Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# ---------- Model Pipelines ----------
+prompt_refiner = pipeline("text-generation", model="mistralai/Mixtral-8x7B-Instruct-v0.1")
+code_model = pipeline("text-generation", model="codellama/CodeLlama-7b-Instruct-hf")
+book_model = pipeline("text-generation", model="mistralai/Mixtral-8x7B-Instruct-v0.1")
+critic_1 = pipeline("text-generation", model="google/gemma-2-9b-it")
+critic_2 = pipeline("text-generation", model="meta-llama/Meta-Llama-3-8B-Instruct")
+
+# ---------- Core Logic ----------
+
+def refine_prompt(idea):
+    refined = prompt_refiner(f"Refine this creative idea into a high-quality prompt: {idea}", max_new_tokens=200)[0]["generated_text"]
+    return refined.strip()
+
+def generate_code(prompt):
+    pseudo = code_model(f"Create simple pseudocode for: {prompt}", max_new_tokens=200)[0]["generated_text"]
+    simple = code_model(f"Expand this pseudocode into a simple code snippet:\n{pseudo}", max_new_tokens=300)[0]["generated_text"]
+    full = code_model(f"Turn this snippet into a complete, working program:\n{simple}", max_new_tokens=700)[0]["generated_text"]
+    return pseudo.strip(), simple.strip(), full.strip()
+
+def generate_book(prompt):
+    structure = ["Start", "Development", "Climax", "Conclusion", "End"]
+    parts = []
+    for section in structure:
+        part = book_model(f"Write the {section} section of a short book based on this idea: {prompt}", max_new_tokens=500)[0]["generated_text"]
+        parts.append(f"### {section}\n{part.strip()}\n")
+    return "\n".join(parts)
+
+def get_critic_feedback(output_text):
+    critique_1 = critic_1(f"Rate this text from 0 to 100 and explain why:\n{output_text}", max_new_tokens=200)[0]["generated_text"]
+    critique_2 = critic_2(f"Rate this text from 0 to 100 and explain why:\n{output_text}", max_new_tokens=200)[0]["generated_text"]
+    return critique_1.strip(), critique_2.strip()
+
+def refine_output_based_on_critics(output_text, feedback1, feedback2):
+    combined_feedback = f"Critic 1: {feedback1}\nCritic 2: {feedback2}"
+    refined = prompt_refiner(f"Refine this text based on the critics' feedback:\n{combined_feedback}\nOriginal text:\n{output_text}", max_new_tokens=700)[0]["generated_text"]
+    return refined.strip()
+
+# ---------- Gradio Workflow ----------
+
+def workflow(idea, mode):
+    refined_prompt = refine_prompt(idea)
+
+    if mode == "Code mode":
+        pseudo, simple, full = generate_code(refined_prompt)
+        initial_output = full
+        output_text = f"## Refined Prompt\n{refined_prompt}\n\n### Pseudocode\n{pseudo}\n\n### Simple Code\n{simple}\n\n### Final Code\n{full}"
+    else:
+        book_text = generate_book(refined_prompt)
+        initial_output = book_text
+        output_text = f"## Refined Prompt\n{refined_prompt}\n\n{book_text}"
+
+    feedback1, feedback2 = get_critic_feedback(initial_output)
+    refined_final = refine_output_based_on_critics(initial_output, feedback1, feedback2)
+
+    return refined_prompt, output_text, feedback1, feedback2, refined_final
+
+
+# ---------- UI Layout ----------
+
+with gr.Blocks() as demo:
+    gr.Markdown("# 🤖 AI Workflow: Code or Book Creator with Self-Critique")
+
+    idea_input = gr.Textbox(label="Enter your idea", placeholder="Type an idea...")
+    mode_select = gr.Radio(["Code mode", "Book mode"], label="Mode")
+    submit = gr.Button("Generate")
+
+    refined_prompt_box = gr.Textbox(label="Refined Prompt")
+    output_box = gr.Markdown(label="Generated Output")
+    critic1_box = gr.Textbox(label="Critic 1 Feedback")
+    critic2_box = gr.Textbox(label="Critic 2 Feedback")
+    refined_output_box = gr.Markdown(label="Final Refined Version")
+
+    submit.click(
+        fn=workflow,
+        inputs=[idea_input, mode_select],
+        outputs=[refined_prompt_box, output_box, critic1_box, critic2_box, refined_output_box],
+    )
+
+demo.launch()