Update app.py

app.py

@@ -1,19 +1,70 @@
-from transformers import pipeline
+# from transformers import pipeline
+# import gradio as gr
+
+
+# chatbot = pipeline("text-generation", model="unsloth/DeepSeek-R1-GGUF", trust_remote_code=True)
+
+# def chat_with_bot(user_input):
+#     # Generate a response from the chatbot model
+#     response = chatbot(user_input)
+#     return response[0]['generated_text'] 
+
+# interface = gr.Interface(
+#     fn=chat_with_bot,  # Function to call for processing the input
+#     inputs=gr.Textbox(label="Enter your message"),  # User input (text)
+#     outputs=gr.Textbox(label="Chatbot Response", lines=10),  # Model output (text)
+#     title="Chat with DeepSeek",  # Optional: Add a title to your interface
+#     description="Chat with an AI model powered by DeepSeek!"  # Optional: Add a description
+# )
+# interface.launch()
+
+
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
 import gradio as gr
 
+# Load the model and tokenizer from Hugging Face
+model_name = "unsloth/Llama-3.2-3B-Instruct"  # Replace with your model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
 
-chatbot = pipeline("text-generation", model="unsloth/DeepSeek-R1-GGUF", trust_remote_code=True)
+# Function to generate text
+def generate_text(input_text, max_length=100, temperature=0.7, top_p=0.9):
+    # Tokenize the input text
+    inputs = tokenizer(input_text, return_tensors="pt")
+    
+    # Generate text using the model
+    outputs = model.generate(
+        inputs["input_ids"],
+        max_length=max_length,
+        temperature=temperature,
+        top_p=top_p,
+        num_return_sequences=1,
+        no_repeat_ngram_size=2,
+    )
+    
+    # Decode the generated text
+    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return generated_text
 
-def chat_with_bot(user_input):
-    # Generate a response from the chatbot model
-    response = chatbot(user_input)
-    return response[0]['generated_text'] 
+# Gradio Interface
+def gradio_interface(input_text, max_length, temperature, top_p):
+    generated_text = generate_text(input_text, max_length, temperature, top_p)
+    return generated_text
 
-interface = gr.Interface(
-    fn=chat_with_bot,  # Function to call for processing the input
-    inputs=gr.Textbox(label="Enter your message"),  # User input (text)
-    outputs=gr.Textbox(label="Chatbot Response", lines=10),  # Model output (text)
-    title="Chat with DeepSeek",  # Optional: Add a title to your interface
-    description="Chat with an AI model powered by DeepSeek!"  # Optional: Add a description
+# Create the Gradio app
+app = gr.Interface(
+    fn=gradio_interface,  # Function to call
+    inputs=[
+        gr.Textbox(lines=2, placeholder="Enter your prompt here...", label="Input Prompt"),
+        gr.Slider(minimum=10, maximum=500, value=100, step=10, label="Max Length"),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.7, step=0.1, label="Temperature"),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.9, step=0.1, label="Top-p (Nucleus Sampling)"),
+    ],
+    outputs=gr.Textbox(lines=10, label="Generated Text"),
+    title="Text Generation with Hugging Face Transformers",
+    description="Generate text using a Hugging Face model.",
 )
-interface.launch()
+
+# Launch the app
+app.launch()