Update app.py

app.py

@@ -1,35 +1,71 @@
 import gradio as gr
+from huggingface_hub import InferenceClient
 from gradio_client import Client, handle_file
 
-# Gradio Client
-client = Client("vikhyatk/moondream2")  # Replace with your Gradio app URL or identifier
+# Initialize the HuggingFace InferenceClient or another client if needed
+client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
-
-def process_image_upload(image):
-    """
-    This function processes the uploaded image and sends it to the Gradio app.
-    """
-    # Convert the uploaded image to a format compatible with the Gradio Client
-    image_file = handle_file(image)  # Handle the uploaded image as a file
+# Function to process image and return a description using another API (e.g., Moondream2)
+def describe_image(image):
+    # Call the external API to get a description of the image
     result = client.predict(
-        img=image_file,
+        img=handle_file(image),
         prompt="Describe this image.",
         api_name="/answer_question"
     )
     return result
 
+def respond(
+    message,
+    history: list[tuple[str, str]],
+    system_message,
+    max_tokens,
+    temperature,
+    top_p,
+    image
+):
+    messages = [{"role": "system", "content": system_message}]
+
+    for val in history:
+        if val[0]:
+            messages.append({"role": "user", "content": val[0]})
+        if val[1]:
+            messages.append({"role": "assistant", "content": val[1]})
 
-# Gradio UI
-with gr.Blocks() as demo:
-    gr.Markdown("## Image Description with Gradio Client")
+    # Process the image if provided
+    if image:
+        description = describe_image(image)
+        return description
     
-    with gr.Row():
-        image_input = gr.Image(type="file", label="Upload an Image")
-        output_text = gr.Textbox(label="Image Description", interactive=False)
+    # If no image, proceed with the usual chat flow
+    messages.append({"role": "user", "content": message})
+
+    response = ""
+
+    for message in client.chat_completion(
+        messages,
+        max_tokens=max_tokens,
+        stream=True,
+        temperature=temperature,
+        top_p=top_p,
+    ):
+        token = message.choices[0].delta.content
 
-    submit_button = gr.Button("Get Description")
+        response += token
+        yield response
 
-    submit_button.click(fn=process_image_upload, inputs=image_input, outputs=output_text)
+# Set up the Gradio interface with image input and output for description
+demo = gr.Interface(
+    fn=respond,
+    inputs=[
+        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
+        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
+        gr.Image(type="file", label="Upload Image")  # Allow image upload
+    ],
+    outputs="text",  # Display the description as text output
+)
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()