Update app.py

app.py

@@ -2,34 +2,45 @@ import gradio as gr
 from huggingface_hub import InferenceClient
 from datasets import load_dataset
 
-# Load the PleIAs/common_corpus dataset with error handling
+# Safely load the PleIAs/common_corpus dataset
 def load_common_corpus():
     try:
-        return load_dataset("PleIAs/common_corpus")
+        print("Loading dataset...")
+        dataset = load_dataset("PleIAs/common_corpus")
+        print("Dataset loaded successfully!")
+        return dataset
     except Exception as e:
         print(f"Error loading dataset: {e}")
         return None
 
 common_corpus = load_common_corpus()
 
-# Retrieve an example from the dataset safely
+# Retrieve an example safely
 def get_example_from_corpus(dataset, index):
     if dataset and "train" in dataset:
         try:
             return dataset["train"][index]
         except IndexError:
-            print("Index out of range for dataset")
+            print("Index out of range for dataset.")
             return {"text": "No example available"}
     else:
-        return {"text": "Dataset not loaded correctly"}
+        print("Dataset not loaded correctly.")
+        return {"text": "Dataset not available."}
 
-# Initialize the Inference Client with error handling
-try:
-    client = InferenceClient("unsloth/Llama-3.2-1B-Instruct")
-except Exception as e:
-    print(f"Error initializing inference client: {e}")
-    client = None
+# Safely initialize the inference client
+def initialize_client():
+    try:
+        print("Initializing inference client...")
+        client = InferenceClient("unsloth/Llama-3.2-1B-Instruct")
+        print("Inference client initialized successfully!")
+        return client
+    except Exception as e:
+        print(f"Error initializing inference client: {e}")
+        return None
+
+client = initialize_client()
 
+# Chatbot response logic
 def respond(
     message,
     history: list[tuple[str, str]],
@@ -43,50 +54,55 @@ def respond(
 
     messages = [{"role": "system", "content": system_message}]
 
+    # Add historical interactions
     for val in history:
         if val[0]:
             messages.append({"role": "user", "content": val[0]})
         if val[1]:
             messages.append({"role": "assistant", "content": val[1]})
 
+    # Add user message
     messages.append({"role": "user", "content": message})
 
     try:
+        print("Sending request to model...")
         response = client.chat_completion(
             messages,
             max_tokens=max_tokens,
             temperature=temperature,
             top_p=top_p,
         ).choices[0].message.content
+        print("Response received successfully!")
+        return response
     except Exception as e:
         print(f"Error during inference: {e}")
-        response = "An error occurred while generating a response."
-    
-    return response
+        return "An error occurred while generating a response."
 
-# Example: Retrieve an entry from the dataset to demonstrate integration
+# Example: Retrieve an entry from the dataset
 example_data = get_example_from_corpus(common_corpus, 0)
-print("Example from PleIAs/common_corpus:", example_data)
+print("Example from dataset:", example_data)
 
-# Gradio interface with proper error handling
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot. Your name is Juninho.", 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)",
-        ),
-    ],
-)
-
-if __name__ == "__main__":
+# Gradio interface
+def launch_demo():
     try:
+        demo = gr.ChatInterface(
+            respond,
+            additional_inputs=[
+                gr.Textbox(value="You are a friendly Chatbot. Your name is Juninho.", 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)",
+                ),
+            ],
+        )
         demo.launch()
     except Exception as e:
-        print(f"Error launching Gradio app: {e}")
+        print(f"Error launching Gradio app: {e}")
+
+if __name__ == "__main__":
+    launch_demo()