Update app.py

app.py

@@ -70,7 +70,7 @@ from fastapi.responses import RedirectResponse, FileResponse, JSONResponse
 import os
 import shutil
 from PIL import Image
-from transformers import ViltProcessor, ViltForQuestionAnswering
+from transformers import ViltProcessor, ViltForQuestionAnswering, pipeline
 from gtts import gTTS
 import torch
 import tempfile
@@ -82,25 +82,42 @@ app = FastAPI()
 vqa_processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
 vqa_model = ViltForQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
 
+# Load GPT model for rewriting short answers
+gpt_rewriter = pipeline("text-generation", model="EleutherAI/gpt-neo-1.3B")
+
+def rewrite_answer(question: str, short_answer: str):
+    prompt = f"Q: {question}\nA: {short_answer}\n\nRespond with a full sentence:"
+    try:
+        result = gpt_rewriter(prompt, max_length=50, do_sample=False)
+        full_sentence = result[0]['generated_text'].split("Respond with a full sentence:")[-1].strip()
+        return full_sentence
+    except Exception as e:
+        return short_answer  # fallback
+
 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()
     short_answer = vqa_model.config.id2label[predicted_id]
 
+    # Rewrite short answer using GPT
+    full_answer = rewrite_answer(question, short_answer)
+
+    # Generate TTS audio
     try:
-        tts = gTTS(text=short_answer)
+        tts = gTTS(text=full_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 f"Answer: {full_answer}\n\n⚠️ Audio generation error: {e}", None
 
-    return short_answer, audio_path
+    return full_answer, audio_path
 
 def process_image_question(image: Image.Image, question: str):
     answer, audio_path = answer_question_from_image(image, question)
@@ -117,7 +134,7 @@ gui = gr.Interface(
         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."
+    description="Upload an image and ask a question. You'll get a detailed text + spoken answer."
 )
 
 app = gr.mount_gradio_app(app, gui, path="/")