deepset/tinyroberta-squad2 added

main.py

@@ -2,12 +2,14 @@ from fastapi import FastAPI
 from pydantic import BaseModel
 from transformers import pipeline
 
-# Load model and resume
-qa_pipeline = pipeline("text2text-generation", model="google/flan-t5-small")
+# Load resume text
 with open("resume.txt", "r", encoding="utf-8") as f:
     resume_text = f.read()
 
-# FastAPI app check
+# Load extractive QA pipeline
+qa_pipeline = pipeline("question-answering", model="deepset/tinyroberta-squad2")
+
+# FastAPI setup
 app = FastAPI()
 
 class Question(BaseModel):
@@ -15,6 +17,8 @@ class Question(BaseModel):
 
 @app.post("/predict")
 async def predict(question: Question):
-    prompt = f"Resume:\n{resume_text}\n\nQuestion: {question.query}\nAnswer:"
-    result = qa_pipeline(prompt, max_length=100)[0]["generated_text"]
-    return {"answer": result.strip()}
+    result = qa_pipeline({
+        "question": question.query,
+        "context": resume_text
+    })
+    return {"answer": result["answer"]}