Update app.py

app.py

@@ -1,78 +1,4 @@
 
-"""
-from fastapi import FastAPI
-from fastapi.responses import RedirectResponse
-import gradio as gr
-
-from transformers import pipeline, ViltProcessor, ViltForQuestionAnswering, AutoTokenizer, AutoModelForCausalLM
-from PIL import Image
-import torch
-import fitz  # PyMuPDF for PDF
-
-
-
-app = FastAPI()
-
-
-
-# ========== Document QA Setup ==========
-doc_tokenizer = AutoTokenizer.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
-doc_model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
-
-def read_pdf(file):
-    doc = fitz.open(stream=file.read(), filetype="pdf")
-    text = ""
-    for page in doc:
-        text += page.get_text()
-    return text
-
-def answer_question_from_doc(file, question):
-    if file is None or not question.strip():
-        return "Please upload a document and ask a question."
-    text = read_pdf(file)
-    prompt = f"Context: {text}\nQuestion: {question}\nAnswer:"
-    inputs = doc_tokenizer(prompt, return_tensors="pt", truncation=True, max_length=2048)
-    with torch.no_grad():
-        outputs = doc_model.generate(**inputs, max_new_tokens=100)
-    answer = doc_tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return answer.split("Answer:")[-1].strip()
-
-# ========== Image QA Setup ==========
-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."
-    inputs = vqa_processor(image, question, return_tensors="pt")
-    with torch.no_grad():
-        outputs = vqa_model(**inputs)
-    predicted_id = outputs.logits.argmax(-1).item()
-    return vqa_model.config.id2label[predicted_id]
-
-# ========== Gradio Interfaces ==========
-doc_interface = gr.Interface(
-    fn=answer_question_from_doc,
-    inputs=[gr.File(label="Upload Document (PDF)"), gr.Textbox(label="Ask a Question")],
-    outputs="text",
-    title="Document Question Answering"
-)
-
-img_interface = gr.Interface(
-    fn=answer_question_from_image,
-    inputs=[gr.Image(label="Upload Image"), gr.Textbox(label="Ask a Question")],
-    outputs="text",
-    title="Image Question Answering"
-)
-
-# ========== Combine and Mount ==========
-demo = gr.TabbedInterface([doc_interface, img_interface], ["Document QA", "Image QA"])
-app = gr.mount_gradio_app(app, demo, path="/")
-
-@app.get("/")
-def root():
-    return RedirectResponse(url="/")
-"""
 import gradio as gr
 import fitz  # PyMuPDF for PDFs
 import easyocr  # OCR for images
@@ -136,10 +62,6 @@ def extract_text_from_xlsx(xlsx_file):
         return f"Error reading XLSX: {e}"
     return "\n".join(text)
 
-def extract_text_from_image(image_path):
-    """Extract text from an image using EasyOCR."""
-    result = reader.readtext(image_path, detail=0)
-    return " ".join(result)  # Return text as a single string
 
 # ---- MAIN PROCESSING FUNCTIONS ----
 def answer_question_from_doc(file, question):
@@ -167,26 +89,12 @@ def answer_question_from_doc(file, question):
     except Exception as e:
         return f"Error generating answer: {e}"
 
-def answer_question_from_image(image, question):
-    """Process an image, extract text, and answer a question."""
-    img_text = extract_text_from_image(image)
-    if not img_text.strip():
-        return """No readable text found in the image."""
-
     try:
         result = qa_model({"question": question, "context": img_text})
         return result["answer"]
     except Exception as e:
         return f"Error generating answer: {e}"
 
-# ---- GRADIO INTERFACES ----
-with gr.Blocks() as doc_interface:
-    gr.Markdown("## 📄 Document Question Answering")
-    file_input = gr.File(label="Upload DOCX, PPTX, XLSX, or PDF")
-    question_input = gr.Textbox(label="Ask a question")
-    answer_output = gr.Textbox(label="Answer")
-    file_submit = gr.Button("Get Answer")
-    file_submit.click(answer_question_from_doc, inputs=[file_input, question_input], outputs=answer_output)
 
 with gr.Blocks() as img_interface:
     gr.Markdown("## 🖼️ Image Question Answering")
@@ -197,7 +105,7 @@ with gr.Blocks() as img_interface:
     image_submit.click(answer_question_from_image, inputs=[image_input, img_question_input], outputs=img_answer_output)
 
 # ---- MOUNT GRADIO APP ----
-demo = gr.TabbedInterface([doc_interface, img_interface], ["Document QA", "Image QA"])
+demo = gr.TabbedInterface(img_interface, "Image QA")
 app = gr.mount_gradio_app(app, demo, path="/")
 
 @app.get("/")