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"""from fastapi import FastAPI, UploadFile, Form
from fastapi.responses import RedirectResponse, FileResponse, JSONResponse
import os
import shutil
from PIL import Image
from transformers import ViltProcessor, ViltForQuestionAnswering
from gtts import gTTS
import torch
import tempfile
import gradio as gr
app = FastAPI()
# Load VQA Model
vqa_processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
vqa_model = ViltForQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
def answer_question_from_image(image, question):
if image is None or not question.strip():
return "Please upload an image and ask a question.", None
# Process with model
inputs = vqa_processor(image, question, return_tensors="pt")
with torch.no_grad():
outputs = vqa_model(**inputs)
predicted_id = outputs.logits.argmax(-1).item()
answer = vqa_model.config.id2label[predicted_id]
# Generate TTS audio
try:
tts = gTTS(text=answer)
with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp:
tts.save(tmp.name)
audio_path = tmp.name
except Exception as e:
return f"Answer: {answer}\n\n⚠️ Audio generation error: {e}", None
return answer, audio_path
def process_image_question(image: Image.Image, question: str):
answer, audio_path = answer_question_from_image(image, question)
return answer, audio_path
gui = gr.Interface(
fn=process_image_question,
inputs=[
gr.Image(type="pil", label="Upload Image"),
gr.Textbox(lines=2, placeholder="Ask a question about the image...", label="Question")
],
outputs=[
gr.Textbox(label="Answer", lines=5),
gr.Audio(label="Answer (Audio)", type="filepath")
],
title="🧠 Image QA with Voice",
description="Upload an image and ask a question. You'll get a text + spoken answer."
)
app = gr.mount_gradio_app(app, gui, path="/")
@app.get("/")
def home():
return RedirectResponse(url="/") """
from fastapi import FastAPI, UploadFile, Form
from fastapi.responses import RedirectResponse, FileResponse, JSONResponse
import os
import shutil
from PIL import Image
from transformers import ViltProcessor, ViltForQuestionAnswering
from gtts import gTTS
import torch
import tempfile
import gradio as gr
app = FastAPI()
# Load VQA Model
vqa_processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
vqa_model = ViltForQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
def answer_question_from_image(image, question):
if image is None or not question.strip():
return "Please upload an image and ask a question.", None
inputs = vqa_processor(image, question, return_tensors="pt")
with torch.no_grad():
outputs = vqa_model(**inputs)
predicted_id = outputs.logits.argmax(-1).item()
short_answer = vqa_model.config.id2label[predicted_id]
try:
tts = gTTS(text=short_answer)
with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp:
tts.save(tmp.name)
audio_path = tmp.name
except Exception as e:
return f"Answer: {short_answer}\n\n⚠️ Audio generation error: {e}", None
return short_answer, audio_path
def process_image_question(image: Image.Image, question: str):
answer, audio_path = answer_question_from_image(image, question)
return answer, audio_path
gui = gr.Interface(
fn=process_image_question,
inputs=[
gr.Image(type="pil", label="Upload Image"),
gr.Textbox(lines=2, placeholder="Ask a question about the image...", label="Question")
],
outputs=[
gr.Textbox(label="Answer", lines=5),
gr.Audio(label="Answer (Audio)", type="filepath")
],
title="🧠 Image QA with Voice",
description="Upload an image and ask a question. You'll get an answer spoken out loud."
)
app = gr.mount_gradio_app(app, gui, path="/")
@app.get("/")
def home():
return RedirectResponse(url="/") |