Update app.py

app.py

@@ -1,7 +1,59 @@
+import gradio as gr
+import pandas as pd
+import torch
+import matplotlib.pyplot as plt
+import seaborn as sns
 from fastapi import FastAPI
+from transformers import pipeline
+
+# ✅ Load Lightweight Hugging Face Models
+code_generator = pipeline("text-generation", model="Salesforce/codegen-350M-multi")
+table_analyzer = pipeline("table-question-answering", model="google/tapas-small-finetuned-wtq")
 
 app = FastAPI()
 
+def generate_visualization(excel_file, user_request):
+    # Load Excel file into DataFrame
+    df = pd.read_excel(excel_file)
+
+    # Convert DataFrame to JSON (for TAPAS input)
+    table_data = df.to_dict(orient="records")
+
+    # Analyze table with TAPAS
+    table_answer = table_analyzer(question=user_request, table=table_data)
+
+    # Generate Python visualization code with CodeGen
+    prompt = f"Generate Python code using Matplotlib and Seaborn to visualize: {user_request} from this data:\n{df.head()}"
+    code_response = code_generator(prompt, max_new_tokens=100, do_sample=True)[0]["generated_text"]
+
+    # Extract relevant Python code (basic cleaning)
+    code_lines = code_response.split("\n")
+    python_code = "\n".join([line for line in code_lines if "import" in line or "plt." in line or "sns." in line])
+
+    # Execute generated code safely
+    exec_globals = {"df": df, "plt": plt, "sns": sns}
+    exec(python_code, exec_globals)
+
+    # Save and return plot
+    plt.savefig("output.png")
+    plt.close()
+
+    return "output.png", python_code, table_answer
+
+# ✅ Gradio Interface
+gradio_ui = gr.Interface(
+    fn=generate_visualization,
+    inputs=[gr.File(label="Upload Excel File"), gr.Textbox(label="Enter Visualization Request")],
+    outputs=[gr.Image(label="Generated Visualization"), gr.Code(label="Generated Python Code"), gr.Textbox(label="Table Insights")],
+    title="Lightweight AI-Powered Data Visualization"
+)
+
+# ✅ Mount Gradio to FastAPI
 @app.get("/")
-def greet_json():
-    return {"Hello": "World!"}
+def home():
+    return {"message": "Welcome to AI-Powered Data Visualization"}
+
+app = gr.mount_gradio_app(app, gradio_ui, path="/visualization")
+
+
+