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app.py

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+# ===================== IMPORTS =====================
+import streamlit as st
+import os
+import json
+import difflib
+import pickle
+import torch
+import numpy as np
+from PIL import Image
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.image import img_to_array, load_img
+from transformers import CLIPProcessor, CLIPModel
+from sentence_transformers import SentenceTransformer, CrossEncoder
+from langdetect import detect
+
+# ===================== PATHS =====================
+model_path = os.path.join(Main_py, "best_cnn_model_finetuned.keras")
+label_path = os.path.join(Main_py, "label_encoder.pkl")
+json_path = os.path.join(Main_py, "banana_disease_knowledge_base_updated_shuffled.json")
+
+# ===================== LOAD MODELS & DATA =====================
+@st.cache_resource
+def load_cnn_clip_kb():
+    model = load_model(model_path)
+    with open(label_path, "rb") as f:
+        le = pickle.load(f)
+    with open(json_path, "r", encoding="utf-8") as f:
+        kb_data = json.load(f)
+    clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
+    clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
+    return model, le, kb_data, clip_model, clip_processor
+
+@st.cache_resource
+def load_nlp_models():
+    embedder = SentenceTransformer("sentence-transformers/paraphrase-xlm-r-multilingual-v1")
+    cross_encoder = CrossEncoder("cross-encoder/mmarco-mMiniLMv2-L12-H384-v1")
+    return embedder, cross_encoder
+
+model, le, kb_data, clip_model, clip_processor = load_cnn_clip_kb()
+embedder, cross_encoder = load_nlp_models()
+
+# ===================== CLIP FILTER =====================
+def verify_image_with_clip(image_path):
+    prompts = ["a photo of a banana leaf", "a photo of something that is not a banana leaf"]
+    try:
+        image = Image.open(image_path).convert("RGB")
+    except Exception as e:
+        return ('REJECTED', f'Invalid image file: {e}', 0.0)
+
+    inputs = clip_processor(text=prompts, images=image, return_tensors="pt", padding=True)
+    with torch.no_grad():
+        outputs = clip_model(**inputs)
+        probs = outputs.logits_per_image.softmax(dim=1).cpu().numpy()[0]
+
+    banana_score, not_banana_score = probs[0], probs[1]
+    rejection_factor = 3.0
+
+    if banana_score >= not_banana_score * rejection_factor:
+        return ('ACCEPTED', 'banana leaf', banana_score)
+    else:
+        return ('REJECTED', 'Not a banana leaf', not_banana_score)
+
+# ===================== CNN PREDICTION =====================
+def predict_disease(image_path, target_size=(224, 224)):
+    image = load_img(image_path, target_size=target_size)
+    img_array = img_to_array(image) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+    preds = model.predict(img_array)[0]
+    idx = np.argmax(preds)
+    label = le.inverse_transform([idx])[0]
+    confidence = preds[idx]
+    return label, confidence, image
+
+# ===================== FUZZY MARATHI OUTPUT =====================
+def match_disease_name_fuzzy(predicted_name):
+    disease_names = [entry["Disease"].strip().lower() for entry in kb_data]
+    matches = difflib.get_close_matches(predicted_name.strip().lower(), disease_names, n=1, cutoff=0.5)
+    if matches:
+        for entry in kb_data:
+            if entry["Disease"].strip().lower() == matches[0]:
+                return entry
+    return None
+
+def get_marathi_recommendation_fuzzy(predicted_disease, confidence=None):
+    entry = match_disease_name_fuzzy(predicted_disease)
+    if entry:
+        return {
+            "पिक": entry.get("Crop", "केळी"),
+            "रोग": entry.get("Local_Name", {}).get("mr", predicted_disease),
+            "लक्षणे": entry.get("Symptoms_MR", ""),
+            "कारण": entry.get("Cause_MR", ""),
+            "किटकनाशके": entry.get("Pesticide_MR", ""),
+            "किटकनाशक शिफारस": entry.get("Pesticide_Recommendation", {}).get("mr", ""),
+            "नियंत्रण पद्धती": entry.get("Management_MR", ""),
+            "रोगजन्य घटक": entry.get("Pathogen", ""),
+            "विश्वासार्हता": f"{confidence:.2%}" if confidence else "N/A"
+        }
+    return None
+
+# ===================== NLP PREDICTION =====================
+def detect_language(query: str) -> str:
+    try:
+        lang = detect(query)
+        return lang if lang in ["mr", "hi"] else "en"
+    except:
+        return "en"
+
+def predict_disease_from_text(query: str):
+    lang = detect_language(query)
+    query_emb = embedder.encode([query], normalize_embeddings=True)
+
+    symptom_key = f"Symptoms_{lang.upper()}" if lang != "en" else "Symptoms"
+    pairs = [[query, entry.get(symptom_key, "")] for entry in kb_data]
+    scores = cross_encoder.predict(pairs)
+    best_idx = np.argmax(scores)
+
+    if scores[best_idx] < 0.2:
+        return {
+            "message": {
+                "mr": "हा रोग आमच्या डेटा���ेसमध्ये नाही.",
+                "hi": "यह रोग हमारे डेटाबेस में नहीं है।",
+                "en": "This disease is not in our database."
+            }[lang]
+        }
+
+    entry = kb_data[best_idx]
+    return {
+        "पिक": entry.get("Crop", "केळी"),
+        "रोग": entry["Local_Name"].get(lang, entry["Disease"]),
+        "लक्षणे": entry.get(symptom_key, ""),
+        "कारण": entry.get(f"Cause_{lang.upper()}", entry.get("Cause", "")),
+        "किटकनाशक शिफारस": entry.get("Pesticide_Recommendation", {}).get(lang, ""),
+        "किटकनाशके": entry.get("Pesticide", ""),
+        "रोगजन्य घटक": entry.get("Pathogen", ""),
+        "नियंत्रण पद्धती": entry.get(f"Management_{lang.upper()}", entry.get("Management_Practices", "")),
+    }
+
+# ===================== STREAMLIT UI =====================
+st.set_page_config(page_title="🍌\ Banana Disease Detection (CNN + NLP)", layout="centered")
+st.title(" केळीच्या पानांवरील रोगांचे निदान")
+st.markdown("प्रतिमा किंवा लक्षणे वापरून केळीवरील रोगांचे निदान करा (मराठी, हिंदी, इंग्रजी भाषांमध्ये).")
+
+option = st.radio("इनपुट पद्धत निवडा:", ["Image Only", "Text Only", "Both"])
+
+# ===================== IMAGE FLOW =====================
+if option in ["Image Only", "Both"]:
+    st.subheader(" प्रतिमा अपलोड करा")
+    uploaded_img = st.file_uploader("JPG / PNG", type=["jpg", "jpeg", "png"])
+    if uploaded_img:
+        temp_path = "temp_uploaded.jpg"
+        with open(temp_path, "wb") as f:
+            f.write(uploaded_img.getbuffer())
+
+        st.image(temp_path, caption="अपलोड केलेली प्रतिमा", use_column_width=True)
+        st.info("CLIP मॉडेलद्वारे पडताळणी करत आहे...")
+
+        status, reason, clip_conf = verify_image_with_clip(temp_path)
+        if status == "REJECTED":
+            st.error(f" CLIP नकार: {reason} [विश्वासार्हता: {clip_conf:.2f}]")
+        else:
+            st.success(f" CLIP मंजूरी: शक्यतो केळीचे पान [विश्वासार्हता: {clip_conf:.2f}]")
+            pred_disease, cnn_conf, img = predict_disease(temp_path)
+            st.markdown(f"**ओळखलेला रोग:** {pred_disease} (विश्वासार्हता: {cnn_conf:.2%})")
+
+            marathi_info = get_marathi_recommendation_fuzzy(pred_disease, cnn_conf)
+            if marathi_info:
+                st.subheader(" मराठी शिफारस:")
+                for k, v in marathi_info.items():
+                    st.markdown(f"**{k}**: {v}")
+            else:
+                st.warning(" ज्ञानतळात रोगासाठी माहिती नाही.")
+            st.image(img, caption=f"{pred_disease} ({cnn_conf:.2%})", use_column_width=True)
+
+        os.remove(temp_path)
+
+# ===================== TEXT FLOW =====================
+if option in ["Text Only", "Both"]:
+    st.subheader(" लक्षणे लिहा")
+    symptoms = st.text_area("लक्षणे (मराठी / हिंदी / इंग्रजी):")
+    if symptoms and st.button(" रोग ओळखा"):
+        result = predict_disease_from_text(symptoms)
+        if "message" in result:
+            st.warning(result["message"])
+        else:
+            st.subheader(" शिफारस:")
+            for k, v in result.items():
+                st.markdown(f"**{k}**: {v}")

requirements.txt

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+tensorflow==2.18.0
+torch==2.6.0+cu124
+transformers==4.54.0
+joblib==1.5.1
+opencv-python-headless== 4.12.0.88
+numpy==2.0.2
+matplotlib==3.10.0
+pandas==2.2.2