Update app.py

app.py

@@ -7,7 +7,6 @@ import numpy as np
 # Configure Gemini API
 GEMINI_API_KEY =  os.environ["GEMINI_API_KEY"]
 genai.configure(api_key=GEMINI_API_KEY)
-
 # Create the model with the same configuration as the sample
 generation_config = {
     "temperature": 0.2,
@@ -48,53 +47,11 @@ model = genai.GenerativeModel(
     system_instruction=SYSTEM_INSTRUCTION,
 )
 
-def create_quality_plot(quality, score):
-    """Create a visualization of the fish quality score"""
-    plt.figure(figsize=(10, 4))
-    
-    # Create bar chart
-    score = float(score)  # Convert score to float
-    bar_color = 'red' if score <= 4 else ('yellow' if score <= 7 else 'green')
-    
-    plt.bar(['Quality Score'], [score], color=bar_color)
-    plt.axhline(y=10, color='gray', linestyle='--', alpha=0.5)
-    
-    # Customize plot
-    plt.ylim(0, 11)
-    plt.title(f'Fish Quality Assessment: {quality}', pad=20)
-    plt.ylabel('Score (0-10)')
-    
-    # Add quality zones
-    plt.axhspan(0, 4, alpha=0.1, color='red', label='Bad (0-4)')
-    plt.axhspan(4, 7, alpha=0.1, color='yellow', label='Average (4-7)')
-    plt.axhspan(7, 10, alpha=0.1, color='green', label='Good (7-10)')
-    
-    # Add legend
-    plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')
-    
-    # Save plot to a temporary file
-    plt.tight_layout()
-    temp_plot = "temp_plot.png"
-    plt.savefig(temp_plot, bbox_inches='tight', dpi=300)
-    plt.close()
-    
-    return temp_plot
-
-def parse_response(response_text):
-    """Parse the model's response to extract quality and score"""
-    try:
-        lines = response_text.strip().split('\n')
-        quality = lines[0].split(': ')[1].strip()
-        score = lines[1].split(': ')[1].strip().strip('()')
-        return quality, score
-    except:
-        return "Bad", "0"
-
 def analyze_fish_quality(image):
     """Analyze the fish quality using Gemini model"""
     try:
         if image is None:
-            return "Please upload an image to analyze.", None
+            return "Please upload an image to analyze."
             
         # Start a new chat session
         chat = model.start_chat()
@@ -108,13 +65,9 @@ def analyze_fish_quality(image):
             "Now provided the image of a fish, give me two output. Dont generate any more extra text other then the following.\n1. Fish Quality: Good/Average/Bad\n2. Quality Score (0-10): "
         ])
         
-        # Parse response and create visualization
-        quality, score = parse_response(response.text)
-        plot_path = create_quality_plot(quality, score)
-        
-        return response.text, plot_path
+        return response.text
     except Exception as e:
-        return f"Error analyzing image: {str(e)}\nPlease make sure you've uploaded a valid image file.", None
+        return f"Error analyzing image: {str(e)}\nPlease make sure you've uploaded a valid image file."
 
 # Get example images from the examples directory
 examples_dir = os.path.join(os.path.dirname(__file__), "examples")
@@ -130,20 +83,13 @@ if os.path.exists(examples_dir):
 iface = gr.Interface(
     fn=analyze_fish_quality,
     inputs=gr.Image(type="filepath", label="Upload Fish Image"),
-    outputs=[
-        gr.Textbox(label="Quality Analysis Result", lines=4),
-        gr.Image(label="Quality Score Visualization")
-    ],
+    outputs=gr.Textbox(label="Quality Analysis Result", lines=4),
     title="Fish Quality Analyzer",
     description="""Upload an image of a fish to analyze its quality. The system will evaluate:
     - Overall quality (Good/Average/Bad)
     - Quality score (0-10)
     
-    The analysis is based on various factors including eyes, gills, skin appearance, color, and overall condition.
-    The visualization shows the quality score with color-coded zones:
-    - Green: Good (7-10)
-    - Yellow: Average (4-7)
-    - Red: Bad (0-4)""",
+    The analysis is based on various factors including eyes, gills, skin appearance, color, and overall condition.""",
     examples=example_images,
     cache_examples=True
 )