Update app.py

app.py

@@ -1,64 +1,96 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+from unsloth import FastLanguageModel
+import torch
 
-"""
-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")
+# Load the pre-trained language model and tokenizer
+model_name = "suhaif/unsloth-llama-3-8b-4bit"
+max_seq_length = 2048
+dtype = None
+load_in_4bit = True
 
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name=model_name,
+    max_seq_length=max_seq_length,
+    dtype=dtype,
+    load_in_4bit=load_in_4bit
+)
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+# Default instruction for generating the story
+default_instruction = "You are a creative writer. Based on the given input, generate a well-structured story with an engaging plot, well-developed characters, and immersive details. Ensure the story has a clear beginning, middle, and end. Include dialogue and descriptions to bring the story to life. You can add twist to the story also"
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+# Function to format the prompt
+def format_prompt(input_text, instruction=default_instruction):
+    return f"{instruction}\n\nInput:\n{input_text}\n\nResponse:\n"
 
-    messages.append({"role": "user", "content": message})
+# Function to generate story from the model
+def generate_story(user_input):
+    # Format the input
+    prompt = format_prompt(user_input)
+    inputs = tokenizer([prompt], return_tensors="pt").to("cuda")
 
-    response = ""
+    # Generate output from the model
+    outputs = model.generate(**inputs, max_new_tokens=500, use_cache=True)
+    
+    # Decode and return the result
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
 
-    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
+# Feedback mechanism (collects and stores feedback)
+feedback_data = []
 
-        response += token
-        yield response
+def submit_feedback(rating, feedback_text, story):
+    feedback_data.append({
+        "rating": rating,
+        "feedback_text": feedback_text,
+        "story": story
+    })
+    return "Thank you for your feedback!"
 
+# Community engagement feature - to upload and share stories
+shared_stories = []
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    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)",
-        ),
-    ],
-)
+def share_story(title, story_text):
+    shared_stories.append({"title": title, "story_text": story_text})
+    return f"Story '{title}' has been shared successfully!"
 
+def display_stories():
+    return [(story['title'], story['story_text']) for story in shared_stories]
 
-if __name__ == "__main__":
+# Gradio interface
+def storytelling_interface():
+    # User inputs
+    with gr.Blocks() as demo:
+        gr.Markdown("# Interactive Storytelling Assistant")
+        
+        with gr.Row():
+            with gr.Column():
+                user_input = gr.Textbox(label="Enter your story prompt", placeholder="A young adventurer embarks on a journey to find a lost treasure...", lines=4)
+                generate_button = gr.Button("Generate Story")
+                
+                story_output = gr.Textbox(label="Generated Story", placeholder="Generated story will appear here...", lines=10, interactive=False)
+                
+                generate_button.click(fn=generate_story, inputs=user_input, outputs=story_output)
+            
+            with gr.Column():
+                gr.Markdown("## Provide Feedback")
+                rating = gr.Slider(1, 5, step=1, label="Rate the story")
+                feedback_text = gr.Textbox(label="Feedback", placeholder="Provide any suggestions or comments...", lines=3)
+                submit_feedback_button = gr.Button("Submit Feedback")
+                submit_feedback_button.click(fn=submit_feedback, inputs=[rating, feedback_text, story_output], outputs=None)
+        
+        with gr.Row():
+            gr.Markdown("## Share your Story")
+            title = gr.Textbox(label="Story Title", placeholder="Enter the title of your story")
+            story_text = gr.Textbox(label="Your Story", placeholder="Enter your full story here...", lines=8)
+            share_button = gr.Button("Share Story")
+            share_button.click(fn=share_story, inputs=[title, story_text], outputs=None)
+
+        with gr.Row():
+            gr.Markdown("## Browse Shared Stories")
+            stories_list = gr.Dropdown(display_stories, label="Select a story to read")
+            story_display = gr.Textbox(label="Story Content", lines=10, interactive=False)
+            stories_list.change(fn=lambda title: next(story['story_text'] for story in shared_stories if story['title'] == title), inputs=stories_list, outputs=story_display)
+    
     demo.launch()
+
+# Start the storytelling interface
+storytelling_interface()