Update main.py

main.py

@@ -1,27 +1,28 @@
-from model import model_pipeline
-
-from typing import Union
-
-from fastapi import FastAPI, UploadFile
-import io
-from PIL import Image
-import os
-os.environ['TRANSFORMERS_CACHE'] = '/blabla/cache/'
-
+from fastapi import FastAPI
+from transformers import pipeline
+ 
+# Create a new FastAPI app instance
 app = FastAPI()
-
-
-@app.get("/")
-def read_root():
-    return {"Hello": "World"}
-
-
-@app.post("/ask")
-def ask(text: str, image: UploadFile):
-    content = image.file.read()
-    
-    image = Image.open(io.BytesIO(content))
-    # image = Image.open(image.file)
-    
-    result = model_pipeline(text, image)
-    return {"answer": result}
+ 
+# 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"]}