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.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+cineguide-merged/*.safetensors filter=lfs diff=lfs merge=lfs -text
+cineguide-merged/tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,13 @@
----
-license: apache-2.0
----
+---
+title: Cineguide Comparator
+emoji: 📉
+colorFrom: red
+colorTo: green
+sdk: gradio
+sdk_version: 5.32.1
+app_file: app.py
+pinned: false
+license: apache-2.0
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,305 @@
+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer
+import time
+import os
+
+# --- Configuration ---
+BASE_MODEL_ID = "Qwen/Qwen2.5-7B-Instruct"
+# Path to your merged fine-tuned model within the Hugging Face Space
+# If 'cineguide-merged' is at the root of your Space repo:
+FINETUNED_MODEL_PATH = "cineguide-merged"
+
+# System prompts
+SYSTEM_PROMPT_CINEGUIDE = """You are CineGuide, a knowledgeable and friendly movie recommendation assistant. Your goal is to:
+1. Provide personalized movie recommendations based on user preferences
+2. Give brief, compelling rationales for why you recommend each movie
+3. Ask thoughtful follow-up questions to better understand user tastes
+4. Maintain an enthusiastic but not overwhelming tone about cinema
+
+When recommending movies, always explain WHY the movie fits their preferences."""
+
+SYSTEM_PROMPT_BASE = "You are a helpful AI assistant."
+
+# --- Model Loading ---
+# Cache models globally so they are loaded only once
+_models_cache = {}
+
+def get_model_and_tokenizer(model_id_or_path):
+    if model_id_or_path in _models_cache:
+        return _models_cache[model_id_or_path]
+
+    print(f"Loading model: {model_id_or_path}")
+    tokenizer = AutoTokenizer.from_pretrained(model_id_or_path, trust_remote_code=True)
+    model = AutoModelForCausalLM.from_pretrained(
+        model_id_or_path,
+        torch_dtype=torch.bfloat16, # Use bfloat16 for faster inference
+        device_map="auto",          # Automatically distribute across GPUs if available
+        trust_remote_code=True,
+        # attn_implementation="flash_attention_2" # Optional: if supported by Space hardware & transformers version
+    )
+    model.eval() # Set to evaluation mode
+
+    if tokenizer.pad_token is None:
+        tokenizer.pad_token = tokenizer.eos_token
+        tokenizer.pad_token_id = tokenizer.eos_token_id
+
+    _models_cache[model_id_or_path] = (model, tokenizer)
+    print(f"Finished loading: {model_id_or_path}")
+    return model, tokenizer
+
+# Pre-load models when the script starts
+# This can take time, so Gradio might show a loading screen.
+# For Spaces, this happens during the build/startup phase.
+print("Pre-loading models...")
+try:
+    model_base, tokenizer_base = get_model_and_tokenizer(BASE_MODEL_ID)
+    print("Base model loaded.")
+except Exception as e:
+    print(f"Error loading base model: {e}")
+    model_base, tokenizer_base = None, None
+
+# Check if fine-tuned model path exists before loading
+if os.path.exists(FINETUNED_MODEL_PATH) and os.path.isdir(FINETUNED_MODEL_PATH):
+    try:
+        model_ft, tokenizer_ft = get_model_and_tokenizer(FINETUNED_MODEL_PATH)
+        print("Fine-tuned model loaded.")
+    except Exception as e:
+        print(f"Error loading fine-tuned model from {FINETUNED_MODEL_PATH}: {e}")
+        model_ft, tokenizer_ft = None, None
+else:
+    print(f"Fine-tuned model path not found: {FINETUNED_MODEL_PATH}. Skipping fine-tuned model.")
+    model_ft, tokenizer_ft = None, None
+print("Model pre-loading complete.")
+
+
+# --- Inference Function ---
+def generate_chat_response(message: str, chat_history: list, model_type: str):
+    if model_type == "base":
+        model, tokenizer = model_base, tokenizer_base
+        system_prompt = SYSTEM_PROMPT_BASE
+    elif model_type == "finetuned":
+        model, tokenizer = model_ft, tokenizer_ft
+        system_prompt = SYSTEM_PROMPT_CINEGUIDE
+    else:
+        yield "Invalid model type."
+        return
+
+    if model is None or tokenizer is None:
+        yield f"Model '{model_type}' is not available."
+        return
+
+    conversation = []
+    if system_prompt:
+        conversation.append({"role": "system", "content": system_prompt})
+
+    for user_msg, assistant_msg in chat_history:
+        conversation.append({"role": "user", "content": user_msg})
+        conversation.append({"role": "assistant", "content": assistant_msg})
+    conversation.append({"role": "user", "content": message})
+
+    # Apply chat template
+    prompt = tokenizer.apply_chat_template(
+        conversation,
+        tokenize=False,
+        add_generation_prompt=True # This adds the <|im_start|>assistant prefix
+    )
+
+    inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=1800).to(model.device)
+    streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+
+    generation_kwargs = dict(
+        inputs,
+        streamer=streamer,
+        max_new_tokens=512,
+        do_sample=True,
+        temperature=0.7,
+        top_p=0.9,
+        repetition_penalty=1.1,
+        pad_token_id=tokenizer.eos_token_id,
+        eos_token_id=tokenizer.eos_token_id,
+    )
+
+    # For streaming, run generation in a separate thread
+    # For Gradio, we can yield partial results
+    # However, TextStreamer prints to stdout. For Gradio, we need to capture.
+    
+    # Simpler non-streaming approach for direct yield:
+    # Remove streamer from generation_kwargs
+    # outputs = model.generate(**generation_kwargs_without_streamer)
+    # decoded_output = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
+    # yield decoded_output
+    
+    # More complex streaming for Gradio:
+    full_response = ""
+    generated_token_ids = model.generate(
+        **inputs,
+        max_new_tokens=512,
+        do_sample=True,
+        temperature=0.7,
+        top_p=0.9,
+        repetition_penalty=1.1,
+        pad_token_id=tokenizer.eos_token_id,
+        eos_token_id=[tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<|im_end|>")]
+    )
+    
+    # Decode only the newly generated tokens
+    new_tokens = generated_token_ids[0, inputs['input_ids'].shape[1]:]
+    response_text = tokenizer.decode(new_tokens, skip_special_tokens=True).strip()
+    response_text = response_text.replace("<|im_end|>", "").strip() # Clean up
+    
+    # Yield character by character for streaming effect (can be slow for long responses)
+    # A better way is to yield chunks. For simplicity, this is char by char.
+    for char in response_text:
+        full_response += char
+        time.sleep(0.005) # Adjust for desired speed
+        yield full_response
+
+
+def respond_base(message, chat_history):
+    # chat_history is a list of [user_msg, assistant_msg]
+    yield from generate_chat_response(message, chat_history, "base")
+
+def respond_finetuned(message, chat_history):
+    yield from generate_chat_response(message, chat_history, "finetuned")
+
+
+# --- Gradio UI ---
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown(
+        """
+        # 🎬 CineGuide vs. Base Qwen2.5-7B-Instruct
+        Compare the fine-tuned CineGuide movie recommender with the base Qwen2.5-7B-Instruct model.
+        Type your movie-related query below and see how each model responds!
+        """
+    )
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("## 🗣️ Base Qwen2.5-7B-Instruct")
+            chatbot_base = gr.Chatbot(label="Base Model Chat", height=500, bubble_full_width=False)
+            if model_base is None:
+                 gr.Markdown("⚠️ Base model could not be loaded. This chat interface will not work.")
+
+        with gr.Column(scale=1):
+            gr.Markdown("## 🤖 Fine-tuned CineGuide (Qwen2.5-7B)")
+            chatbot_ft = gr.Chatbot(label="CineGuide Chat", height=500, bubble_full_width=False)
+            if model_ft is None:
+                 gr.Markdown("⚠️ Fine-tuned model could not be loaded. This chat interface will not work.")
+
+    with gr.Row():
+        shared_input_textbox = gr.Textbox(
+            show_label=False,
+            placeholder="Enter your movie query here and press Enter...",
+            container=False,
+            scale=7, # Make it wider
+        )
+        submit_button = gr.Button("✉️ Send", variant="primary", scale=1)
+        # clear_button = gr.Button("🗑️ Clear All", scale=1) # If you want a single clear button
+
+    # Predefined examples
+    gr.Examples(
+        examples=[
+            "Hi! I'm looking for something funny to watch tonight.",
+            "I love dry, witty humor more than slapstick. Think more British comedy style.",
+            "I'm really into complex sci-fi movies that make you think. I loved Arrival and Blade Runner 2049.",
+            "I need help planning a family movie night. We have kids aged 8, 11, and 14, plus adults.",
+            "I'm going through a tough breakup and need something uplifting but not cheesy romantic.",
+            "I loved Parasite and want to explore more international cinema. Where should I start?",
+        ],
+        inputs=[shared_input_textbox],
+        # outputs=[chatbot_base, chatbot_ft], # Examples don't directly populate chatbots
+        # fn=lambda x: (None, None), # Dummy function for examples
+        label="Example Prompts (click to use)"
+    )
+
+    # Event handlers
+    def handle_submit(user_message, chat_history_base, chat_history_ft):
+        # This will return iterators. Gradio handles them for streaming.
+        # Important: chat_history is updated by Gradio automatically by returning (user_message, bot_message_chunk)
+        # For simultaneous updates, we need to manage history carefully or use a trick.
+        # Gradio's chatbot expects the history list to be updated.
+        # The `respond_base` and `respond_finetuned` functions already take history.
+        # The issue is that Gradio wants a function that returns the new state of the chatbot.
+        
+        # Simplest for simultaneous: return None for the other chatbot if we trigger one by one.
+        # For true simultaneous, you'd need a more complex setup or separate submit buttons.
+        # Let's make them update sequentially for simplicity with one input.
+        
+        # Update base model chat
+        chat_history_base.append((user_message, None)) # Add user message
+        # The `yield` from respond_base will update the last message (None)
+        
+        # Update fine-tuned model chat
+        chat_history_ft.append((user_message, None)) # Add user message
+        
+        # We need to return generators that Gradio can iterate over
+        # This won't work directly as Gradio expects outputs to be bound to specific components.
+        # We need to make the function return the new state for *both* chatbots.
+        # The `respond_base` and `respond_finetuned` should update their respective histories.
+        
+        # Gradio's Chatbot expects (message, history) -> history or (message, history) -> yield history_updates
+        # Let's define wrapper functions for the submit action.
+        return "", chat_history_base, chat_history_ft # Clear textbox, pass history
+
+    def base_model_predict(user_message, chat_history):
+        chat_history.append((user_message, "")) # Add user message and placeholder for bot
+        for response_chunk in respond_base(user_message, chat_history[:-1]): # Pass history without current turn
+            chat_history[-1] = (user_message, response_chunk)
+            yield chat_history
+    
+    def ft_model_predict(user_message, chat_history):
+        chat_history.append((user_message, ""))
+        for response_chunk in respond_finetuned(user_message, chat_history[:-1]):
+            chat_history[-1] = (user_message, response_chunk)
+            yield chat_history
+
+    # When shared_input_textbox is submitted or submit_button is clicked:
+    if model_base is not None:
+        shared_input_textbox.submit(
+            base_model_predict,
+            [shared_input_textbox, chatbot_base],
+            [chatbot_base],
+        )
+        submit_button.click(
+            base_model_predict,
+            [shared_input_textbox, chatbot_base],
+            [chatbot_base],
+        )
+    
+    if model_ft is not None:
+        shared_input_textbox.submit(
+            ft_model_predict,
+            [shared_input_textbox, chatbot_ft],
+            [chatbot_ft],
+        )
+        submit_button.click(
+            ft_model_predict,
+            [shared_input_textbox, chatbot_ft],
+            [chatbot_ft],
+        )
+    
+    # After both predictions are done (or if one is skipped), clear the input textbox
+    # This is a bit tricky with simultaneous submits.
+    # A simpler way is to clear it on the second submit if both models are active.
+    # Or, let Gradio handle textbox clearing by returning "" as the first element of the outputs list.
+    
+    # If ft_model_predict is the last one to be called from submit:
+    if model_ft is not None:
+         shared_input_textbox.submit(lambda: "", [], [shared_input_textbox])
+         submit_button.click(lambda: "", [], [shared_input_textbox])
+    elif model_base is not None: # If only base model is active
+         shared_input_textbox.submit(lambda: "", [], [shared_input_textbox])
+         submit_button.click(lambda: "", [], [shared_input_textbox])
+
+
+    # Clear buttons (Individual)
+    # clear_base_btn = gr.Button("🗑️ Clear Base Chat")
+    # clear_ft_btn = gr.Button("🗑️ Clear CineGuide Chat")
+    # clear_base_btn.click(lambda: (None, ""), None, [chatbot_base, shared_input_textbox], queue=False)
+    # clear_ft_btn.click(lambda: (None, ""), None, [chatbot_ft, shared_input_textbox], queue=False)
+
+# --- Launch the App ---
+if __name__ == "__main__":
+    demo.queue() # Enable queuing for handling multiple users
+    demo.launch(debug=True, share=False) # share=True for public link if running locally

cineguide-merged/README.md

@@ -0,0 +1,33 @@
+
+# CineGuide: Conversational Movie Recommendation Assistant
+
+## Model Description
+This model is a fine-tuned version of Qwen2.5-7B-Instruct for conversational movie recommendations.
+
+## Training Details
+- **Base Model**: Qwen/Qwen2.5-7B-Instruct
+- **Method**: LoRA (Low-Rank Adaptation) with rank-16
+- **Dataset**: ReDial corpus (7999 training examples)
+- **Training Loss**: 0.9140
+- **Perplexity**: 2.49
+
+## Usage
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("./cineguide-merged")
+model = AutoModelForCausalLM.from_pretrained("./cineguide-merged")
+
+# Generate movie recommendations
+prompt = "I love sci-fi movies with complex plots. Any recommendations?"
+# ... [generation code]
+```
+
+## Performance
+The model shows significant improvement over the base model in:
+- Providing specific movie recommendations with rationales
+- Maintaining conversational context
+- Understanding genre preferences
+- Giving compelling explanations for recommendations
+
+Created for CS515 Deep Learning Course Project by Serhan Yilmaz (00031275)

cineguide-merged/added_tokens.json

@@ -0,0 +1,24 @@
+{
+  "</tool_call>": 151658,
+  "<tool_call>": 151657,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
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+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

cineguide-merged/chat_template.jinja

@@ -0,0 +1,54 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

cineguide-merged/config.json

@@ -0,0 +1,28 @@
+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 3584,
+  "initializer_range": 0.02,
+  "intermediate_size": 18944,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "model_type": "qwen2",
+  "num_attention_heads": 28,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 4,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": 131072,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.52.4",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}

cineguide-merged/generation_config.json

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+  "do_sample": true,
+  "eos_token_id": [
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+    151643
+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.05,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.52.4"
+}

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requirements.txt

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