Update app.py

app.py

@@ -65,46 +65,43 @@ 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 fastapi import FastAPI
+from fastapi.responses import RedirectResponse
+import tempfile
+import torch
 from PIL import Image
-from transformers import ViltProcessor, ViltForQuestionAnswering, AutoTokenizer, AutoModelForCausalLM
 from gtts import gTTS
-import torch
-import tempfile
 import gradio as gr
+from transformers import ViltProcessor, ViltForQuestionAnswering, AutoTokenizer, AutoModelForCausalLM
 
 app = FastAPI()
 
-# Load VQA Model
+# Load Models
 vqa_processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
 vqa_model = ViltForQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
 
-# Load GPT model to rewrite answers
-# Replacing Falcon-7B-Instruct with GPT-Neo-125M
 gpt_tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-125M")
 gpt_model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-neo-125M")
 
-def rewrite_answer(question, short_answer):
-    prompt = f"Question: {question}\nShort Answer: {short_answer}\nFull Sentence:"  # Simpler prompt for GPT-Neo
+# Rewrite answer to human-like sentence
+def rewrite_answer(question: str, short_answer: str) -> str:
+    prompt = f"Question: {question}\nAnswer: {short_answer}\nRewrite the answer into a complete sentence:"
     inputs = gpt_tokenizer(prompt, return_tensors="pt")
     with torch.no_grad():
         outputs = gpt_model.generate(
             **inputs,
-            max_new_tokens=50,
-            do_sample=True,
-            top_p=0.9,
-            temperature=0.8,
-            pad_token_id=gpt_tokenizer.eos_token_id
+            max_new_tokens=40,
+            do_sample=False,
+            pad_token_id=gpt_tokenizer.eos_token_id,
+            temperature=0.7,
         )
-    full = gpt_tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return full.split("Full Sentence:")[-1].strip()
+    result = gpt_tokenizer.decode(outputs[0], skip_special_tokens=True)
+    # Extract only the sentence after the "Rewrite..." line
+    return result.split("Rewrite the answer into a complete sentence:")[-1].strip()
 
-def answer_question_from_image(image, question):
+def answer_question_from_image(image: Image.Image, question: str):
     if image is None or not question.strip():
-        return "Please upload an image and ask a question.", None
+        return "❗ Please upload an image and type a question.", None
 
     inputs = vqa_processor(image, question, return_tensors="pt")
     with torch.no_grad():
@@ -112,38 +109,34 @@ def answer_question_from_image(image, question):
     predicted_id = outputs.logits.argmax(-1).item()
     short_answer = vqa_model.config.id2label[predicted_id]
 
-    # Rewrite short answer to full sentence
-    full_answer = rewrite_answer(question, short_answer)
+    full_sentence = rewrite_answer(question, short_answer)
 
     try:
-        tts = gTTS(text=full_answer)
+        tts = gTTS(text=full_sentence)
         with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp:
             tts.save(tmp.name)
             audio_path = tmp.name
     except Exception as e:
-        return f"Answer: {full_answer}\n\n⚠️ Audio generation error: {e}", None
+        return f"{full_sentence}\n\n⚠️ Audio generation error: {e}", None
 
-    return full_answer, audio_path
+    return full_sentence, 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,
+# Gradio Interface
+interface = gr.Interface(
+    fn=answer_question_from_image,
     inputs=[
-        gr.Image(type="pil", label="Upload Image"),
-        gr.Textbox(lines=2, placeholder="Ask a question about the image...", label="Question")
+        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")
+        gr.Textbox(label="💬 Answer"),
+        gr.Audio(label="🔊 Voice Output", type="filepath")
     ],
-    title="🧠 Image QA with Voice",
-    description="Upload an image and ask a question. You'll get a full-sentence spoken answer."
+    title="🧠 Image QA with Voice (VQA + GPT-Neo)",
+    description="Ask a question about an image and get a full sentence answer, including audio!"
 )
 
-app = gr.mount_gradio_app(app, gui, path="/")
+app = gr.mount_gradio_app(app, interface, path="/")
 
 @app.get("/")
 def home():