Upload api_app.py

api_app.py

@@ -0,0 +1,56 @@
+from fastapi import FastAPI, Request
+from fastapi.responses import HTMLResponse, JSONResponse
+from fastapi.staticfiles import StaticFiles
+from fastapi.templating import Jinja2Templates
+from transformers import AutoTokenizer, AutoModelForQuestionAnswering, pipeline
+import os
+
+app = FastAPI(title="QA Dashboard Pro")
+
+# Load your fine-tuned model
+MODEL_PATH = "MedhaCodes/qna_finetuned_model"
+qa_pipeline = pipeline(
+    "question-answering",
+    model=AutoModelForQuestionAnswering.from_pretrained(MODEL_PATH),
+    tokenizer=AutoTokenizer.from_pretrained(MODEL_PATH)
+)
+
+# Mount static files (CSS, JS)
+app.mount(
+    "/static",
+    StaticFiles(directory=os.path.join(os.path.dirname(__file__), "static")),
+    name="static"
+)
+
+# Load templates
+templates = Jinja2Templates(directory="templates")
+
+@app.get("/", response_class=HTMLResponse)
+async def home(request: Request):
+    return templates.TemplateResponse("index.html", {"request": request})
+
+@app.post("/predict")
+async def predict(request: Request):
+    data = await request.json()
+    context = data.get("context")
+    questions_text = data.get("question")
+
+    if not context or not questions_text:
+        return JSONResponse({"error": "Please provide both context and question"}, status_code=400)
+
+    # Handle multiple questions line-by-line
+    questions = [q.strip() for q in questions_text.strip().split("\n") if q.strip()]
+    answers = []
+
+    for i, q in enumerate(questions, start=1):
+        try:
+            result = qa_pipeline(question=q, context=context)
+            answers.append({
+                "question": q,
+                "answer": result["answer"],
+                "score": round(result["score"], 4)
+            })
+        except Exception as e:
+            answers.append({"question": q, "answer": f"Error: {e}", "score": 0})
+
+    return {"results": answers}