adding minimax model

app.py

@@ -2,50 +2,64 @@ import os
 import gradio as gr
 from huggingface_hub import InferenceClient
 
-# Authenticate with Hugging Face
-client = InferenceClient(
-    provider="sambanova",
-    api_key=os.environ["HF_TOKEN"],
-)
+# Clients for both providers
+llama_client = InferenceClient(provider="sambanova", api_key=os.environ["HF_TOKEN"])
+minimax_client = InferenceClient(provider="novita", api_key=os.environ["HF_TOKEN"])
 
-def llama4_chat(image_url, text_prompt):
-    if not text_prompt:
-        return "Please enter a text question or prompt."
+def chat_with_model(model_choice, prompt, image_url):
+    if not prompt:
+        return "Please enter a text prompt."
 
-    message_content = [{"type": "text", "text": text_prompt}]
+    try:
+        if model_choice == "LLaMA 4 (SambaNova)":
+            # Prepare message with optional image
+            content = [{"type": "text", "text": prompt}]
+            if image_url:
+                content.append({
+                    "type": "image_url",
+                    "image_url": {"url": image_url}
+                })
 
-    if image_url:
-        message_content.append({
-            "type": "image_url",
-            "image_url": {"url": image_url}
-        })
+            messages = [{"role": "user", "content": content}]
+            completion = llama_client.chat.completions.create(
+                model="meta-llama/Llama-4-Maverick-17B-128E-Instruct",
+                messages=messages
+            )
+            return completion.choices[0].message.content
 
-    messages = [{"role": "user", "content": message_content}]
+        elif model_choice == "MiniMax M1 (Novita)":
+            messages = [{"role": "user", "content": prompt}]
+            completion = minimax_client.chat.completions.create(
+                model="MiniMaxAI/MiniMax-M1-80k",
+                messages=messages
+            )
+            return completion.choices[0].message.content
+
+        else:
+            return "Unsupported model selected."
 
-    try:
-        completion = client.chat.completions.create(
-            model="meta-llama/Llama-4-Maverick-17B-128E-Instruct",
-            messages=messages
-        )
-        return completion.choices[0].message.content
     except Exception as e:
         return f"Error: {e}"
 
 # Gradio UI
 with gr.Blocks() as demo:
-    gr.Markdown("## 🦙 LLaMA 4 Chat (Text + Optional Image URL)")
-    gr.Markdown("Enter a text prompt. Optionally provide an image URL.")
-
-    with gr.Row():
-        text_prompt_input = gr.Textbox(label="Text Prompt / Question", placeholder="What is happening in the image?", lines=2)
-        image_url_input = gr.Textbox(label="Optional Image URL", placeholder="https://example.com/image.jpg")
+    gr.Markdown("## 🤖 Multimodel Chatbot: LLaMA 4 & MiniMax M1")
+    gr.Markdown("Choose a model, enter your prompt, and optionally add an image URL for LLaMA.")
 
+    model_dropdown = gr.Dropdown(
+        choices=["LLaMA 4 (SambaNova)", "MiniMax M1 (Novita)"],
+        value="LLaMA 4 (SambaNova)",
+        label="Select Model"
+    )
+    prompt_input = gr.Textbox(label="Text Prompt", placeholder="Ask something...", lines=2)
+    image_url_input = gr.Textbox(label="Optional Image URL (for LLaMA only)", placeholder="https://example.com/image.jpg")
+    
     submit_btn = gr.Button("Generate Response")
-    output_box = gr.Textbox(label="LLaMA 4 Response", lines=8)
+    output_box = gr.Textbox(label="Response", lines=8)
 
     submit_btn.click(
-        fn=llama4_chat,
-        inputs=[image_url_input, text_prompt_input],
+        fn=chat_with_model,
+        inputs=[model_dropdown, prompt_input, image_url_input],
         outputs=output_box
     )