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Dockerfile

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+# Read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
+# you will also find guides on how best to write your Dockerfile
+
+FROM python:3.9
+
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+WORKDIR /app
+
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+COPY --chown=user . /app
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+import os
+from dotenv import load_dotenv
+from langchain_groq import ChatGroq
+from langchain_core.prompts import ChatPromptTemplate
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+
+
+load_dotenv()
+
+
+groq_api_key = os.getenv('GROQ_API_KEY')
+
+
+llm_model = ChatGroq(
+    groq_api_key=groq_api_key,
+    model_name="Llama3-8b-8192"
+)
+
+
+app = FastAPI()
+
+
+origins = ["*"]
+
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+class textFromFrontendModel(BaseModel):
+    textFromNextJSFrontend: str
+
+
+
+@app.get('/')
+def welcome():
+    return {
+        'success': True,
+        'message': 'server of "fitbites is up and running successfully '
+    }
+
+
+@app.post('/predict')
+async def predict(incomingTextFromFrontend: textFromFrontendModel):
+
+    prompt_text = incomingTextFromFrontend.textFromNextJSFrontend
+
+    prompt_template = ChatPromptTemplate.from_template(
+        """
+        {text}
+        """
+    )
+
+    chain = prompt_template | llm_model
+    
+    response_from_model = chain.invoke({"text": prompt_text})
+
+    return {
+        'success': True,
+        'response_from_model': response_from_model
+    }
+

requirements.txt

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+fastapi==0.109.2
+langchain_core==0.2.18
+langchain_groq==0.1.6
+pydantic==2.7.2
+python-dotenv==1.0.1