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| from fastapi import FastAPI, File, UploadFile, Form | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| import io | |
| from fastapi.responses import StreamingResponse | |
| from transformers import pipeline, AutoModelForSeq2SeqLM, AutoTokenizer | |
| app = FastAPI() | |
| # ✅ Load Hugging Face models dynamically from the internet | |
| #code_generator =pipeline("text-generation", model="codellama/CodeLlama-7b-Python") | |
| table_analyzer = pipeline("table-question-answering", model="google/tapas-large") | |
| user_input_processor = pipeline("text-generation", model="tiiuae/falcon-7b-instruct") # comprend lsngusge naturel | |
| #image_captioner = pipeline("image-to-text", model="Salesforce/blip2-opt-2.7b") | |
| # ✅ Load T5 Model (ensure correct architecture) | |
| model_name = "google/t5-small" # Change to the correct T5 model if needed | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForSeq2SeqLM.from_pretrained(model_name) | |
| async def visualize( | |
| file: UploadFile = File(...), | |
| description: str = Form(None), | |
| chart_type: str = Form(None), | |
| x_column: str = Form(None), | |
| y_column: str = Form(None) | |
| ): | |
| print("🔵 Début du traitement...") | |
| contents = await file.read() | |
| excel_data = io.BytesIO(contents) | |
| print("✅ Fichier reçu et converti en mémoire.") | |
| try: | |
| df = pd.read_excel(excel_data) | |
| print("✅ Lecture du fichier Excel réussie.") | |
| except Exception as e: | |
| print(f"❌ Erreur lors de la lecture du fichier Excel : {e}") | |
| return {"error": "Impossible de lire le fichier Excel."} | |
| df.columns = df.columns.str.strip().str.lower() | |
| print("📌 Colonnes après nettoyage :", df.columns.tolist()) | |
| # If no specific chart details are given, infer from description | |
| if description: | |
| print("📝 Analyse de la description utilisateur...") | |
| response = user_input_processor(description, max_length=50) | |
| inferred_data = response[0]['generated_text'] | |
| print("🔍 Inference AI:", inferred_data) | |
| # TODO: Extract structured data from response (chart_type, x_column, y_column) | |
| # Ensure x_column and y_column exist | |
| if x_column.lower() not in df.columns or y_column.lower() not in df.columns: | |
| print(f"❌ Erreur: '{x_column}' ou '{y_column}' non trouvées.") | |
| return {"error": f"Les colonnes '{x_column}' ou '{y_column}' n'existent pas."} | |
| print("✅ Colonnes valides, préparation du graphique...") | |
| plt.figure(figsize=(20, 12)) | |
| if chart_type == "bar": | |
| df.plot(kind="bar", x=x_column.lower(), y=y_column.lower()) | |
| elif chart_type == "line": | |
| df.plot(kind="line", x=x_column.lower(), y=y_column.lower()) | |
| elif chart_type == "scatter": | |
| df.plot(kind="scatter", x=x_column.lower(), y=y_column.lower()) | |
| elif chart_type == "pie": | |
| df.set_index(x_column.lower())[y_column.lower()].plot(kind="pie", autopct="%1.1f%%") | |
| elif chart_type == "histogram": | |
| df[y_column.lower()].plot(kind="hist", bins=10) | |
| else: | |
| return {"error": "Invalid chart type"} | |
| img_stream = io.BytesIO() | |
| plt.savefig(img_stream, format="png") | |
| img_stream.seek(0) | |
| plt.close() | |
| # Generate image caption | |
| #image_description = image_captioner(img_stream) | |
| #print("🖼️ Description du graphique:", image_description) | |
| return StreamingResponse(img_stream, media_type="image/png") | |