Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,185 +1,184 @@
-import streamlit as st
-from inference_sdk import InferenceHTTPClient
-import requests
-from PIL import Image
-import tempfile
-import os
-import cv2
-import numpy as np
-
-# -----------------------------
-# PAGE CONFIG
-# -----------------------------
-st.set_page_config(page_title="Cassava Disease Detection", layout="centered")
-
-# -----------------------------
-# CONFIGURATION
-# -----------------------------
-ROBOFLOW_API_KEY = st.secrets["ROBOFLOW_API_KEY"]
-OPENROUTER_API_KEY = st.secrets["OPENROUTER_API_KEY"]
-
-MODEL_ID = "cassavadisease/1"
-ROBOFLOW_API_URL = "https://serverless.roboflow.com"
-
-# -----------------------------
-# INITIALIZE ROBOFLOW CLIENT
-# -----------------------------
-CLIENT = InferenceHTTPClient(
-    api_url=ROBOFLOW_API_URL,
-    api_key=ROBOFLOW_API_KEY
-)
-
-# -----------------------------
-# FUNCTION: AI EXPLANATION
-# -----------------------------
-def get_ai_explanation(disease_name):
-
-    prompt = f"""
-    Explain briefly the cassava disease: {disease_name}.
-    Include:
-    - Cause
-    - Main Symptoms
-    - Prevention
-    - Treatment
-    Keep answer short.
-    """
-
-    response = requests.post(
-        "https://openrouter.ai/api/v1/chat/completions",
-        headers={
-            "Authorization": f"Bearer {OPENROUTER_API_KEY}",
-            "Content-Type": "application/json",
-            "HTTP-Referer": "http://localhost:8501",
-            "X-Title": "Cassava Disease Detection App"
-        },
-        json={
-            "model": "minimax/minimax-m2.5",
-            "messages": [
-                {"role": "user", "content": prompt}
-            ],
-            "max_tokens": 800,
-            "temperature": 0.3
-        }
-    )
-
-    if response.status_code != 200:
-        return f"OpenRouter API Error:\n{response.text}"
-
-    result = response.json()
-
-    if "choices" not in result:
-        return f"Unexpected API Response:\n{result}"
-
-    return result["choices"][0]["message"]["content"]
-
-
-# -----------------------------
-# UI
-# -----------------------------
-st.title("Cassava Disease Detection Web App")
-st.write("Upload or capture a cassava leaf image for disease detection.")
-
-source = st.radio("Select Image Source:", ["Upload Image", "Use Camera"])
-
-image = None
-
-if source == "Upload Image":
-    uploaded_file = st.file_uploader("Upload Image", type=["jpg", "jpeg", "png"])
-    if uploaded_file:
-        image = Image.open(uploaded_file)
-
-elif source == "Use Camera":
-    camera_photo = st.camera_input("Take a picture of the cassava leaf")
-    if camera_photo:
-        image = Image.open(camera_photo)
-
-# -----------------------------
-# MAIN PROCESS
-# -----------------------------
-if image is not None:
-
-    st.image(image, caption="Captured Image", use_container_width=True)
-
-    # Save temp image
-    with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as tmp:
-        image.save(tmp.name)
-        temp_path = tmp.name
-
-    # Roboflow inference
-    with st.spinner("Analyzing image..."):
-        result = CLIENT.infer(temp_path, model_id=MODEL_ID)
-
-    os.remove(temp_path)
-
-    predictions = result.get("predictions", [])
-
-    if predictions:
-
-        img_cv = np.array(image)
-        img_cv = cv2.cvtColor(img_cv, cv2.COLOR_RGB2BGR)
-
-        for pred in predictions:
-
-            x = pred["x"]
-            y = pred["y"]
-            w = pred["width"]
-            h = pred["height"]
-            label = pred["class"]
-            confidence = round(pred["confidence"] * 100, 2)
-
-            # Convert center to corner format
-            x1 = int(x - w / 2)
-            y1 = int(y - h / 2)
-            x2 = int(x + w / 2)
-            y2 = int(y + h / 2)
-
-            # Draw bounding box
-            cv2.rectangle(img_cv, (x1, y1), (x2, y2), (0, 255, 0), 2)
-
-            # Label background
-            cv2.rectangle(img_cv, (x1, y1 - 30), (x1 + 250, y1), (0, 255, 0), -1)
-
-            # Label text
-            cv2.putText(
-                img_cv,
-                f"{label} ({confidence}%)",
-                (x1 + 5, y1 - 10),
-                cv2.FONT_HERSHEY_SIMPLEX,
-                0.6,
-                (0, 0, 0),
-                2
-            )
-
-        # Convert back to RGB
-        img_display = cv2.cvtColor(img_cv, cv2.COLOR_BGR2RGB)
-        st.image(img_display, caption="Detected & Labeled Image", use_container_width=True)
-
-        # Get highest confidence prediction
-        top_prediction = max(predictions, key=lambda x: x["confidence"])
-        disease_name = top_prediction["class"]
-        confidence = round(top_prediction["confidence"] * 100, 2)
-
-        st.success(f"Detected: **{disease_name}**")
-        st.info(f"Confidence: {confidence}%")
-
-        # AI Explanation
-        with st.spinner("Generating disease explanation..."):
-            explanation = get_ai_explanation(disease_name)
-
-        st.markdown("## 📘 Disease Information")
-        st.write(explanation)
-
-    else:
-        st.warning("No cassava leaf detected.")
-
-
-# -----------------------------
-# FOOTER
-# -----------------------------
-st.markdown("---")
-st.markdown(
-    "<div style='text-align: center; font-size: 14px;'>"
-    "Developed by <b>Edcel Bogay</b>"
-    "</div>",
-    unsafe_allow_html=True
+import streamlit as st
+import requests
+from PIL import Image
+import tempfile
+import os
+import cv2
+import numpy as np
+
+# -----------------------------
+# PAGE CONFIG
+# -----------------------------
+st.set_page_config(page_title="Cassava Disease Detection", layout="centered")
+
+# -----------------------------
+# CONFIGURATION
+# -----------------------------
+ROBOFLOW_API_KEY = st.secrets["ROBOFLOW_API_KEY"]
+OPENROUTER_API_KEY = st.secrets["OPENROUTER_API_KEY"]
+
+MODEL_ID = "cassavadisease/1"
+ROBOFLOW_API_URL = "https://serverless.roboflow.com"
+
+# -----------------------------
+# INITIALIZE ROBOFLOW CLIENT
+# -----------------------------
+CLIENT = InferenceHTTPClient(
+    api_url=ROBOFLOW_API_URL,
+    api_key=ROBOFLOW_API_KEY
+)
+
+# -----------------------------
+# FUNCTION: AI EXPLANATION
+# -----------------------------
+def get_ai_explanation(disease_name):
+
+    prompt = f"""
+    Explain briefly the cassava disease: {disease_name}.
+    Include:
+    - Cause
+    - Main Symptoms
+    - Prevention
+    - Treatment
+    Keep answer short.
+    """
+
+    response = requests.post(
+        "https://openrouter.ai/api/v1/chat/completions",
+        headers={
+            "Authorization": f"Bearer {OPENROUTER_API_KEY}",
+            "Content-Type": "application/json",
+            "HTTP-Referer": "http://localhost:8501",
+            "X-Title": "Cassava Disease Detection App"
+        },
+        json={
+            "model": "minimax/minimax-m2.5",
+            "messages": [
+                {"role": "user", "content": prompt}
+            ],
+            "max_tokens": 800,
+            "temperature": 0.3
+        }
+    )
+
+    if response.status_code != 200:
+        return f"OpenRouter API Error:\n{response.text}"
+
+    result = response.json()
+
+    if "choices" not in result:
+        return f"Unexpected API Response:\n{result}"
+
+    return result["choices"][0]["message"]["content"]
+
+
+# -----------------------------
+# UI
+# -----------------------------
+st.title("Cassava Disease Detection Web App")
+st.write("Upload or capture a cassava leaf image for disease detection.")
+
+source = st.radio("Select Image Source:", ["Upload Image", "Use Camera"])
+
+image = None
+
+if source == "Upload Image":
+    uploaded_file = st.file_uploader("Upload Image", type=["jpg", "jpeg", "png"])
+    if uploaded_file:
+        image = Image.open(uploaded_file)
+
+elif source == "Use Camera":
+    camera_photo = st.camera_input("Take a picture of the cassava leaf")
+    if camera_photo:
+        image = Image.open(camera_photo)
+
+# -----------------------------
+# MAIN PROCESS
+# -----------------------------
+if image is not None:
+
+    st.image(image, caption="Captured Image", use_container_width=True)
+
+    # Save temp image
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as tmp:
+        image.save(tmp.name)
+        temp_path = tmp.name
+
+    # Roboflow inference
+    with st.spinner("Analyzing image..."):
+        result = CLIENT.infer(temp_path, model_id=MODEL_ID)
+
+    os.remove(temp_path)
+
+    predictions = result.get("predictions", [])
+
+    if predictions:
+
+        img_cv = np.array(image)
+        img_cv = cv2.cvtColor(img_cv, cv2.COLOR_RGB2BGR)
+
+        for pred in predictions:
+
+            x = pred["x"]
+            y = pred["y"]
+            w = pred["width"]
+            h = pred["height"]
+            label = pred["class"]
+            confidence = round(pred["confidence"] * 100, 2)
+
+            # Convert center to corner format
+            x1 = int(x - w / 2)
+            y1 = int(y - h / 2)
+            x2 = int(x + w / 2)
+            y2 = int(y + h / 2)
+
+            # Draw bounding box
+            cv2.rectangle(img_cv, (x1, y1), (x2, y2), (0, 255, 0), 2)
+
+            # Label background
+            cv2.rectangle(img_cv, (x1, y1 - 30), (x1 + 250, y1), (0, 255, 0), -1)
+
+            # Label text
+            cv2.putText(
+                img_cv,
+                f"{label} ({confidence}%)",
+                (x1 + 5, y1 - 10),
+                cv2.FONT_HERSHEY_SIMPLEX,
+                0.6,
+                (0, 0, 0),
+                2
+            )
+
+        # Convert back to RGB
+        img_display = cv2.cvtColor(img_cv, cv2.COLOR_BGR2RGB)
+        st.image(img_display, caption="Detected & Labeled Image", use_container_width=True)
+
+        # Get highest confidence prediction
+        top_prediction = max(predictions, key=lambda x: x["confidence"])
+        disease_name = top_prediction["class"]
+        confidence = round(top_prediction["confidence"] * 100, 2)
+
+        st.success(f"Detected: **{disease_name}**")
+        st.info(f"Confidence: {confidence}%")
+
+        # AI Explanation
+        with st.spinner("Generating disease explanation..."):
+            explanation = get_ai_explanation(disease_name)
+
+        st.markdown("## 📘 Disease Information")
+        st.write(explanation)
+
+    else:
+        st.warning("No cassava leaf detected.")
+
+
+# -----------------------------
+# FOOTER
+# -----------------------------
+st.markdown("---")
+st.markdown(
+    "<div style='text-align: center; font-size: 14px;'>"
+    "Developed by <b>Edcel Bogay</b>"
+    "</div>",
+    unsafe_allow_html=True
 )