app.py

from fastapi import FastAPI, File, UploadFile
import pdfplumber
import pytesseract
from PIL import Image
import easyocr
import docx
import openpyxl
from pptx import Presentation
from transformers import pipeline
import gradio as gr
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from fastapi.responses import RedirectResponse
import io

# ✅ Initialize FastAPI
app = FastAPI()

# ✅ Load AI Models
from transformers import pipeline

qa_pipeline = pipeline("text2text-generation",model="google/flan-t5-large",tokenizer="google/flan-t5-large",use_fast=True,device=0)
table_analyzer = pipeline("table-question-answering",model="google/tapas-large-finetuned-wtq",tokenizer="google/tapas-large-finetuned-wtq",use_fast=True,device=0)
code_generator = pipeline("text-generation",model="openai-community/gpt2-medium",tokenizer="openai-community/gpt2-medium",use_fast=True,device=0)
vqa_pipeline = pipeline("image-to-text",model="Salesforce/blip-vqa-base",device=0 )


# ✅ Function to truncate text to 450 tokens
def truncate_text(text, max_tokens=450):
    words = text.split()
    return " ".join(words[:max_tokens])  # ✅ Keeps only the first 450 words

# ✅ Functions for Document & Image QA
def extract_text_from_pdf(pdf_file):
    text = ""
    with pdfplumber.open(pdf_file) as pdf:
        for page in pdf.pages:
            text += page.extract_text() + "\n"
    return text.strip()

def extract_text_from_docx(docx_file):
    doc = docx.Document(docx_file)
    return "\n".join([para.text for para in doc.paragraphs])

def extract_text_from_pptx(pptx_file):
    ppt = Presentation(pptx_file)
    text = []
    for slide in ppt.slides:
        for shape in slide.shapes:
            if hasattr(shape, "text"):
                text.append(shape.text)
    return "\n".join(text)

def extract_text_from_excel(excel_file):
    wb = openpyxl.load_workbook(excel_file)
    text = []
    for sheet in wb.worksheets:
        for row in sheet.iter_rows(values_only=True):
            text.append(" ".join(map(str, row)))
    return "\n".join(text)

def extract_text_from_image(image_file):
    reader = easyocr.Reader(["en"])
    result = reader.readtext(image_file)
    return " ".join([res[1] for res in result])

def answer_question_from_document(file, question):
    file_ext = file.name.split(".")[-1].lower()
    
    if file_ext == "pdf":
        text = extract_text_from_pdf(file)
    elif file_ext == "docx":
        text = extract_text_from_docx(file)
    elif file_ext == "pptx":
        text = extract_text_from_pptx(file)
    elif file_ext == "xlsx":
        text = extract_text_from_excel(file)
    else:
        return "Unsupported file format!"

    if not text:
        return "No text extracted from the document."

    truncated_text = truncate_text(text)  # ✅ Prevents token limit error

    input_text = f"Question: {question} Context: {truncated_text}"  # ✅ Proper FLAN-T5 format
    response = qa_pipeline(input_text)

    return response[0]["generated_text"]  # ✅ Returns the correct output

def answer_question_from_image(image, question):
    image_text = extract_text_from_image(image)
    if not image_text:
        return "No text detected in the image."

    truncated_text = truncate_text(image_text)  # ✅ Prevents token limit error

    input_text = f"Question: {question} Context: {truncated_text}"
    response = qa_pipeline(input_text)

    return response[0]["generated_text"]

# ✅ Gradio UI for Document & Image QA
doc_interface = gr.Interface(
    fn=answer_question_from_document,
    inputs=[gr.File(label="Upload Document"), gr.Textbox(label="Ask a Question")],
    outputs="text",
    title="AI 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="AI Image Question Answering"
)


# ✅ Gradio UI for Data Visualization
viz_interface = gr.Interface(
    fn=generate_visualization,
    inputs=[
        gr.File(label="Upload Excel File"),
        gr.Radio(["Bar Chart", "Line Chart", "Scatter Plot", "Histogram"], label="Choose Visualization Type"),
        gr.Textbox(label="Enter Visualization Request")
    ],
    outputs=[gr.Code(label="Generated Python Code"), gr.Image(label="Visualization Output")],
    title="AI-Powered Data Visualization"
)

# ✅ Mount Gradio Interfaces
demo = gr.TabbedInterface([doc_interface, img_interface], ["Document QA", "Image QA"])
app = gr.mount_gradio_app(app, demo, path="/")

@app.get("/")
def home():
    return RedirectResponse(url="/")