Update app.py

app.py

@@ -13,44 +13,38 @@ load_dotenv()
 # ===============================
 # AZURE CONFIG
 # ===============================
-AZURE_API_KEY = os.getenv("AZURE_OPENAI_API_KEY")
-AZURE_ENDPOINT = os.getenv("AZURE_OPENAI_ENDPOINT")
-AZURE_DEPLOYMENT = os.getenv("AZURE_OPENAI_DEPLOYMENT")
-AZURE_API_VERSION = os.getenv(
-    "AZURE_OPENAI_API_VERSION",
-    "2024-02-15-preview"
-)
-
 client = AzureOpenAI(
-    api_key=AZURE_API_KEY,
-    api_version=AZURE_API_VERSION,
-    azure_endpoint=AZURE_ENDPOINT
+    api_key=os.getenv("AZURE_OPENAI_API_KEY"),
+    api_version=os.getenv(
+        "AZURE_OPENAI_API_VERSION",
+        "2024-02-15-preview"
+    ),
+    azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT")
 )
 
+AZURE_DEPLOYMENT = os.getenv("AZURE_OPENAI_DEPLOYMENT")
+
 # ===============================
-# MEMORY
+# IMAGE CACHE ONLY
 # ===============================
-last_crop_result = None
 crop_cache = {}
 
 
-def get_image_hash(image_bytes: bytes):
+def get_hash(image_bytes):
     return hashlib.md5(image_bytes).hexdigest()
 
 
 # ===============================
-# IMAGE IDENTIFICATION
+# IDENTIFY CROP
 # ===============================
-def identify_crop(image_file):
-    global last_crop_result
+def identify_crop(image_file, crop_state):
 
     if image_file is None:
-        return "❌ Please upload a crop image."
+        return "❌ Please upload a crop image.", crop_state
 
     try:
         img = Image.open(image_file)
 
-        # Resize large images
         if img.width > 1000 or img.height > 1000:
             img.thumbnail((1000, 1000))
 
@@ -61,80 +55,78 @@ def identify_crop(image_file):
         img.save(buffer, format="JPEG", quality=85)
 
         image_bytes = buffer.getvalue()
-        image_base64 = base64.b64encode(image_bytes).decode()
+        image_hash = get_hash(image_bytes)
 
-        image_hash = get_image_hash(image_bytes)
-
-        # ✅ IMAGE CACHE
+        # ✅ cache
         if image_hash in crop_cache:
-            last_crop_result = crop_cache[image_hash]
-            return f"🌾 Cached Crop Result:\n\n{last_crop_result}"
+            result = crop_cache[image_hash]
+            return f"🌾 Cached Crop Result:\n\n{result}", result
+
+        image_base64 = base64.b64encode(image_bytes).decode()
 
-        # Azure Vision Call
         response = client.chat.completions.create(
             model=AZURE_DEPLOYMENT,
             messages=[
                 {
                     "role": "system",
-                    "content": "You are an expert agricultural scientist."
+                    "content":
+                    "You are an expert agricultural scientist."
                 },
                 {
                     "role": "user",
                     "content": [
                         {
                             "type": "text",
-                            "text": "Identify this crop and describe briefly."
+                            "text":
+                            "Identify this crop briefly."
                         },
                         {
                             "type": "image_url",
                             "image_url": {
-                                "url": f"data:image/jpeg;base64,{image_base64}"
+                                "url":
+                                f"data:image/jpeg;base64,{image_base64}"
                             },
                         },
                     ],
                 },
             ],
-            max_tokens=400,
+            max_tokens=300,
         )
 
         result = response.choices[0].message.content
-
         crop_cache[image_hash] = result
-        last_crop_result = result
 
-        return f"🌾 Crop Identification:\n\n{result}"
+        # ✅ SAVE ONLY IN SESSION
+        return f"🌾 Crop Identification:\n\n{result}", result
 
     except Exception:
-        return f"❌ Error:\n{traceback.format_exc()}"
+        return traceback.format_exc(), crop_state
 
 
 # ===============================
-# CHATBOT (NO CACHE ✅)
+# CHATBOT
 # ===============================
-def ask_chatbot(user_message):
-    global last_crop_result
+def ask_chatbot(message, crop_state):
 
-    if last_crop_result is None:
+    if not crop_state:
         return "⚠️ Please upload and identify a crop image first."
 
-    context = f"\nIdentified Crop Info:\n{last_crop_result}\n"
+    context = f"\nCrop Info:\n{crop_state}\n"
 
     response = client.chat.completions.create(
         model=AZURE_DEPLOYMENT,
         messages=[
             {
                 "role": "system",
-                "content": (
-                    "You are an agricultural advisor. "
-                    "Answer using simple farmer-friendly language."
-                ),
+                "content":
+                "You are a farming advisor. Give direct practical answers."
             },
             {
                 "role": "user",
-                "content": context + user_message
-            },
+                "content": context + message
+            }
         ],
-        max_tokens=500,
+        max_tokens=400,
     )
 
     return response.choices[0].message.content
@@ -143,31 +135,35 @@ def ask_chatbot(user_message):
 # ===============================
 # CHAT UI
 # ===============================
-def chat_ui(message, history):
+def chat_ui(message, history, crop_state):
 
     if history is None:
         history = []
 
     if not message:
-        return history, ""
+        return history, "", crop_state
 
-    reply = ask_chatbot(message)
+    reply = ask_chatbot(message, crop_state)
 
     history.append([message, reply])
 
-    return history, ""
+    return history, "", crop_state
 
 
 # ===============================
-# GRADIO UI
+# UI
 # ===============================
 with gr.Blocks(title="Crop Prediction") as demo:
 
-    gr.Markdown("# 🌾 Smart Crop Identification & Farming Assistant")
+    gr.Markdown(
+        "# 🌾 Smart Crop Identification & Farming Assistant"
+    )
+
+    # ✅ SESSION MEMORY
+    crop_state = gr.State(None)
 
     with gr.Row():
 
-        # LEFT SIDE
         with gr.Column():
             image_input = gr.Image(
                 type="filepath",
@@ -181,36 +177,32 @@ with gr.Blocks(title="Crop Prediction") as demo:
                 label="Result"
             )
 
-        # RIGHT SIDE
         with gr.Column():
             chatbot = gr.Chatbot(height=400)
             msg = gr.Textbox(
-                placeholder="Ask about soil, water, disease..."
+                placeholder="Ask about soil, disease..."
             )
             send = gr.Button("Send")
 
     identify_btn.click(
         identify_crop,
-        image_input,
-        image_output
+        [image_input, crop_state],
+        [image_output, crop_state]
     )
 
     send.click(
         chat_ui,
-        [msg, chatbot],
-        [chatbot, msg]
+        [msg, chatbot, crop_state],
+        [chatbot, msg, crop_state]
     )
 
     msg.submit(
         chat_ui,
-        [msg, chatbot],
-        [chatbot, msg]
+        [msg, chatbot, crop_state],
+        [chatbot, msg, crop_state]
     )
 
 
-# ===============================
-# LAUNCH
-# ===============================
 if __name__ == "__main__":
     demo.launch(
         server_name="0.0.0.0",