Create app.py

app.py

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+# file: interactive_storytelling.py
+import streamlit as st
+from unsloth import FastLanguageModel
+from transformers import TextStreamer
+# import torch
+
+# Initialize the model and tokenizer
+max_seq_length = 2048
+dtype = None  # Auto-detection
+load_in_4bit = True
+
+# Load the pre-trained model
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name="suhaif/unsloth-llama-3-8b-4bit",
+    max_seq_length=max_seq_length,
+    dtype=dtype,
+    load_in_4bit=load_in_4bit,
+)
+
+FastLanguageModel.for_inference(model)
+
+# Set up the Streamlit app
+st.title("Interactive Storytelling Assistant")
+st.write("Collaboratively write stories and get real-time suggestions!")
+
+# Text input for the user prompt
+user_input = st.text_area("Write the beginning of your story:", placeholder="Once upon a time...")
+
+if st.button("Generate Suggestions"):
+    if user_input:
+        # Tokenize and send the input to the model
+        inputs = tokenizer([user_input], return_tensors="pt")
+
+        # Stream the generated output
+        text_streamer = TextStreamer(tokenizer)
+        st.write("Generating suggestions...")
+        generated_text = model.generate(**inputs, streamer=text_streamer, max_new_tokens=250)
+        
+        # Display the generated text
+        st.subheader("Story Suggestions:")
+        st.write(generated_text)
+
+        # Feedback section for rating
+        rating = st.slider("Rate the suggestion:", 1, 5)
+        if rating:
+            st.success(f"Thank you for your feedback! You rated this {rating} stars.")
+
+# Community sharing section
+st.subheader("Community Stories")
+st.write("Upload your story and share it with the community!")
+
+uploaded_story = st.file_uploader("Upload your story (txt file)", type="txt")
+
+if uploaded_story:
+    # Display uploaded story
+    story_text = uploaded_story.read().decode("utf-8")
+    st.text_area("Your Uploaded Story:", story_text, height=300)
+
+# Display other community stories (could be expanded with database integration)
+st.subheader("Community Stories Feedback Section")
+st.write("In this section, you can provide critiques for other community stories.")