Rename _app.py to app.py

_app.py

@@ -1,102 +0,0 @@
-import gradio as gr
-from transformers import pipeline
-
-# Define all pipelines
-def load_pipelines():
-    pipelines = {
-        "GPT-2 Original": pipeline("text-generation", model="gpt2"),
-        "GPT-2 Medium": pipeline("text-generation", model="gpt2-medium"),
-        "DistilGPT-2": pipeline("text-generation", model="distilgpt2"),
-        "German GPT-2": pipeline("text-generation", model="german-nlp-group/german-gpt2"),
-        "German Wechsel GPT-2": pipeline("text-generation", model="benjamin/gpt2-wechsel-german"),
-        "T5 Base": pipeline("text-generation", model="t5-base"),
-        "T5 Large": pipeline("text-generation", model="t5-large"),
-        "Text Classification": pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2-english"),
-        "Sentiment Analysis": pipeline("sentiment-analysis", model="nlptown/bert-base-multilingual-uncased-sentiment")
-    }
-    return pipelines
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    model_name,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    # Load pipelines
-    pipelines = load_pipelines()
-    pipe = pipelines.get(model_name)
-    
-    if not pipe:
-        return "Error: Model not found."
-
-    # For text generation models
-    if model_name in ["GPT-2 Original", "GPT-2 Medium", "DistilGPT-2", 
-                      "German GPT-2", "German Wechsel GPT-2", 
-                      "T5 Base", "T5 Large"]:
-        # Prepare full prompt
-        full_history = ' '.join([f"{msg[0]} {msg[1] or ''}" for msg in history]) if history else ''
-        full_prompt = f"{system_message}\n{full_history}\nUser: {message}\nAssistant:"
-
-        try:
-            response = pipe(
-                full_prompt, 
-                max_length=len(full_prompt) + max_tokens,
-                temperature=temperature,
-                top_p=top_p,
-                num_return_sequences=1
-            )[0]['generated_text']
-            
-            # Extract just the new assistant response
-            assistant_response = response[len(full_prompt):].strip()
-            return assistant_response
-        except Exception as e:
-            return f"Generation error: {e}"
-    
-    # For classification and sentiment models
-    elif model_name == "Text Classification":
-        try:
-            result = pipe(message)[0]
-            return f"Classification: {result['label']} (Confidence: {result['score']:.2f})"
-        except Exception as e:
-            return f"Classification error: {e}"
-    
-    elif model_name == "Sentiment Analysis":
-        try:
-            result = pipe(message)[0]
-            return f"Sentiment: {result['label']} (Confidence: {result['score']:.2f})"
-        except Exception as e:
-            return f"Sentiment analysis error: {e}"
-
-def create_chat_interface():
-    """Create Gradio ChatInterface with model selection."""
-    demo = gr.ChatInterface(
-        respond,
-        additional_inputs=[
-            gr.Textbox(value="You are a helpful assistant.", label="System message"),
-            gr.Dropdown(
-                ["GPT-2 Original", "GPT-2 Medium", "DistilGPT-2", 
-                 "German GPT-2", "German Wechsel GPT-2", 
-                 "T5 Base", "T5 Large", 
-                 "Text Classification", "Sentiment Analysis"], 
-                value="GPT-2 Original", 
-                label="Select Model"
-            ),
-            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)",
-            ),
-        ]
-    )
-    return demo
-
-if __name__ == "__main__":
-    chat_interface = create_chat_interface()
-    chat_interface.launch(share=True)

app.py

@@ -0,0 +1,141 @@
+import gradio as gr
+from transformers import pipeline
+import os
+from huggingface_hub import login
+
+# Hugging Face login function
+def hf_login(token):
+    if token:
+        try:
+            login(token)
+            return "Successfully logged in to Hugging Face Hub"
+        except Exception as e:
+            return f"Login error: {str(e)}"
+    return "No token provided"
+
+# Define all pipelines with lazy loading
+def get_pipeline(model_name):
+    """Lazy load pipeline only when needed"""
+    try:
+        if model_name == "GPT-2 Original":
+            return pipeline("text-generation", model="gpt2")
+        elif model_name == "GPT-2 Medium":
+            return pipeline("text-generation", model="gpt2-medium")
+        elif model_name == "DistilGPT-2":
+            return pipeline("text-generation", model="distilgpt2")
+        elif model_name == "German GPT-2":
+            return pipeline("text-generation", model="german-nlp-group/german-gpt2")
+        elif model_name == "German Wechsel GPT-2":
+            return pipeline("text-generation", model="benjamin/gpt2-wechsel-german")
+        elif model_name == "T5 Base":
+            return pipeline("text2text-generation", model="t5-base")
+        elif model_name == "T5 Large":
+            return pipeline("text2text-generation", model="t5-large")
+        elif model_name == "Text Classification":
+            return pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2-english")
+        elif model_name == "Sentiment Analysis":
+            return pipeline("sentiment-analysis", model="nlptown/bert-base-multilingual-uncased-sentiment")
+        else:
+            raise ValueError(f"Unknown model: {model_name}")
+    except Exception as e:
+        raise Exception(f"Error loading model {model_name}: {str(e)}")
+
+def respond(
+    message,
+    history: list[tuple[str, str]],
+    system_message,
+    model_name,
+    max_tokens,
+    temperature,
+    top_p,
+):
+    try:
+        # Get the appropriate pipeline
+        pipe = get_pipeline(model_name)
+        
+        # For text generation models
+        if model_name in ["GPT-2 Original", "GPT-2 Medium", "DistilGPT-2", 
+                          "German GPT-2", "German Wechsel GPT-2"]:
+            # Prepare full prompt
+            full_history = ' '.join([f"User: {msg[0]}\nAssistant: {msg[1] or ''}" for msg in history]) if history else ''
+            full_prompt = f"{system_message}\n{full_history}\nUser: {message}\nAssistant:"
+            
+            response = pipe(
+                full_prompt, 
+                max_length=len(full_prompt.split()) + max_tokens,
+                temperature=temperature,
+                top_p=top_p,
+                num_return_sequences=1
+            )[0]['generated_text']
+            
+            # Extract just the new assistant response
+            assistant_response = response[len(full_prompt):].strip()
+            return assistant_response
+        
+        # For T5 models
+        elif model_name in ["T5 Base", "T5 Large"]:
+            # T5 doesn't handle chat history the same way, so simplify
+            input_text = f"{message}"
+            
+            response = pipe(
+                input_text,
+                max_length=max_tokens,
+                temperature=temperature,
+                top_p=top_p,
+                num_return_sequences=1
+            )[0]['generated_text']
+            
+            return response
+        
+        # For classification and sentiment models
+        elif model_name == "Text Classification":
+            result = pipe(message)[0]
+            return f"Classification: {result['label']} (Confidence: {result['score']:.2f})"
+        
+        elif model_name == "Sentiment Analysis":
+            result = pipe(message)[0]
+            return f"Sentiment: {result['label']} (Confidence: {result['score']:.2f})"
+            
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+def create_interface():
+    with gr.Blocks(title="Hugging Face Models Demo") as demo:
+        gr.Markdown("# Hugging Face Models Chat Interface")
+        
+        with gr.Accordion("Hugging Face Login", open=False):
+            with gr.Row():
+                hf_token = gr.Textbox(label="Enter Hugging Face Token", type="password")
+                login_btn = gr.Button("Login")
+                login_output = gr.Textbox(label="Login Status")
+            login_btn.click(hf_login, inputs=[hf_token], outputs=[login_output])
+        
+        chat_interface = gr.ChatInterface(
+            respond,
+            additional_inputs=[
+                gr.Textbox(value="You are a helpful assistant.", label="System message"),
+                gr.Dropdown(
+                    ["GPT-2 Original", "GPT-2 Medium", "DistilGPT-2", 
+                     "German GPT-2", "German Wechsel GPT-2", 
+                     "T5 Base", "T5 Large", 
+                     "Text Classification", "Sentiment Analysis"], 
+                    value="GPT-2 Original", 
+                    label="Select Model"
+                ),
+                gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
+                gr.Slider(minimum=0.1, maximum=2.0, 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)",
+                ),
+            ]
+        )
+    
+    return demo
+
+if __name__ == "__main__":
+    interface = create_interface()
+    interface.launch(share=True)