Update app.py

app.py

@@ -1,20 +1,23 @@
 import gradio as gr
 import pandas as pd
+from transformers import pipeline
+
+# Initialize the Hugging Face pipeline
+model_name = "gpt-4"  # Replace with the desired model
+generator = pipeline("text-generation", model=model_name)
 
 def generate_solutions(query):
-    # Dummy function to simulate solution generation
-    # Replace with your actual logic for generating solutions
-    solutions = [
-        {"Solution": "Check the power supply", "Link": "https://example.com/power-supply"},
-        {"Solution": "Inspect the wiring", "Link": "https://example.com/wiring-inspection"},
-        {"Solution": "Update the firmware", "Link": "https://example.com/firmware-update"}
-    ]
+    # Use the language model to generate solutions
+    response = generator(query, max_length=100, num_return_sequences=3)
+    
+    # Extract the generated texts
+    solutions = [{"Solution": r['generated_text']} for r in response]
     
     # Convert solutions to a DataFrame
     df = pd.DataFrame(solutions)
     
-    # Convert DataFrame to HTML table with clickable links
-    table_html = df.to_html(escape=False, index=False, render_links=True)
+    # Convert DataFrame to HTML table
+    table_html = df.to_html(escape=False, index=False)
     
     return table_html
 
@@ -24,7 +27,8 @@ iface = gr.Interface(
     inputs=gr.Textbox(lines=2, placeholder="Describe the problem with the machine..."), 
     outputs=gr.HTML(),
     title="Oroz: Your Industry Maintenance Assistant",
-    description="Describe the problem with your machine precisely, and get an organized table of suggested solutions with web links."
+    description="Describe the problem with your machine, and get an organized table of suggested solutions."
 )
 
 iface.launch(share=True)
+