Update app.py

app.py

@@ -1,63 +1,122 @@
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+
+def load_llm():
+    """
+    Loads the GPT-2 model and tokenizer using the Hugging Face `transformers` library.
+    """
+    try:
+        # Load pre-trained model and tokenizer from Hugging Face
+        print("Downloading or loading the GPT-2 model and tokenizer...")
+        model_name = 'gpt2'
+        model = GPT2LMHeadModel.from_pretrained(model_name)
+        tokenizer = GPT2Tokenizer.from_pretrained(model_name)
+        print("Model and tokenizer successfully loaded!")
+        return model, tokenizer
+    
+    except Exception as e:
+        print(f"An error occurred while loading the model: {e}")
+        return None, None
+
+def generate_response(model, tokenizer, user_input):
+    """
+    Generates a response using the GPT-2 model and tokenizer.
+    
+    Args:
+    - model: The loaded GPT-2 model.
+    - tokenizer: The tokenizer corresponding to the GPT-2 model.
+    - user_input (str): The input question from the user.
+
+    Returns:
+    - response (str): The generated response.
+    """
+    try:
+        inputs = tokenizer.encode(user_input, return_tensors='pt')
+        outputs = model.generate(inputs, max_length=512, num_return_sequences=1)
+        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        return response
+    
+    except Exception as e:
+        return f"An error occurred during response generation: {e}"
+
+# Load the model and tokenizer
+model, tokenizer = load_llm()
+
+if model is None or tokenizer is None:
+    print("Model and/or tokenizer loading failed.")
+else:
+    print("Model and tokenizer are ready for use.")
+
 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")
+# Initialize the Hugging Face API client
+# The InferenceClient does not take an api_key argument
+client = InferenceClient()
 
+def retrieval_QA_chain(llm, prompt, db):
+    # Placeholder function - ensure it integrates as needed
+    return RetrievalQA.from_chain_type(
+        llm=llm,
+        chain_type="stuff",
+        retriever=db.as_retriever(search_kwargs={'k': 2}),
+        return_source_documents=True,
+        chain_type_kwargs={'prompt': prompt}
+    )
 
-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]})
+def respond(message, history, system_message, max_tokens, temperature, top_p):
+    """
+    Handles interaction with the chatbot by sending the conversation history
+    and system message to the Hugging Face Inference API.
+    """
+    print("Starting respond function")
+    print("Received message:", message)
+    print("Conversation history:", history)
 
+    messages = [{"role": "system", "content": system_message}]
+    
+    for user_msg, assistant_msg in history:
+        if user_msg:
+            print("Adding user message to messages:", user_msg)
+            messages.append({"role": "user", "content": user_msg})
+        if assistant_msg:
+            print("Adding assistant message to messages:", assistant_msg)
+            messages.append({"role": "assistant", "content": assistant_msg})
+    
     messages.append({"role": "user", "content": message})
+    print("Final message list for the model:", messages)
 
     response = ""
+    try:
+        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
+            print("Token received:", token)
+            yield response
+    except Exception as e:
+        print("An error occurred:", e)
+        yield f"An error occurred: {e}"
 
-    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
-"""
+    print("Response generation completed")
+
+# Set up the Gradio ChatInterface
 demo = gr.ChatInterface(
-    respond,
+    fn=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)",
-        ),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
     ],
+    title="Human Rights AI Chatbot",
+    description="Ask questions about human rights, and get informed, passionate answers!"
 )
 
-
+# Launch the Gradio app
 if __name__ == "__main__":
     demo.launch()