Create app.py

app.py

@@ -0,0 +1,37 @@
+from fastapi import FastAPI
+from pydantic import BaseModel
+from transformers import pipeline, AutoTokenizer
+import uvicorn
+
+app = FastAPI()
+
+MODEL_NAME = "VincentMuriuki/legal-summarizer"
+summarizer = pipeline("summarization", model=MODEL_NAME)
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+
+class SummarizeInput(BaseModel):
+    text: str
+
+class ChunkInput(BaseModel):
+    text: str
+    max_tokens: int = 512 
+
+@app.post("/summarize")
+def summarize_text(data: SummarizeInput):
+    summary = summarizer(data.text, max_length=150, min_length=30, do_sample=False)
+    return {"summary": summary[0]["summary_text"]}
+
+@app.post("/chunk")
+def chunk_text(data: ChunkInput):
+    tokens = tokenizer.encode(data.text, truncation=False)
+    chunks = []
+
+    for i in range(0, len(tokens), data.max_tokens):
+        chunk_tokens = tokens[i:i + data.max_tokens]
+        chunk_text = tokenizer.decode(chunk_tokens, skip_special_tokens=True)
+        chunks.append(chunk_text.strip())
+
+    return {"chunks": chunks}
+
+if _name_ == "_main_":
+    uvicorn.run(app, host="0.0.0.0", port=7860)