Create app.py

app.py

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+# app.py on Hugging Face Space
+import gradio as gr
+import requests # Used for making HTTP requests to your backend
+import os
+
+# --- IMPORTANT ---
+# Replace this with your actual Cloudflare Worker URL after deployment.
+# You can also set this as a Hugging Face Space secret if you prefer.
+BACKEND_API_URL = os.getenv("BACKEND_API_URL", "https://actor-llm-deepseek-backend.smplushypermedia.workers.dev/")
+# Example: https://actor-llm-deepseek-backend.your-username.workers.dev
+
+# Store conversation history for context
+conversation_history = []
+current_script_in_session = ""
+current_character_in_session = ""
+
+def get_actor_advice(user_query, script_input, character_name_input):
+    global conversation_history, current_script_in_session, current_character_in_session
+
+    # 1. Check if script or character changed to reset context
+    if script_input != current_script_in_session or character_name_input != current_character_in_session:
+        conversation_history = [] # Reset history
+        current_script_in_session = script_input
+        current_character_in_session = character_name_input
+        gr.Warning("Script or character changed! Conversation context has been reset.")
+
+    # 2. Prepare payload for the Cloudflare Worker
+    payload = {
+        "userQuery": user_query,
+        "scriptContent": script_input,
+        "characterName": character_name_input,
+        "conversationHistory": conversation_history # Send current history
+    }
+
+    headers = {"Content-Type": "application/json"}
+
+    try:
+        # 3. Make HTTP POST request to your Cloudflare Worker
+        response = requests.post(BACKEND_API_URL, json=payload, headers=headers)
+        response.raise_for_status() # Raise HTTPError for bad responses (4xx or 5xx)
+
+        response_data = response.json()
+        llm_response = response_data.get("response", "No advice received.")
+
+        # 4. Update conversation history with user query and LLM response
+        conversation_history.append({"role": "user", "content": user_query})
+        conversation_history.append({"role": "assistant", "content": llm_response})
+
+        return llm_response
+    except requests.exceptions.RequestException as e:
+        print(f"Error communicating with backend: {e}")
+        return f"Error connecting to the backend. Please ensure the backend is deployed and accessible. Details: {e}"
+    except Exception as e:
+        print(f"An unexpected error occurred: {e}")
+        return f"An unexpected error occurred: {e}"
+
+# --- Frontend UI with Gradio ---
+with gr.Blocks() as demo:
+    gr.Markdown("# Actor's LLM Assistant")
+    gr.Markdown("Enter your script and ask for acting advice for your character. The AI will remember past queries in the current session.")
+
+    with gr.Row():
+        with gr.Column():
+            script_input = gr.Textbox(
+                label="Paste Your Script Here",
+                lines=10,
+                placeholder="[Scene: A dimly lit stage...]\nANNA: (Whispering) 'I can't believe this...'"
+            )
+            character_name_input = gr.Textbox(
+                label="Your Character's Name",
+                placeholder="e.g., Anna"
+            )
+            # Photo customization placeholder (as discussed, for UI or future multimodal)
+            photo_upload = gr.Image(
+                label="Upload Actor Photo (for UI personalization)",
+                type="pil", # Pillow image object
+                sources=["upload"],
+                interactive=True
+            )
+            gr.Markdown("*(Note: Photo customization is for UI personalization. The LLM itself currently processes text only.)*")
+
+        with gr.Column():
+            query_input = gr.Textbox(
+                label="Ask for Acting Advice",
+                placeholder="e.g., How should Anna deliver her line 'I can't believe this...' to convey despair?",
+                lines=3
+            )
+            submit_btn = gr.Button("Get Advice")
+            output_text = gr.Textbox(label="LLM Advice", lines=7)
+
+    submit_btn.click(
+        fn=get_actor_advice,
+        inputs=[query_input, script_input, character_name_input],
+        outputs=output_text
+    )
+
+    gr.Markdown("---")
+    gr.Markdown("Powered by DeepSeek LLMs, Hugging Face, and Cloudflare.")
+
+demo.launch(share=True)