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new.py

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+import os
+from fastapi import FastAPI
+from pydantic import BaseModel
+from huggingface_hub import InferenceClient
+
+# Initialize FastAPI app
+app = FastAPI()
+
+# Get the API key securely from the environment variables
+API_KEY = os.getenv("HF_API_KEY")  # The key name matches the one you set in Secrets
+MODEL_NAME = "meta-llama/Llama-3.1-8B-Instruct"
+
+# Initialize Hugging Face Inference Client
+client = InferenceClient(api_key=API_KEY)
+
+# Define input data model
+class ChatInput(BaseModel):
+    role: str
+    content: str
+
+@app.post("/chat")
+async def chat(input_data: ChatInput):
+    try:
+        # Prepare input messages for the model
+        messages = [
+            {
+                "role": input_data.role,
+                "content": input_data.content
+            }
+        ]
+
+        # Get completion from the Hugging Face model
+        completion = client.chat.completions.create(
+            model=MODEL_NAME,
+            messages=messages,
+            max_tokens=500
+        )
+        # Extract and return the response
+        return {
+            "response": completion.choices[0].message
+        }
+    except Exception as e:
+        return {"error": str(e)}

requirements.txt

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+# fastapi
+uvicorn
+huggingface-hub