Update app.py

app.py

@@ -1,20 +1,41 @@
-from fastapi import FastAPI
 from pydantic import BaseModel
+
+from .ConfigEnv import config
+from fastapi.middleware.cors import CORSMiddleware
 from happytransformer import HappyTextToText, TTSettings
+from langchain.llms import Clarifai
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
+from TextGen import app
+
+class Generate(BaseModel):
+    text:str
+
+def generate_text(prompt: str):
+    if prompt == "":
+        return {"detail": "Please provide a prompt."}
+    else:
 
-app = FastAPI()
 
-happy_tt = HappyTextToText("T5", "vennify/t5-base-grammar-correction")
+        happy_tt = HappyTextToText("T5", "vennify/t5-base-grammar-correction")
+        
+        args = TTSettings(num_beams=5, min_length=1,  max_length=100000)
+        
+        result = happy_tt.generate_text(f"grammar: {prompt}", args=args)
+        return result.text
 
-class InputText(BaseModel):
-    txt: str
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
 
-@app.post("/correct_grammar/")
-def correct_grammar(input_text: InputText):
-    args = TTSettings(num_beams=5, min_length=1, max_length=100000)
-    corrected_text = happy_tt.generate_text(f"grammar: {input_text.txt}", args=args)
-    return {"corrected_text": corrected_text.text}
+@app.get("/", tags=["Home"])
+def api_home():
+    return {'detail': 'Welcome to FastAPI TextGen Tutorial!'}
 
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=8000)
+@app.post("/api/generate",  tags=["Generate"], response_model=Generate)
+def inference(input_prompt: str):
+    return generate_text(prompt=input_prompt)