2nd commit

Visualisation/app.py

@@ -0,0 +1,83 @@
+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="bigcode/starcoder",times_out=1000)
+user_input_processor = pipeline("text-generation", model="tiiuae/falcon-7b-instruct")  # comprend lsngusge naturel
+table_analyzer = pipeline("table-question-answering", model="google/tapas-large")
+#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)
+
+@app.post("/visualize/")
+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")

Visualisation/dockerfile

@@ -8,7 +8,7 @@ WORKDIR /app
 COPY requirements.txt .
 
 # Install the dependencies
-RUN pip install --no-cache-dir -r requirements.txt
+RUN pip install --no-cache-dir -r  project/requirements.txt
 
 # Copy the entire project into the container
 COPY . .
@@ -17,4 +17,4 @@ COPY . .
 EXPOSE 8000
 
 # Command to start FastAPI with Uvicorn
-CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000", "--reload"]
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000", "--reload"]

Visualisation/requirements.txt

@@ -1,8 +0,0 @@
-fastapi
-uvicorn
-pandas
-openpyxl
-matplotlib
-seaborn
-pydantic
-python-multipart

main.py

@@ -0,0 +1,7 @@
+import subprocess
+
+# Start Data Visualization Service
+subprocess.Popen(["uvicorn", "data-visualisation.app:app", "--host", "0.0.0.0", "--port", "8001"])
+
+# Start Document Translation Service
+subprocess.Popen(["uvicorn", "document-translation.app:app", "--host", "0.0.0.0", "--port", "8002"])

translation/dockerfile

@@ -1,20 +1,23 @@
-# Utilisation de l'image Python comme base
+# Use Python 3.9 as base image
 FROM python:3.9
 
-# Définition du répertoire de travail
+# Set working directory inside the container
 WORKDIR /app
 
-# Copier les fichiers du projet dans l'image Docker
+# Copy project files
 COPY . .
 
-# Installer les dépendances
-RUN pip install --no-cache-dir -r requirements.txt
+# Copy the torch_wheels directory from location
+COPY ../torch_wheels /tmp/torch_wheels
 
-# Exposer le port utilisé par FastAPI
-EXPOSE 7860
+# Install torch wheels
+RUN pip install --no-cache-dir /tmp/torch_wheels/*
 
-# Lancer l'application avec Uvicorn
-CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]
+# Install other dependencies
+RUN pip install --no-cache-dir -r project/requirements.txt
 
+# Expose port for FastAPI
+EXPOSE 7860
 
-ENV C:\Program Files\Docker\Docker
+# Start the application
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]