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app.py

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+
+from fastapi import FastAPI
+from langserve import add_routes
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+from langchain_huggingface import HuggingFacePipeline
+from fastapi.middleware.cors import CORSMiddleware
+import torch
+
+app = FastAPI(
+    title="Phi-3 Code Explainer API",
+    description="Explains Python code using the Phi-3 Mini model and LangChain.",
+    version="1.0"
+)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+model_id = "microsoft/phi-3-mini-4k-instruct"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id, torch_dtype=torch.float16, device_map="auto"
+)
+
+generator = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    max_new_tokens=256,
+    return_full_text=False,
+    do_sample=False
+)
+
+llm = HuggingFacePipeline(pipeline=generator)
+
+prompt = ChatPromptTemplate.from_template(
+    "### Instruction:\nExplain this Python code step-by-step:\nUse code with caution\n{code}\n\n### Explanation:"
+)
+
+parser = StrOutputParser()
+chain = prompt | llm | parser
+
+add_routes(app, chain, path="/explain")