Update app.py

app.py

@@ -1,18 +1,16 @@
 import gradio as gr
 import os
 import json
-import mimetypes
 from dotenv import load_dotenv
 
-# Your exact requested imports
+# Your requested imports
 from google import genai
-from google.genai import types
+from google.genai import types # Still needed for GenerationConfig
 
 # --- Configuration and Client Initialization ---
 load_dotenv()
 
 try:
-    # Initializing the client exactly as in your code
     client = genai.Client(api_key=os.environ["GEMINI_API_KEY"])
 except KeyError:
     raise gr.Error("FATAL: GEMINI_API_KEY not found. Please set it in your Hugging Face Space secrets.")
@@ -23,16 +21,12 @@ def analyze_device_condition(video_file_path):
     if not video_file_path:
         return "Please upload video", "", ""
 
+    uploaded_file = None # Initialize to ensure it exists for the finally block
     try:
-        # 1. Prepare video file
-        mime_type, _ = mimetypes.guess_type(video_file_path)
-        if not mime_type or not mime_type.startswith("video"):
-            raise ValueError("Unsupported file type.")
-        
-        with open(video_file_path, "rb") as video:
-            video_part = types.Part(
-                inline_data=types.Blob(mime_type=mime_type, data=video.read())
-            )
+        # 1. --- UPLOAD FILE USING THE NEW, CORRECT METHOD ---
+        print(f"Log: Uploading file: {video_file_path}...")
+        uploaded_file = client.files.upload(file_path=video_file_path)
+        print("Log: File uploaded successfully.")
         
         # 2. Prepare the prompt
         prompt = """
@@ -41,35 +35,27 @@ def analyze_device_condition(video_file_path):
         2. "condition": A single word: "Mint", "Excellent", "Good", "Fair", or "Poor".
         3. "reason": A brief string explaining the condition.
         """
-        prompt_part = types.Part.from_text(prompt)
-
-        # 3. --- THIS IS THE KEY CORRECTION ---
-        #    Wrapping the parts inside types.Content, exactly like your reference code.
-        contents = [
-            types.Content(parts=[prompt_part, video_part])
-        ]
-        # --- END OF CORRECTION ---
         
-        # 4. Use gemini-2.5-flash and the specified config
+        # 3. Use gemini-2.5-flash and the specified config
         model_name = "gemini-2.5-flash"
         generate_content_config = types.GenerateContentConfig(
             temperature=0.2,
             response_mime_type="application/json"
         )
         
-        # 5. Call the API using the streaming method from your code
+        # 4. --- CALL THE API WITH THE SIMPLIFIED CONTENTS LIST ---
+        #    Just the uploaded file object and the prompt string.
+        contents = [uploaded_file, prompt]
+        
         print(f"Log: Sending request to model: {model_name}...")
-        stream = client.models.generate_content_stream(
+        response = client.models.generate_content(
             model=f"models/{model_name}",
             contents=contents,
             generation_config=generate_content_config,
         )
 
-        # 6. Handle the streamed response to build the complete JSON string
-        response_text = "".join(chunk.text for chunk in stream)
-
-        # 7. Parse the final JSON response
-        parsed_json = json.loads(response_text)
+        # 5. Parse the final JSON response
+        parsed_json = json.loads(response.text)
         device_type = parsed_json.get("device_type", "N/A")
         condition = parsed_json.get("condition", "N/A")
         reason = parsed_json.get("reason", "N/A")
@@ -80,6 +66,13 @@ def analyze_device_condition(video_file_path):
         error_message = f"An error occurred: {e}"
         print(f"ERROR: {error_message}")
         return error_message, "", ""
+    
+    finally:
+        # 6. --- CLEANUP: Delete the file from Google's servers ---
+        if uploaded_file:
+            print(f"Log: Deleting uploaded file: {uploaded_file.name}")
+            client.files.delete(name=uploaded_file.name)
+
 
 # --- Gradio Interface ---
 with gr.Blocks(theme=gr.themes.Soft()) as demo: