Update app.py

app.py

@@ -55,7 +55,7 @@ def create_space_action(repo_name: str, sdk: str, profile: gr.OAuthProfile, toke
             space_sdk=sdk
         )
         url    = f"https://huggingface.co/spaces/{repo_id}"
-        iframe = f'<iframe src="{url}" width="100%" height="500px"></iframe>'
+        iframe = f'<iframe src="{url}" width="100%" height="500px"></iframe' # Fixed closing tag
         return repo_id, iframe
     except Exception as e:
         raise RuntimeError(f"Failed to create Space `{repo_id}`: {e}")
@@ -175,7 +175,13 @@ def call_gemini(prompt: str, api_key: str, model_name: str, use_grounding: bool
         if response.prompt_feedback and response.prompt_feedback.block_reason:
              raise RuntimeError(f"Gemini API call blocked: {response.prompt_feedback.block_reason}")
         if not response.candidates:
-             raise RuntimeError("Gemini API call returned no candidates.")
+             # Check for safety ratings if no candidates are returned but not blocked
+             if response.prompt_feedback and response.prompt_feedback.safety_ratings:
+                  ratings = "; ".join([f"{r.category}: {r.probability}" for r in response.prompt_feedback.safety_ratings])
+                  raise RuntimeError(f"Gemini API call returned no candidates. Safety ratings: {ratings}")
+             else:
+                  raise RuntimeError("Gemini API call returned no candidates.")
+
 
         # If response.candidates is not empty, get the text
         # Using response.text is a convenient way to get text from the first candidate part
@@ -212,6 +218,8 @@ def add_bot_message(history: list[dict], bot_message: str) -> list[dict]:
     return history
 
 # This is the main generator function for the workflow, triggered by the 'Send' button
+# NOTE: This function MUST accept ALL state variables as inputs that it might need to modify or pass through.
+# It MUST also yield/return ALL state variables in the same order they appear in the `outputs` list of the `.click()` event.
 def ai_workflow_chat(
     message: str,
     history: list[dict],
@@ -273,7 +281,7 @@ def ai_workflow_chat(
 
     # Yield immediately to update the chat UI with the user's message
     # This provides immediate feedback to the user while the AI processes
-    # Ensure all state variables are yielded back
+    # Ensure all state variables are yielded back in the correct order
     yield (history, repo_id, state, updated_preview, updated_run, updated_build,
            attempts, app_desc, repo_name, generated_code, use_grounding)
 
@@ -282,6 +290,7 @@ def ai_workflow_chat(
 
         if state == STATE_IDLE:
             # Check prerequisites before starting any workflow actions
+            # Use the passed-in state variable values for checks
             if not (hf_profile and hf_token):
                 history = add_bot_message(history, "Workflow paused: Please log in to Hugging Face first.")
                 # Yield updated history and current state, then exit for this click
@@ -308,7 +317,7 @@ def ai_workflow_chat(
                 history = add_bot_message(history, "Workflow reset.")
                 # Yield updated history and reset state variables to their initial values
                 yield (history, None, STATE_IDLE, "<p>No Space created yet.</p>", "", "", 0,
-                       None, None, None, False) # Reset use_grounding to default False
+                       None, None, None, False) # Reset use_grounding to default False, and other states to None/default
                 # No return needed after yield in this generator pattern; execution for this click ends here.
 
             elif generate_match:
@@ -500,16 +509,36 @@ Return **only** the python code block for `app.py`. Do not include any extra tex
              # Logic to determine required packages based on SDK and keywords in the app description
              reqs_list = ["gradio"] if space_sdk == "gradio" else ["streamlit"]
              # Add essential libraries regardless of description keywords or grounding
-             reqs_list.extend(["google-generativeai", "huggingface_hub", "Pillow", "numpy", "pandas", "requests"]) # Assume common ones might be needed
-             # Add scikit-image and opencv if description implies image processing
+             # Be more specific about which packages might be needed
+             essential_libs = ["google-generativeai", "huggingface_hub"]
+             if gemini_api_key and gemini_model: # Only add if Gemini is actually configured
+                  reqs_list.extend(essential_libs)
+
+             # Add common libraries if description suggests they might be needed
              if app_desc:
                  app_desc_lower = app_desc.lower()
+                 if "requests" in app_desc_lower or "api" in app_desc_lower:
+                      reqs_list.append("requests")
+                 # Image processing libraries
+                 if "image" in app_desc_lower or "upload" in app_desc_lower or "blur" in app_desc_lower or "vision" in app_desc_lower or "photo" in app_desc_lower:
+                     reqs_list.append("Pillow")
+                 if "numpy" in app_desc_lower: reqs_list.append("numpy")
+                 if "pandas" in app_desc_lower or "dataframe" in app_desc_lower: reqs_list.append("pandas")
+                 # Add scikit-image and opencv if image processing is heavily implied
                  if any(lib in app_desc_lower for lib in ["scikit-image", "skimage", "cv2", "opencv"]):
                       reqs_list.extend(["scikit-image", "opencv-python"]) # Note: opencv-python for pip
-
+                 # Add transformers if large models are implied
+                 if any(lib in app_desc_lower for lib in ["transformer", "llama", "mistral", "bert", "gpt2"]):
+                     reqs_list.append("transformers")
+                 # Add torch or tensorflow if deep learning frameworks are implied
+                 if any(lib in app_desc_lower for lib in ["torch", "pytorch", "tensorflow", "keras"]):
+                     reqs_list.extend(["torch", "tensorflow"]) # Users might need specific versions, but this is a start
 
              # Use dict.fromkeys to get unique items while preserving insertion order (Python 3.7+)
              reqs_list = list(dict.fromkeys(reqs_list))
+             # Sort alphabetically for cleaner requirements.txt
+             reqs_list.sort()
+
 
              reqs_content = "\n".join(reqs_list) + "\n"
 
@@ -800,7 +829,7 @@ with gr.Blocks(title="AI-Powered HF Space App Builder") as ai_builder_tab:
     # Gradio State variables - these persist their values across user interactions (clicks)
     hf_profile   = gr.State(None)
     hf_token     = gr.State(None)
-    # FIX: Initialize gemini_key state from env var on load for robustness
+    # FIX: Initialize gemini_key state from env var on load
     gemini_key   = gr.State(os.environ.get("GOOGLE_API_KEY"))
     gemini_model = gr.State("gemini-1.5-flash") # Default selected model
     repo_id      = gr.State(None) # Stores the ID of the created Space
@@ -821,35 +850,18 @@ with gr.Blocks(title="AI-Powered HF Space App Builder") as ai_builder_tab:
             # Hugging Face Login Button
             login_btn    = gr.LoginButton(variant="huggingface")
 
-            # Initial load event to check login status (if cached) and explicitly set gemini_key state
-            # Chain events: Show profile -> Set Gemini Key state -> Configure Gemini -> Update Send Button
-            def initial_setup_on_load(profile, token):
-                 # This function explicitly sets the gemini_key state from the env var
-                 # This runs *after* the profile is potentially loaded, ensuring gemini_key is set
-                 # before configure_gemini and update_send_button_state read it from state.
-                 # It needs to return the values for the next step in the chain.
-                 return profile, token, os.environ.get("GOOGLE_API_KEY")
-
-            ai_builder_tab.load(
-                 initial_setup_on_load,
-                 inputs=[hf_profile, hf_token], # Pass current state values
-                 outputs=[hf_profile, hf_token, gemini_key] # Update state values
-            ).then( # Chain after initial state setup
-                configure_gemini,
-                inputs=[gemini_key, gemini_model],
-                outputs=[gemini_status] # Update Gemini status text
-            ).then( # Chain after config status
-                update_send_button_state,
-                inputs=[hf_profile, hf_token, gemini_key, gemini_model],
-                outputs=[send_btn, prereq_status] # Update button interactivity and status text
-            )
-
-
             # Update status display when login button reports success
-            login_btn.click(show_profile, outputs=login_status).then(
-                 update_send_button_state, # Recalculate status and button state
+            # Chain to also update the Send button/prereq status
+            login_btn.click(
+                 # The LoginButton outputs a tuple (OAuthProfile, OAuthToken) on success
+                 lambda x: (x[0], x[1]),
+                 inputs=[login_btn],
+                 outputs=[hf_profile, hf_token] # Update these State variables
+            ).then( # Chain the next action after state is updated
+                 # Pass all relevant state variables to re-evaluate prerequisites
+                 update_send_button_state,
                  inputs=[hf_profile, hf_token, gemini_key, gemini_model],
-                 outputs=[send_btn, prereq_status]
+                 outputs=[send_btn, prereq_status] # Update button interactivity and status text
             )
 
             gr.Markdown("## Google AI Studio / Gemini")
@@ -893,7 +905,6 @@ with gr.Blocks(title="AI-Powered HF Space App Builder") as ai_builder_tab:
                 update_send_button_state, inputs=[hf_profile, hf_token, gemini_key, gemini_model], outputs=[send_btn, prereq_status] # Update button/prereq status
             )
 
-
             # New checkbox for optional grounding
             use_grounding_checkbox = gr.Checkbox(
                 label="Enable Grounding with Google Search",
@@ -902,6 +913,7 @@ with gr.Blocks(title="AI-Powered HF Space App Builder") as ai_builder_tab:
                 info="Use Google Search results to inform Gemini's response (may improve factuality)."
             )
             # Link checkbox change to update the state variable
+            # No need to update send button status here, as grounding is not a core prerequisite
             use_grounding_checkbox.change(
                 lambda v: v, inputs=use_grounding_checkbox, outputs=use_grounding_state
             )
@@ -934,6 +946,7 @@ with gr.Blocks(title="AI-Powered HF Space App Builder") as ai_builder_tab:
 
             # Helper function to control send button interactivity and prerequisite status text
             # This function is triggered by changes in login status and Gemini configuration
+            # Defined outside Block but used inside event handlers
             def update_send_button_state(
                 profile: gr.OAuthProfile | None,
                 token: gr.OAuthToken | None,
@@ -1005,12 +1018,32 @@ with gr.Blocks(title="AI-Powered HF Space App Builder") as ai_builder_tab:
             # When the 'repo_id' state variable changes, update the text in repo_id_text
             repo_id.change(lambda r: r if r else "None", inputs=repo_id, outputs=repo_id_text)
 
-
-    # Add an initial welcome message to the chatbot when the UI loads
-    def greet():
-        return [{"role": "assistant", "content": "Welcome! Please log in to Hugging Face and provide your Google AI Studio API key to start building Spaces. Once ready, type 'generate me a gradio app called myapp' or 'create' to begin."}]
-
-    ai_builder_tab.load(greet, outputs=chatbot)
+    # --- Initial Load Event Chain (Defined INSIDE gr.Blocks) ---
+    # This chain runs once when the app loads
+    ai_builder_tab.load(
+        # Action 1: Configure Gemini using the initial state values (from env var if set)
+        configure_gemini,
+        inputs=[gemini_key, gemini_model],
+        outputs=[gemini_status] # Update Gemini status display
+    ).then(
+        # Action 2: Update the send button state and prerequisite status based on initial states
+        update_send_button_state,
+        inputs=[hf_profile, hf_token, gemini_key, gemini_model],
+        outputs=[send_btn, prereq_status] # Update button interactivity and status text
+    ).then(
+        # Action 3: Add the initial welcome message to the chatbot
+        greet,
+        inputs=None,
+        outputs=chatbot
+    )
+
+
+# These helper functions and the main workflow function are correctly defined OUTSIDE the gr.Blocks context
+# because they operate on the *values* passed to them by Gradio event triggers, not the UI component objects themselves.
+
+# Add an initial welcome message to the chatbot (defined outside Blocks to be called by load chain)
+def greet():
+    return [{"role": "assistant", "content": "Welcome! Please log in to Hugging Face and provide your Google AI Studio API key to start building Spaces. Once ready, type 'generate me a gradio app called myapp' or 'create' to begin."}]
 
 
 if __name__ == "__main__":
@@ -1031,4 +1064,5 @@ if __name__ == "__main__":
     os.makedirs(os.environ["GRADIO_TEMP_DIR"], exist_ok=True) # Ensure the directory exists
 
     # Launch the Gradio UI
-    # The Gradio launch call blo
+    # The Gradio launch call blocks execution.
+    ai_builder_tab.launch()