-        "version": "1.0.0"
-    }
-
-@app.get("/health")
-async def health_check():
-    try:
-        info = vector_store.get_collection_info()
-        return {
-            "status": "healthy",
-            "index_path": settings.FAISS_INDEX_PATH,
-            "payloads_path": settings.FAISS_PAYLOADS_PATH,
-            "vectors_count": info.get("vectors_count", 0)
-        }
-    except Exception as e:
-        logger.error(f"Health check failed: {e}")
-        return {"status": "error", "message": str(e)}
-
-# Minimal Gradio UI (required for ZeroGPU)
 @spaces.GPU
-def query_jira(question: str):
-    """Simple wrapper for Gradio - calls your actual RAG logic"""
-    try:
-        # Import your actual query function here
-        from app.services.rag_service import process_query  # adjust import as needed
-        result = process_query(question)
-        return result
-    except Exception as e:
-        return f"Error: {str(e)}"
+def your_gpu_function(input_data):
+    # Your GPU computation
+    return result
+
+# FastAPI endpoint
+@app.get("/api/predict")
+async def predict(input: str):
+    result = your_gpu_function(input)
+    return {"result": result}
 
-# Create Gradio interface
-with gr.Blocks(title="WorkWise - Ask Jira") as demo:
-    gr.Markdown("# WorkWise - Jira RAG Assistant")
-    gr.Markdown("Ask questions about your Jira data")
-    
-    with gr.Row():
-        input_text = gr.Textbox(label="Your Question", placeholder="e.g., What are the top issues this sprint?")
-        output_text = gr.Textbox(label="Answer")
-    
-    submit_btn = gr.Button("Ask")
-    submit_btn.click(fn=query_jira, inputs=input_text, outputs=output_text)
+# Gradio interface (required for ZeroGPU)
+with gr.Blocks() as demo:
+    gr.Interface(fn=your_gpu_function, inputs="text", outputs="text")
 
-# Mount Gradio to FastAPI
+# Mount FastAPI to Gradio
 app = gr.mount_gradio_app(app, demo, path="/")
 
-# Launch
 if __name__ == "__main__":
     demo.launch()

app.py.merged

@@ -0,0 +1,86 @@
+import gradio as gr
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+import spaces
+
+# Import your existing routes and services
+from app.config import settings
+from app.routes import ingest_routes, ask_routes, metrics_routes
+from app.services.vector_store import vector_store
+from app.utils.logger import setup_logger
+
+logger = setup_logger(__name__)
+
+# Create FastAPI app
+app = FastAPI(
+    title="WorkWise Backend",
+    description="RAG-powered Jira analytics application",
+    version="1.0.0"
+)
+
+# CORS Middleware
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=settings.ALLOWED_ORIGINS,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Include your existing routers
+app.include_router(ingest_routes.router, prefix="/api", tags=["Ingestion"])
+app.include_router(ask_routes.router, prefix="/api", tags=["Query"])
+app.include_router(metrics_routes.router, prefix="/api", tags=["Metrics"])
+
+@app.get("/")
+async def root():
+    return {
+        "status": "online",
+        "service": "WorkWise API",
+        "version": "1.0.0"
+    }
+
+@app.get("/health")
+async def health_check():
+    try:
+        info = vector_store.get_collection_info()
+        return {
+            "status": "healthy",
+            "index_path": settings.FAISS_INDEX_PATH,
+            "payloads_path": settings.FAISS_PAYLOADS_PATH,
+            "vectors_count": info.get("vectors_count", 0)
+        }
+    except Exception as e:
+        logger.error(f"Health check failed: {e}")
+        return {"status": "error", "message": str(e)}
+
+# Minimal Gradio UI (required for ZeroGPU)
+@spaces.GPU
+def query_jira(question: str):
+    """Simple wrapper for Gradio - calls your actual RAG logic"""
+    try:
+        # Import your actual query function here
+        from app.services.rag_service import process_query  # adjust import as needed
+        result = process_query(question)
+        return result
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# Create Gradio interface
+with gr.Blocks(title="WorkWise - Ask Jira") as demo:
+    gr.Markdown("# WorkWise - Jira RAG Assistant")
+    gr.Markdown("Ask questions about your Jira data")
+    
+    with gr.Row():
+        input_text = gr.Textbox(label="Your Question", placeholder="e.g., What are the top issues this sprint?")
+        output_text = gr.Textbox(label="Answer")
+    
+    submit_btn = gr.Button("Ask")
+    submit_btn.click(fn=query_jira, inputs=input_text, outputs=output_text)
+
+# Mount Gradio to FastAPI
+app = gr.mount_gradio_app(app, demo, path="/")
+
+# Launch
+if __name__ == "__main__":
+    demo.launch()