Update app.py

app.py

@@ -1,28 +1,32 @@
 from fastapi import FastAPI
+from fastapi.responses import JSONResponse
 from transformers import pipeline
- 
-# Create a new FastAPI app instance
+from fastapi.requests import Request
+
 app = FastAPI()
- 
+
 # Initialize the text generation pipeline
-# This function will be able to generate text
-# given an input.
-pipe = pipeline("text2text-generation", 
-model="google/flan-t5-small")
- 
-# Define a function to handle the GET request at `/generate`
-# The generate() function is defined as a FastAPI route that takes a 
-# string parameter called text. The function generates text based on the # input using the pipeline() object, and returns a JSON response 
-# containing the generated text under the key "output"
+pipe = pipeline("text2text-generation", model="google/flan-t5-small")
+
+@app.middleware("http")
+async def log_requests(request: Request, call_next):
+    print(f"Incoming request: {request.method} {request.url}")
+    response = await call_next(request)
+    return response
+
+@app.get("/")
+def read_root():
+    return {
+        "message": "Welcome to the FastAPI text generation API! Use the /generate endpoint with a 'text' query parameter."
+    }
+
 @app.get("/generate")
 def generate(text: str):
-    """
-    Using the text2text-generation pipeline from `transformers`, generate text
-    from the given input text. The model used is `google/flan-t5-small`, which
-    can be found [here](<https://huggingface.co/google/flan-t5-small>).
-    """
-    # Use the pipeline to generate text from the given input text
-    output = pipe(text)
-     
-    # Return the generated text in a JSON response
-    return {"output": output[0]["generated_text"]}
+    try:
+        output = pipe(text)
+        return {"output": output[0]["generated_text"]}
+    except Exception as e:
+        return JSONResponse(
+            status_code=500,
+            content={"error": f"An error occurred: {str(e)}"}
+        )