Upload app.py

app.py

@@ -7,7 +7,6 @@ from pydantic import BaseModel
 from fastapi import FastAPI
 # Import the required library
 from transformers import pipeline
-# Load the tokenizer and model
 
 # Initialize the FastAPI app
 app = FastAPI(docs_url="/")
@@ -25,31 +24,29 @@ def greet_json():
 @app.post("/generatetext")
 def get_response(request: RequestModel):
     # Define the task and model
-	task = "text-generation"
-	model_name = "gpt2"
-
-	# Define the input text, maximum output length, and the number of return sequences
-	input_text = "he draw to the town "
-	max_output_length = 50
-	num_of_return_sequences = 1
-
-	# Initialize the text generation pipeline
-	text_generator = pipeline(
-		task,
-		model=model_name
-	)
-
-	# Generate text sequences
-	generated_texts = text_generator(
-		input_text,
-		max_length=max_output_length,
-		num_return_sequences=num_of_return_sequences
-	)
-	
-	# Print the generated text sequences
-	for i, text in enumerate(generated_texts):
-		print(f"Generated Text {1}: {text['generated_text']}")
-
-    return {"generated_text": text['generated_text']}
+    task = "text-generation"
+    model_name = "gpt2"
+
+    # Define the input text, maximum output length, and the number of return sequences
+    input_text = request.input
+    max_output_length = 50
+    num_of_return_sequences = 1
+
+    # Initialize the text generation pipeline
+    text_generator = pipeline(
+        task,
+        model=model_name
+    )
+
+    # Generate text sequences
+    generated_texts = text_generator(
+        input_text,
+        max_length=max_output_length,
+        num_return_sequences=num_of_return_sequences
+    )
+
+    # Extract and return the generated text
+    generated_text = generated_texts[0]['generated_text']
+    return {"generated_text": generated_text}
 
 # To run the FastAPI app, use the command: uvicorn <filename>:app --reload