Create app.py

app.py

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+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("AZURE_API_KEY")
+endpoint = os.getenv("AZURE_ENDPOINT")
+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 Client
+client = ChatCompletionsClient(
+    endpoint=endpoint,
+    credential=AzureKeyCredential(token),
+)
+
+# 🔍 Analyze disease
+def analyze_leaf_disease(image_path, leaf_type):
+    try:
+        response = client.complete(
+            messages=[
+                SystemMessage(
+                    content=f"You are a subject matter expert that describes leaf disease in detail for {leaf_type} leaves."
+                ),
+                UserMessage(
+                    content=[
+                        TextContentItem(text="What's the name of the leaf disease in this image and what is the confidence score? What is the probable reason? What are the medicine or stops to prevent the disease"),
+                        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"❌ Translation error: {e}"
+
+# 🔊 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):
+    return "", analyze_leaf_disease(image_path, leaf_type)
+
+# 🌿 Gradio App
+with gr.Blocks() as interface:
+    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 Leaf Image")
+        leaf_type = gr.Dropdown(
+            choices=["Tomato", "Tobacco", "Corn", "Paddy", "Maze", "Potato", "Wheat"],
+            label="🌿 Select Leaf Type",
+        )
+        proceed_button = gr.Button("🔍 Analyze")
+
+    with gr.Row():
+        detecting_label = gr.Label("Detecting...", visible=False)
+        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)
+
+    # 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()