Update app.py

app.py

@@ -2,28 +2,26 @@ import gradio as gr
 import google.generativeai as genai
 import os
 import time
-import json # We need this library to parse the JSON output
+import json
 from dotenv import load_dotenv
 
 # --- Configuration ---
 load_dotenv()
 
+# This is the correct way to initialize for the 'google-genai' library
 try:
     genai.configure(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.")
 
-# --- Core Function with JSON Parsing ---
+# --- Core Function ---
 
 def analyze_device_condition(video_file_path):
-    """
-    Analyzes a video and returns structured JSON data with three fields.
-    """
     if not video_file_path:
-        return "Please upload video", "", "" # Return empty strings for the other fields
+        return "Please upload video", "", ""
 
     try:
-        # 1. Upload the file (same as before)
+        # 1. This library has a simple helper to upload and process the file
         print("Log: Uploading file to Google...")
         video_file = genai.upload_file(path=video_file_path)
 
@@ -33,89 +31,60 @@ def analyze_device_condition(video_file_path):
             video_file = genai.get_file(video_file.name)
 
         if video_file.state.name == "FAILED":
-            error_message = "Error: Video processing failed."
-            return error_message, "", ""
+            raise gr.Error("Video processing failed. The file might be corrupted or in an unsupported format.")
             
         print(f"Log: File processed successfully.")
 
-        # 2. ** NEW: Update the prompt to request JSON output **
+        # 2. Prompt for JSON output
         prompt = """
-        Analyze the provided video of a device. Respond ONLY with a valid JSON object.
-        The JSON object must have the following three keys and nothing else:
-        1. "device_type": A short string identifying the device (e.g., "iPhone 14 Pro", "Washing Machine", "Laptop").
-        2. "condition": A single word describing its condition. Choose from: "Mint", "Excellent", "Good", "Fair", "Poor".
-        3. "reason": A brief string explaining the condition rating, mentioning specific defects like "minor screen scratches", "dents on corner", or "clean".
-
-        Example JSON output:
-        {
-          "device_type": "Samsung Galaxy S22",
-          "condition": "Fair",
-          "reason": "Visible cracks on the screen and scratches on the back panel."
-        }
+        Analyze the provided video. Respond ONLY with a valid JSON object with three keys:
+        1. "device_type": A short string identifying the device.
+        2. "condition": A single word: "Mint", "Excellent", "Good", "Fair", or "Poor".
+        3. "reason": A brief string explaining the condition.
         """
 
-        # 3. Call the Gemini Model
-        model = genai.GenerativeModel(model_name="gemini-1.5-pro-latest")
+        # 3. Call the Gemini Model using the simpler syntax
+        # Using a reliable flash model
+        model = genai.GenerativeModel(model_name="gemini-1.5-flash-latest")
         
         print("Log: Sending prompt and video to Gemini...")
         response = model.generate_content(
             [prompt, video_file],
-            # Ask the model specifically for a JSON response
-            generation_config=genai.types.GenerationConfig(response_mime_type="application/json")
+            generation_config=genai.types.GenerationConfig(
+                response_mime_type="application/json",
+                temperature=0.2
+            )
         )
         
         print("Log: Analysis received from Gemini.")
         genai.delete_file(video_file.name)
-        print(f"Log: Uploaded file deleted.")
 
-        # 4. ** NEW: Parse the JSON response **
-        print(f"Raw model response: {response.text}")
+        # 4. Parse the 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")
 
-        # The function now returns three separate values
         return device_type, condition, reason
 
     except Exception as e:
         print(f"!!!!!!!! AN ERROR OCCURRED !!!!!!!!\n{e}")
         error_message = f"An error occurred: {e}"
-        # Return the error message in the first field and empty strings for the others
         return error_message, "", ""
 
-
-# --- ** NEW: Gradio Interface with Multiple Output Fields ** ---
-
+# --- Gradio Interface ---
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown(
-        """
-        # 📱 Device Condition Analyzer
-        Upload or record a short video of an electronic device to get a structured analysis of its condition.
-        """
-    )
-    
-    video_input = gr.Video(
-        label="Upload or Record Video", 
-        sources=["upload", "webcam"],
-        format="mp4"
-    )
-    
+    gr.Markdown("# 📱 Device Condition Analyzer")
+    video_input = gr.Video(label="Upload or Record Video", sources=["upload", "webcam"], format="mp4")
     submit_button = gr.Button("Analyze Device", variant="primary")
-    
-    # Create a row layout for the output fields
     with gr.Row():
-        # Create three separate Textbox outputs
         device_type_output = gr.Textbox(label="Device Type")
         condition_output = gr.Textbox(label="Condition")
         reason_output = gr.Textbox(label="Reason / Details")
 
-    # The click function now maps to three outputs instead of one
     submit_button.click(
         fn=analyze_device_condition,
         inputs=video_input,
-        # The list of outputs must match the order of the return values in the function
         outputs=[device_type_output, condition_output, reason_output],
         show_progress='full'
     )