from fastapi import FastAPI,Request,File,UploadFile from fastapi.templating import Jinja2Templates from fastapi.staticfiles import StaticFiles from fastapi.responses import HTMLResponse,JSONResponse from fastapi.middleware.cors import CORSMiddleware import pandas as pd import re import io import base64 import matplotlib.pyplot as plt import torch import tensorflow as tf import fitz from docx import Document from pptx import Presentation import seaborn as sns import PIL.Image as Image import fitz from huggingface_hub import snapshot_download from transformers import ( AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM,pipeline ) # === 1. Load BLIP Image Captioning (TensorFlow) === try: print("[Info] installing Salesforce/blip-image-captioning-base ....") blip_dir = "./models/blip-base-tf" snapshot_download("Salesforce/blip-image-captioning-base", local_dir=blip_dir, local_dir_use_symlinks=False) interpreter = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base") print("[Info] Salesforce/blip-image-captioning-base is inatalled.") except Exception as exp: print("Can't load the model Salesforce/blip-image-captioning-base") print(f"[Error] {str(exp)}") # === 2. Load BART Summarization (PyTorch) === try: print("[Info] installing facebook/bart-large-cnn ....") bart_dir = "./models/bart-large-cnn" snapshot_download("facebook/bart-large-cnn", local_dir=bart_dir, local_dir_use_symlinks=False) bart_tokenizer = AutoTokenizer.from_pretrained(bart_dir) bart_model = AutoModelForSeq2SeqLM.from_pretrained(bart_dir) summarizer = pipeline("summarization", model=bart_model, tokenizer=bart_tokenizer) print("[Info] facebook/bart-large-cnn is installed") except Exception as exp: print("Can't load the model facebook/bart-large-cnn") print(f"[Error] {str(exp)}") # === 3. Load DeepSeek Coder (PyTorch with trust_remote_code) === try: print("[Info] installing deepseek-ai/deepseek-coder-1.3b-instruct ") deepseek_dir = "./models/deepseek-coder" snapshot_download("deepseek-ai/deepseek-coder-1.3b-instruct", local_dir=deepseek_dir, local_dir_use_symlinks=False) deepseek_tokenizer = AutoTokenizer.from_pretrained(deepseek_dir, trust_remote_code=True) deepseek_model = AutoModelForCausalLM.from_pretrained(deepseek_dir, trust_remote_code=True) generator = pipeline("text-generation", model=deepseek_model, tokenizer=deepseek_tokenizer) print("[Info] facebook/bart-large-cnn is installed") except Exception as exp: print("Can't load the model deepseek-ai/deepseek-coder-1.3b-instruct") print(f"[Error] {str(exp)}") app=FastAPI() app.add_middleware( CORSMiddleware, allow_origins=["*"], allow_credentials=True, allow_methods=["*"], allow_headers=["*"], ) MAX_SIZE= 1 * 1024 *1024 app.mount("/static",StaticFiles(directory='static'),'static') templates = Jinja2Templates(directory='templates') @app.get("/",response_class=HTMLResponse) def index(req:Request): return templates.TemplateResponse('index.html',{'request':req}) @app.get("/summarization",response_class=HTMLResponse) def index(req:Request): return templates.TemplateResponse('text-summarization.html',{'request':req}) @app.get("/datavisualisation",response_class=HTMLResponse) def index(req:Request): return templates.TemplateResponse('data-visualization.html',{'request':req}) @app.get("/imageinterpretation",response_class=HTMLResponse) def index(req:Request): return templates.TemplateResponse('image-interpretation.html',{'request':req}) @app.post("/interpret") def interpret(file_img:UploadFile=File(...)): extension = file_img.filename.split(".")[-1] Supported_extensions = ["png","jpg","jpeg"] if extension not in Supported_extensions: return JSONResponse(content={"error": "Unsupported file type"},status_code=400) image = Image.open(file_img.file) global interpreter try: caption = interpreter(image) except Exception as exp: return JSONResponse(content={"error": "Can't interpret the image "},status_code=400) return JSONResponse(content={"caption": caption[0]['generated_text']},status_code=200) @app.post("/summerize") def summerzation(file:UploadFile=File(...)): try: extension = file.filename.split(".")[-1] supported_ext=["pdf","xlxs","docx","ppt"] if extension not in supported_ext : return JSONResponse(content={"error": "Unsupported file type"},status_code=400) file_bytes = file.file.read() if len(file_bytes) > MAX_SIZE : return JSONResponse(content={"error": "too large file "},status_code=400) if extension == "pdf": text = get_text_from_PDF(file_bytes) elif extension == "docx": text = get_text_from_DOC(file_bytes) elif extension == "pptx": text = get_text_from_PPT(file_bytes) elif extension == "xlsx": text = get_text_from_EXCEL(file_bytes) if not text.strip(): return JSONResponse(content={'error':'File is emplty'},status_code=400) result="" global summarizer for i in range(0, len(text), 1024): try: summary = summarizer(text[i:i+1024], max_length=150, min_length=30, do_sample=False) result += summary[0]['summary_text'] except Exception as e: return JSONResponse(content={"error": f"Summarization failed: {str(e)}"},status_code=403) return JSONResponse(content={"summary": result},status_code=200) except Exception as exp: return JSONResponse(content={"error":"Internel Server Error:"+str(exp)} ,status_code=500) @app.post("/plot") def plot(user_need:str,file:UploadFile=File(...)): try: extension = file.filename.split(".")[-1] Supported_extensions = ["xlsx","xls"] if extension not in Supported_extensions: return JSONResponse(content={"error": "Unsupported file type"},status_code=400) df = pd.read_excel(io= file.file) message = f""" You are a helpful assistant that helps users write Python code. ## Requirements: -you will be given a task and you will write the code to solve the task. -you have a dataset called **df** contains the following information: df.columns:{df.columns.to_list()} df.dtypes:{df.dtypes.to_dict()} -you have to write the code to solve the task using the dataset df. -you can use pandas to manipulate the dataframe. -you can use matplotlib to plot the data. -you can use seaborn to plot the data. -don't use print or input statements in the code. -don't use any other libraries except pandas, matplotlib, seaborn. -don't use any other functions except the ones provided in the libraries. -don't write the code for the dataframe creation. -check if the columns has a nan values and raise exception if yes . -exclude plt.show() from the code. -you have to write the code in a markdown code block. -make sure that the type of the chart is compatible with the dtypes of the columns -use only the column specified in the task. -you have to extract the column names and the plot type from the prompt bellow and use them in the code. -if the user task is not clear or there is an error like the column names are not in the dataframe, raise an error. ##Prompt: {user_need}. """ global generator output = generator(message, max_length=1000) match = re.search(r'```python(.*?)```', output[0]["generated_text"], re.DOTALL) code ='' if not match: return JSONResponse(content={"error": "Can't generate the plot"},status_code=403) code = match.group(1).replace("plt.show()\n","") safe_globals={ "plt": plt, "sns": sns, "pd": pd, "df": df } try: exec(code,safe_globals) buf = io.BytesIO() plt.savefig(buf, format='png') buf.seek(0) base64_image = base64.b64encode(buf.getvalue()).decode('utf-8') return JSONResponse(content={"plot": f"data:image/png;base64,{base64_image}",'code':code},status_code=200) except Exception as e: print(e) return JSONResponse(content={"error": str(e) },status_code=500) except Exception as exp: return JSONResponse(content={"error":"Internel Server Error:"+str(exp)} ,status_code=500) def get_text_from_PDF(file_content): doc = fitz.open(stream=file_content, filetype="pdf") text = "" for page in doc: text += page.get_text() return text def get_text_from_PPT(file_content): prs = Presentation(io.BytesIO(file_content)) text = "" for slide in prs.slides: for shape in slide.shapes: if hasattr(shape, "text"): text += shape.text return text def get_text_from_DOC(file_content): doc = Document(io.BytesIO(file_content)) text = "" for paragraph in doc.paragraphs: text += paragraph.text return text def get_text_from_EXCEL(file): df = pd.read_excel(io=io.BytesIO(file)) text = df.to_string() return text