Update app.py

app.py

@@ -1,63 +1,31 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    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]})
-
-    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
-
-        response += token
-        yield response
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_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)",
-        ),
-    ],
+import pandas as pd
+from PIL import Image
+
+def process_image(image):
+    # Dummy function to simulate image processing and generating answers
+    # Replace with your actual image processing and answer generation logic
+    suggested_answers = [
+        {"Answer": "Answer 1", "Link": "https://example.com/1"},
+        {"Answer": "Answer 2", "Link": "https://example.com/2"},
+        {"Answer": "Answer 3", "Link": "https://example.com/3"}
+    ]
+    
+    # Convert suggested answers to a DataFrame
+    df = pd.DataFrame(suggested_answers)
+    
+    # Convert DataFrame to HTML table with clickable links
+    table_html = df.to_html(escape=False, index=False, render_links=True)
+    
+    return table_html
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=process_image, 
+    inputs=gr.inputs.Image(type="pil"), 
+    outputs="html",
+    title="Oroz: Your Industry Miantain Assistant",
+    description="ou are Oroz , industry maintain smart assistant. You help users with their concern about the maintain and industrial safety, so based on user input of various machines changes, you could predict what could go wrong and get suggested answers in an organized table with web links."
 )
 
-
-if __name__ == "__main__":
-    demo.launch()
+iface.launch()