Upload app.py

app.py

@@ -1,20 +1,28 @@
 from fastapi import FastAPI
-from fastapi.staticfiles import StaticFiles
-from fastapi.responses import FileResponse
-
 from transformers import pipeline
-
+ 
+# Create a new FastAPI app instance
 app = FastAPI()
-
-pipe_flan = pipeline("text2text-generation", model="google/flan-t5-small")
-
-@app.get("/infer_t5")
-def t5(input):
-    output = pipe_flan(input)
-    return {"output": output[0]["generated_text"]}
-
-app.mount("/", StaticFiles(directory="static", html=True), name="static")
-
-@app.get("/")
-def index() -> FileResponse:
-    return FileResponse(path="/app/static/index.html", media_type="text/html")
+ 
+# 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"
+@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"]}