Update app.py

app.py

@@ -8,6 +8,11 @@ from PIL import Image
 import io
 from docx import Document
 import fitz  # PyMuPDF
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import uuid
+from transformers import MarianMTModel, MarianTokenizer
 
 # Configure logging
 logging.basicConfig(level=logging.INFO)
@@ -24,6 +29,13 @@ multimodal_pipeline = pipeline("image-to-text", model="Salesforce/blip-image-cap
 # Load a text-based model for summarization and text question answering
 text_pipeline = pipeline("text2text-generation", model="t5-small")
 
+# Load a translation model (initialized dynamically based on target language)
+translation_models = {
+    "fr": "Helsinki-NLP/opus-mt-en-fr",
+    "es": "Helsinki-NLP/opus-mt-en-es",
+    "de": "Helsinki-NLP/opus-mt-en-de"
+}
+
 @app.get("/")
 def read_root():
     # Redirect to the static HTML file
@@ -117,6 +129,84 @@ async def visual_question_answering(file: UploadFile = File(...), question: str
         logger.error(f"Error during visual question answering: {e}")
         raise HTTPException(status_code=500, detail=str(e))
 
+@app.post("/visualize")
+async def visualize_data(
+    file: UploadFile = File(...),
+    request: str = Form(...)
+):
+    logger.info(f"Received Excel file for visualization: {file.filename}")
+    logger.info(f"Received visualization request: {request}")
+
+    try:
+        # Read the Excel file
+        df = pd.read_excel(io.BytesIO(await file.read()))
+
+        # Generate visualization code based on the request
+        if "bar" in request.lower():
+            code = f"""
+import matplotlib.pyplot as plt
+plt.bar(df['{df.columns[0]}'], df['{df.columns[1]}'])
+plt.xlabel('{df.columns[0]}')
+plt.ylabel('{df.columns[1]}')
+plt.title('Bar Chart')
+plt.show()
+"""
+        elif "line" in request.lower():
+            code = f"""
+import matplotlib.pyplot as plt
+plt.plot(df['{df.columns[0]}'], df['{df.columns[1]}'])
+plt.xlabel('{df.columns[0]}')
+plt.ylabel('{df.columns[1]}')
+plt.title('Line Chart')
+plt.show()
+"""
+        else:
+            code = f"""
+import seaborn as sns
+sns.pairplot(df)
+plt.show()
+"""
+
+        # Save the generated code to a file (optional)
+        code_filename = f"visualization_{uuid.uuid4()}.py"
+        with open(code_filename, "w") as f:
+            f.write(code)
+
+        return {"code": code, "filename": code_filename}
+    except Exception as e:
+        logger.error(f"Error during visualization code generation: {e}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.post("/translate")
+async def translate_document(
+    file: UploadFile = File(...),
+    target_language: str = Form(...)
+):
+    logger.info(f"Received document for translation: {file.filename}")
+    logger.info(f"Target language: {target_language}")
+
+    try:
+        # Extract text from the document
+        text = await extract_text_from_file(file)
+
+        # Load a translation model based on the target language
+        if target_language in translation_models:
+            model_name = translation_models[target_language]
+        else:
+            model_name = "Helsinki-NLP/opus-mt-en-de"  # Default to German
+
+        tokenizer = MarianTokenizer.from_pretrained(model_name)
+        model = MarianMTModel.from_pretrained(model_name)
+
+        # Translate the text
+        translated = model.generate(**tokenizer(text, return_tensors="pt", truncation=True))
+        translated_text = tokenizer.decode(translated[0], skip_special_tokens=True)
+
+        return {"translated_text": translated_text}
+    except Exception as e:
+        logger.error(f"Error during document translation: {e}")
+        raise HTTPException(status_code=500, detail=str(e))
+
 # Helper function to extract text from files
 async def extract_text_from_file(file: UploadFile):
     try: