Update app.py

app.py

@@ -1,4 +1,5 @@
-# app.py
+from fastapi import FastAPI, UploadFile, Form
+from fastapi.responses import JSONResponse, FileResponse
 import fitz  # PyMuPDF for PDFs
 import easyocr  # OCR for images
 import openpyxl  # XLSX processing
@@ -7,12 +8,15 @@ import docx  # DOCX processing
 from transformers import pipeline
 from gtts import gTTS
 import tempfile
+import os
+
 app = FastAPI()
-# Initialize AI Models
+
+# Load AI models
 qa_model = pipeline("question-answering", model="deepset/roberta-base-squad2")
-reader = easyocr.Reader(['en', 'fr'])  # OCR for English & French
+reader = easyocr.Reader(['en', 'fr'])
 
-# ---- TEXT EXTRACTION FUNCTIONS ----
+# Text Extraction
 def extract_text_from_pdf(pdf_file):
     text = []
     try:
@@ -25,13 +29,13 @@ def extract_text_from_pdf(pdf_file):
 
 def extract_text_from_docx(docx_file):
     doc = docx.Document(docx_file)
-    return "\n".join([p.text for p in doc.paragraphs if p.text.strip()])
+    return "\n".join(p.text for p in doc.paragraphs if p.text.strip())
 
 def extract_text_from_pptx(pptx_file):
     text = []
     try:
-        presentation = pptx.Presentation(pptx_file)
-        for slide in presentation.slides:
+        prs = pptx.Presentation(pptx_file)
+        for slide in prs.slides:
             for shape in slide.shapes:
                 if hasattr(shape, "text"):
                     text.append(shape.text)
@@ -51,18 +55,22 @@ def extract_text_from_xlsx(xlsx_file):
         return f"Error reading XLSX: {e}"
     return "\n".join(text)
 
-# ---- MAIN QA FUNCTION ----
+# Main QA logic
 def answer_question_from_doc(file, question):
-    ext = file.name.split(".")[-1].lower()
+    ext = file.filename.split(".")[-1].lower()
+    file_path = f"/tmp/{file.filename}"
+
+    with open(file_path, "wb") as f:
+        f.write(file.file.read())
 
     if ext == "pdf":
-        context = extract_text_from_pdf(file.name)
+        context = extract_text_from_pdf(file_path)
     elif ext == "docx":
-        context = extract_text_from_docx(file.name)
+        context = extract_text_from_docx(file_path)
     elif ext == "pptx":
-        context = extract_text_from_pptx(file.name)
+        context = extract_text_from_pptx(file_path)
     elif ext == "xlsx":
-        context = extract_text_from_xlsx(file.name)
+        context = extract_text_from_xlsx(file_path)
     else:
         return "Unsupported file format.", None
 
@@ -72,10 +80,25 @@ def answer_question_from_doc(file, question):
     try:
         result = qa_model({"question": question, "context": context})
         answer = result["answer"]
-        tts = gTTS(text=answer)
+        tts = gTTS(answer)
         with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp:
             tts.save(tmp.name)
             audio_path = tmp.name
         return answer, audio_path
     except Exception as e:
         return f"Error generating answer: {e}", None
+
+# API route for prediction
+@app.post("/predict")
+async def predict(file: UploadFile, question: str = Form(...)):
+    answer, audio_path = answer_question_from_doc(file, question)
+    if audio_path:
+        return JSONResponse(content={"answer": answer, "audio": f"/audio/{os.path.basename(audio_path)}"})
+    else:
+        return JSONResponse(content={"answer": answer})
+
+# Route to serve audio
+@app.get("/audio/{filename}")
+async def get_audio(filename: str):
+    file_path = os.path.join(tempfile.gettempdir(), filename)
+    return FileResponse(path=file_path, media_type="audio/mpeg")