Update app.py

app.py

@@ -1,31 +1,36 @@
 import gradio as gr
-
 from azure.ai.inference import ChatCompletionsClient
 from azure.ai.inference.models import (
     SystemMessage,
+    UserMessage,
+    TextContentItem,
+    ImageContentItem,
+    ImageUrl,
     ImageDetailLevel,
 )
 from azure.core.credentials import AzureKeyCredential
+from gtts import gTTS
+from deep_translator import GoogleTranslator
+import os
 
+# ✅ Securely load Azure credentials from environment
+token = os.getenv("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")
+endpoint = os.getenv("https://models.inference.ai.azure.com")
+model_name = os.getenv("AZURE_MODEL_NAME", "gpt-4o")  # Optional: use secret or default to gpt-4o
 
+# ✅ Validate credentials
+if not (isinstance(token, str) and token.strip()) or not (isinstance(endpoint, str) and endpoint.strip()):
+    raise ValueError("Azure API credentials are missing. Please set AZURE_API_KEY and AZURE_ENDPOINT in Hugging Face secrets.")
 
-
-# Azure API credentials
-token = "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"
-endpoint = "https://models.inference.ai.azure.com"
-model_name = "gpt-4o"
-
-# Initialize the ChatCompletionsClient
+# ✅ Azure Client
 client = ChatCompletionsClient(
+    endpoint=endpoint,
     credential=AzureKeyCredential(token),
 )
 
-# Define the function to handle the image and get predictions
+# 🔍 Analyze disease
 def analyze_leaf_disease(image_path, leaf_type):
-
-
     try:
-        # Prepare and send the request to the Azure API
         response = client.complete(
             messages=[
                 SystemMessage(
@@ -37,44 +42,67 @@ def analyze_leaf_disease(image_path, leaf_type):
                         ImageContentItem(
                             image_url=ImageUrl.load(
                                 image_file=image_path,
+                                image_format="jpg",
+                                detail=ImageDetailLevel.LOW,
+                            )
+                        ),
+                    ],
+                ),
+            ],
+            model=model_name,
+        )
+        return response.choices[0].message.content
+    except Exception as e:
+        return f"❌ Error: {e}"
+
+# 🌐 Translate to Bangla
+def translate_to_bangla(text):
+    try:
+        return GoogleTranslator(source="auto", target="bn").translate(text)
     except Exception as e:
-        return f"An error occurred: {e}"
+        return f"❌ Translation error: {e}"
 
-# Define the Gradio interface
+# 🔊 Text to Speech
+def text_to_speech(text):
+    try:
+        tts = gTTS(text)
+        audio_file = "tts_output.mp3"
+        tts.save(audio_file)
+        return audio_file
+    except Exception as e:
+        return f"❌ TTS error: {e}"
+
+# 🚀 Main Action
 def handle_proceed(image_path, leaf_type):
-    # Display detecting status
-    detecting_status = "Detecting..."
-    result = analyze_leaf_disease(image_path, leaf_type)
-    # Clear detecting status after processing
-    return "", result
+    return "", analyze_leaf_disease(image_path, leaf_type)
 
+# 🌿 Gradio App
 with gr.Blocks() as interface:
-    with gr.Row():
-        gr.Markdown("""
-        # Leaf Disease Detector
-        Upload a leaf image, select the leaf type, and let the AI analyze the disease.
-        """)
+    gr.Markdown("# 🍃 Leaf Disease Detector\nUpload an image, select the leaf type, and analyze the disease. Listen or translate the result.")
 
     with gr.Row():
-        image_input = gr.Image(type="filepath", label="Upload an Image or Take a Photo")
+        image_input = gr.Image(type="filepath", label="📸 Upload Leaf Image")
         leaf_type = gr.Dropdown(
             choices=["Tomato", "Tobacco", "Corn", "Paddy", "Maze", "Potato", "Wheat"],
-            label="Select Leaf Type",
+            label="🌿 Select Leaf Type",
         )
-        proceed_button = gr.Button("Proceed")
-
-
-
-
-
-
+        proceed_button = gr.Button("🔍 Analyze")
 
     with gr.Row():
         detecting_label = gr.Label("Detecting...", visible=False)
-        output_box = gr.Textbox(label="Results", placeholder="Results will appear here.")
+        output_box = gr.Textbox(label="📋 Result", placeholder="Analysis will appear here", lines=10)
+
+    with gr.Row():
+        tts_button = gr.Button("🔊 Read Aloud")
+        tts_audio = gr.Audio(label="🎧 Audio", autoplay=True)
+
+        translate_button = gr.Button("🌐 Translate to Bangla")
+        translated_output = gr.Textbox(label="📘 Bangla Translation", placeholder="Translation will appear here", lines=10)
 
-    # Update the detecting_label and result in outputs
+    # Button logic
     proceed_button.click(handle_proceed, inputs=[image_input, leaf_type], outputs=[detecting_label, output_box])
+    tts_button.click(text_to_speech, inputs=[output_box], outputs=[tts_audio])
+    translate_button.click(translate_to_bangla, inputs=[output_box], outputs=[translated_output])
 
 if __name__ == "__main__":
-    interface.launch()
+    interface.launch()