added comparator app

app.py

@@ -1,7 +1,134 @@
 import gradio as gr
+import torch
+from transformers import AutoProcessor, AutoModelForVision2Seq
+from PIL import Image
+import time
+import fitz  # PyMuPDF for PDF support
+import io
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Define the models you want to compare
+MODELS = {
+    "Pixtral-12B": "mistralai/Pixtral-12B-2409",
+    "InternVL-2.5": "OpenGVLab/InternVL2_5-Chat",
+    "Aria-7B": "Aria-7B"  # Replace with actual model ID when public
+}
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+MODEL_CACHE = {}
+
+# Load models and processors (lazy loading for faster startup)
+def load_model(model_id):
+    if model_id not in MODEL_CACHE:
+        processor = AutoProcessor.from_pretrained(model_id)
+        model = AutoModelForVision2Seq.from_pretrained(model_id, device_map="auto", torch_dtype=torch.float16)
+        MODEL_CACHE[model_id] = (processor, model)
+    return MODEL_CACHE[model_id]
+
+
+def convert_pdf_to_image(pdf_bytes):
+    try:
+        pdf_doc = fitz.open(stream=pdf_bytes, filetype="pdf")
+        page = pdf_doc.load_page(0)  # first page only
+        pix = page.get_pixmap(dpi=150)
+        image_bytes = pix.tobytes("png")
+        image = Image.open(io.BytesIO(image_bytes))
+        return image
+    except Exception as e:
+        raise ValueError(f"Failed to convert PDF: {e}")
+
+
+def compare_models(file, prompt):
+    results = {}
+
+    if file is None or not prompt:
+        return {name: "Please provide both image/PDF and prompt." for name in MODELS}, None
+
+    # Determine input type (PDF or image)
+    if isinstance(file, str):
+        image = Image.open(file)
+    else:
+        file_bytes = file.read() if hasattr(file, 'read') else file
+        if file.name.endswith('.pdf'):
+            image = convert_pdf_to_image(file_bytes)
+        else:
+            image = Image.open(io.BytesIO(file_bytes))
+
+    image.thumbnail((512, 512))  # optimize
+
+    latency_data = {}
+
+    for name, model_id in MODELS.items():
+        try:
+            processor, model = load_model(model_id)
+            start = time.time()
+
+            inputs = processor(prompt, image, return_tensors="pt").to("cuda" if torch.cuda.is_available() else "cpu")
+            outputs = model.generate(**inputs, max_new_tokens=128)
+            text = processor.decode(outputs[0], skip_special_tokens=True)
+
+            elapsed = time.time() - start
+            results[name] = f"🧠 {text}\n\n⏱️ {elapsed:.2f}s"
+            latency_data[name] = elapsed
+
+        except Exception as e:
+            results[name] = f"❌ Error: {str(e)}"
+            latency_data[name] = 0
+
+    # Return results and latency chart data
+    return [results.get(name, "Model not loaded.") for name in MODELS], latency_data
+
+
+def plot_latency(latency_data):
+    if not latency_data:
+        return None
+    import matplotlib.pyplot as plt
+    plt.figure(figsize=(6, 3))
+    plt.bar(latency_data.keys(), latency_data.values())
+    plt.title("Model Inference Latency (s)")
+    plt.ylabel("Seconds")
+    plt.tight_layout()
+    return plt
+
+
+def build_ui():
+    with gr.Blocks(title="Multimodal Model Comparator") as demo:
+        gr.Markdown("""
+        # 🤖 Multimodal Model Comparator
+        Upload an **image or PDF document** and enter a question. 
+        The app compares outputs from **Pixtral-12B**, **InternVL-2.5**, and **Aria-7B** side-by-side.
+        
+        _Licenses: Apache 2.0 / MIT — safe for research and demo use._
+        """)
+
+        with gr.Row():
+            file_input = gr.File(label="Upload Image or PDF", file_types=[".png", ".jpg", ".jpeg", ".pdf"])
+            prompt_input = gr.Textbox(label="Prompt", placeholder="Ask something about the image or PDF...")
+
+        with gr.Row():
+            pixtral_out = gr.Textbox(label="Pixtral Output")
+            internvl_out = gr.Textbox(label="InternVL Output")
+            aria_out = gr.Textbox(label="Aria Output")
+
+        latency_plot = gr.Plot(label="Latency Comparison")
+
+        def process(file, prompt):
+            outputs, latency_data = compare_models(file, prompt)
+            plot = plot_latency(latency_data)
+            return outputs[0], outputs[1], outputs[2], plot
+
+        run_button = gr.Button("Run Comparison")
+        run_button.click(fn=process, inputs=[file_input, prompt_input], outputs=[pixtral_out, internvl_out, aria_out, latency_plot])
+
+        gr.Examples(
+            examples=[
+                ["sample_image.jpg", "What is shown in this picture?"],
+                ["chart_example.png", "Describe the trend in this chart."],
+            ],
+            inputs=[file_input, prompt_input]
+        )
+
+    return demo
+
+
+if __name__ == "__main__":
+    demo = build_ui()
+    demo.launch()

requirements.txt

@@ -0,0 +1,8 @@
+transformers>=4.45.0
+torch>=2.2.0
+Pillow
+gradio>=4.39.0
+accelerate
+sentencepiece
+pdf2image
+pymupdf