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Dockerfile

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+FROM python:3.10.9
+
+COPY . .
+
+WORKDIR /
+
+RUN pip install --no-cache-dir --upgrade -r /requirements.txt
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

@@ -1,11 +1,13 @@
 ---
-title: EvBackend
-emoji: 😻
-colorFrom: green
-colorTo: pink
+title: TextGen
+emoji: 📝
+colorFrom: yellow
+colorTo: yellow
 sdk: docker
+app_file: app.py
 pinned: false
-license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# FastAPI-Deployment
+
+Simple Text Generation app using Cohere generate a model to demonstrate deploying FastAPI Applications on Huggingface Spaces via Docker and Github Actions

TextGen/ConfigEnv.py

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+"""Config class for handling env variables.
+"""
+from functools import lru_cache
+from pydantic import BaseSettings
+
+class Settings(BaseSettings):
+    APP_ID: str
+    USER_ID: str
+    MODEL_ID: str
+    CLARIFAI_PAT: str
+    MODEL_VERSION_ID: str
+    
+    class Config:
+        env_file = '.env'
+
+@lru_cache()
+def get_settings():
+    return Settings()
+config = get_settings()

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)

app.py

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+from TextGen import app

requirements.txt

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+fastapi==0.99.1
+uvicorn
+requests
+pydantic==1.10.12
+langchain
+clarifai
+Pillow