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TextGen/__init__.py

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+from fastapi import FastAPI
+
+app = FastAPI(title="Deploying FastAPI Apps on Huggingface")
+
+from TextGen import router

TextGen/router.py

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+
+from pydantic import BaseModel
+
+from .ConfigEnv import config
+from fastapi.middleware.cors import CORSMiddleware
+
+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:
+        prompt = PromptTemplate(template=prompt, input_variables=['Prompt'])
+
+        llm = Clarifai(
+            pat = config.CLARIFAI_PAT,
+            user_id = config.USER_ID,
+            app_id = config.APP_ID, 
+            model_id = config.MODEL_ID,
+            model_version_id=config.MODEL_VERSION_ID,
+        )
+
+        llmchain = LLMChain(
+            prompt=prompt,
+            llm=llm
+        )
+
+        llm_response = llmchain.run({"Prompt": prompt})
+        return Generate(text=llm_response)
+
+        
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+@app.get("/", tags=["Home"])
+def api_home():
+    return {'detail': 'Welcome to FastAPI TextGen Tutorial!'}
+
+@app.post("/api/generate", summary="Generate text from prompt", tags=["Generate"], response_model=Generate)
+def inference(input_prompt: str):
+    return generate_text(prompt=input_prompt)