Use faq-canada-immigration model

app.py

@@ -1,7 +1,24 @@
 from fastapi import FastAPI
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
 
+tokenizer = AutoTokenizer.from_pretrained("mayanklad/faq-canada-immigration-tokenizer")
+model = AutoModelForCausalLM.from_pretrained("mayanklad/faq-canada-immigration")
+pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
 app = FastAPI()
 
-@app.get("/")
-def greet_json():
-    return {"Hello": "World!"}
+# 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 text-generation pipeline from `transformers`, generate text
+    from the given input text. The model used is `mayanklad/faq-canada-immigration`, which
+    can be found [here](<https://huggingface.co/mayanklad/faq-canada-immigration>).
+    """
+    # 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"]}

requirements.txt

@@ -1,2 +1,6 @@
-fastapi
-uvicorn[standard]
+fastapi==0.74.*
+requests==2.27.*
+uvicorn[standard]==0.17.*
+sentencepiece==0.1.*
+torch==1.11.*
+transformers==4.*