Add application file

.gitignore

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+.env
+_pycache_/
+Model1-crop-rec\__pycache__
+Model-yeild-rec\yield_model_from_csv.joblib

Dockerfile

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+FROM python:3.10-slim
+
+WORKDIR /app
+COPY . /app
+
+RUN pip install --no-cache-dir -r requirements.txt
+
+EXPOSE 7860
+CMD ["uvicorn", "runmodel:app", "--host", "0.0.0.0", "--port", "7860"]

Model-crop-rec/crop_imputer.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:cb75c61e69929761446a89bf2ca95e1c5e9e9c5d524f4795f716a8b0cd2aa007
+size 847

Model-crop-rec/crop_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8f2cca809359586cef47f38ef48e9fc7c61b516494e06632829eeec3194906e8
+size 11030105

Model-plant/plant_disease_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2928928345656a1e78c9fd63b7743135824346899068e39657366032e604e13e
+size 97589648

Model-yeild-rec/yield_model_from_csv.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:196329e9bad8817270ea1f5806b37bcbfdf2d797b795b28363cb6a23f076d555
+size 126713923

README.md

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 ---
-title: Farmer Bud
-emoji: 👁
-colorFrom: indigo
-colorTo: gray
-sdk: docker
+title: Farmer Help
+emoji: 📚
+colorFrom: gray
+colorTo: indigo
+sdk: gradio
+sdk_version: 5.44.1
+app_file: app.py
 pinned: false
 license: apache-2.0
 ---

app.py

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+from fastapi import FastAPI, HTTPException, File, UploadFile
+from pydantic import BaseModel
+import pandas as pd
+import numpy as np
+import joblib
+from pymongo import MongoClient
+from datetime import datetime
+from openai import OpenAI
+import os
+from runmodel import app
+import dotenv
+import json
+import io
+import tensorflow as tf
+from PIL import Image
+
+# -------------------------
+# Config
+# -------------------------
+dotenv.load_dotenv()
+
+MONGO_URI = os.getenv("MONGO_URI")
+MONGO_DB = os.getenv("MONGO_DB")
+MONGO_COLL = os.getenv("MONGO_COLL")
+HF_TOKEN = os.getenv("HF_TOKEN")
+
+# Model paths
+CROP_MODEL_PATH = "Model-crop-rec/crop_model.joblib"
+CROP_IMPUTER_PATH = "Model-crop-rec/crop_imputer.joblib"
+YIELD_MODEL_PATH = "Model-yeild-rec/yield_model_from_csv.joblib"
+DISEASE_MODEL_PATH = "Model-plant/plant_disease_model.h5" # <-- NEW
+
+# --- NEW: Disease Class Names ---
+## IMPORTANT: Update with your actual class names in the correct order ##
+DISEASE_CLASS_NAMES = ["Early Blight", "Late Blight", "Healthy"]
+
+# Feature sets
+CROP_FEATURES = ["N", "P", "K", "temperature", "humidity", "ph", "rainfall"]
+YIELD_FEATURES = ['Year', 'rainfall_mm', 'pesticides_tonnes', 'avg_temp', 'Area', 'Item']
+AI_MODEL_NAME = "openai/gpt-oss-120b:cerebras"
+
+# -------------------------
+# Load models & imputer
+# -------------------------
+print("Loading machine learning models...")
+clf = joblib.load(CROP_MODEL_PATH)
+imp = joblib.load(CROP_IMPUTER_PATH)
+yield_model = joblib.load(YIELD_MODEL_PATH)
+print("Crop, Imputer, and Yield models loaded successfully.")
+
+# --- NEW: Load Disease Detection Model ---
+disease_model = None
+try:
+    if os.path.exists(DISEASE_MODEL_PATH):
+        disease_model = tf.keras.models.load_model(DISEASE_MODEL_PATH)
+        print("Plant disease detection model loaded successfully.")
+    else:
+        print(f"Warning: Disease model not found at {DISEASE_MODEL_PATH}")
+except Exception as e:
+    print(f"Error loading disease model: {e}")
+
+
+# Database connection
+client = MongoClient(MONGO_URI)
+coll = client[MONGO_DB][MONGO_COLL]
+
+# -------------------------
+# LLM Client (for gpt-oss-120b text generation)
+# -------------------------
+llm_client = None
+if HF_TOKEN:
+    try:
+        llm_client = OpenAI(
+            base_url="https://router.huggingface.co/v1",
+            api_key=HF_TOKEN,
+            default_headers={"Accept-Encoding": "identity"}
+        )
+        print("LLM client for text generation initialized.")
+    except Exception as e:
+        print(f"Warning: LLM client init failed: {e}")
+        llm_client = None
+
+# -------------------------
+# NEW: Image Preprocessing Helper
+# -------------------------
+def preprocess_image(image_bytes: bytes) -> np.ndarray:
+    """Reads image bytes, resizes to 128x128, normalizes, and prepares for the model."""
+    img = Image.open(io.BytesIO(image_bytes))
+    img = img.convert("RGB") # Ensure 3 channels
+    img = img.resize((128, 128))
+    img_array = tf.keras.preprocessing.image.img_to_array(img)
+    img_array = img_array / 255.0  # Normalize to [0, 1]
+    img_batch = np.expand_dims(img_array, axis=0) # Add batch dimension
+    return img_batch
+
+# -------------------------
+# Core Logic Functions
+# -------------------------
+def get_crop_recommendation_logic(farmer_doc: dict):
+    # This function remains the same
+    if any(pd.isna(farmer_doc.get(k)) for k in CROP_FEATURES):
+        raise ValueError("Missing required fields for crop recommendation.")
+    feat = {k: float(farmer_doc[k]) for k in CROP_FEATURES}
+    actual_crop = str(farmer_doc.get("crop", "")).strip().lower()
+    df_in = pd.DataFrame([feat], columns=CROP_FEATURES)
+    df_in_imp = pd.DataFrame(imp.transform(df_in), columns=CROP_FEATURES)
+    probs = clf.predict_proba(df_in_imp)[0]
+    prob_map = dict(zip(clf.classes_, probs))
+    crop_df = pd.DataFrame(list(coll.find()))
+    centroids = crop_df.groupby("crop")[CROP_FEATURES].mean()
+    centroid_matrix = centroids.values
+    dists = np.linalg.norm(centroid_matrix - df_in_imp.values.reshape(1, -1), axis=1)
+    sims = 1.0 / (1.0 + dists)
+    sim_map = dict(zip(centroids.index, sims))
+    all_scores = {}
+    for crop in clf.classes_:
+        p = prob_map.get(crop, 0.0)
+        s = sim_map.get(crop, 0.0)
+        all_scores[crop.lower()] = { "crop": crop, "probability": float(p), "centroid_similarity": float(s), "final_score": float(0.5 * p + 0.5 * s) }
+    advice_for_existing_crop = None
+    if actual_crop and actual_crop in all_scores and llm_client:
+        advice_for_existing_crop = get_cultivation_advice(llm_client, all_scores[actual_crop]['crop'], feat)
+    sorted_scores = sorted(all_scores.values(), key=lambda x: x['final_score'], reverse=True)
+    new_recommendations = [s for s in sorted_scores if s['crop'].lower() != actual_crop][:3]
+    advice_for_new_crop = None
+    if new_recommendations and llm_client:
+        advice_for_new_crop = get_cultivation_advice(llm_client, new_recommendations[0]['crop'], feat)
+    return {
+        "features_used": feat,
+        "new_crop_recommendations": new_recommendations,
+        "advice_for_top_new_crop": advice_for_new_crop,
+        "advice_for_existing_crop": advice_for_existing_crop
+    }
+
+def get_yield_prediction_logic(farmer_doc: dict):
+    # This function remains the same
+    yield_input = {
+        'Year': farmer_doc.get('year', datetime.now().year),
+        'rainfall_mm': farmer_doc.get('rainfall'),
+        'pesticides_tonnes': farmer_doc.get('pesticides_tonnes', 0.0),
+        'avg_temp': farmer_doc.get('temperature'),
+        'Area': farmer_doc.get('state', 'Unknown'),
+        'Item': farmer_doc.get('crop')
+    }
+    missing_fields = [k for k in YIELD_FEATURES if yield_input.get(k) is None]
+    if missing_fields:
+        raise ValueError(f"Missing required fields for yield prediction: {missing_fields}")
+    yield_input_data = pd.DataFrame([yield_input], columns=YIELD_FEATURES)
+    predicted_yield_hg_ha = yield_model.predict(yield_input_data)[0]
+    predicted_yield_quintal_ha = float(round(predicted_yield_hg_ha / 10, 2))
+    return {
+        "predicted_yield_quintal_per_hectare": predicted_yield_quintal_ha,
+        "features_used": yield_input
+    }
+
+# -------------------------
+# LLM Helper Functions
+# -------------------------
+
+def get_cultivation_advice(client, crop_name, features):
+    if not client: return "LLM client not available for advice generation."
+    prompt = f"Provide concise cultivation advice for '{crop_name}' given these conditions: N={features['N']:.2f}, P={features['P']:.2f}, K={features['K']:.2f}, pH={features['ph']:.2f}, Temp={features['temperature']:.2f}C, Humidity={features['humidity']:.2f}%, Rainfall={features['rainfall']:.2f}mm. Suggest land prep, sowing, fertilization, irrigation in bullets."
+    try:
+        completion = client.chat.completions.create(model=AI_MODEL_NAME, messages=[{"role": "user", "content": prompt}], temperature=0.7, max_tokens=512)
+        return completion.choices[0].message.content.strip()
+    except Exception as e: return f"Could not generate advice: {e}"
+
+def generate_yield_advice(client, farmer_data, prediction):
+    if not client: return "LLM client not available for advice generation."
+    prompt = f"An expert agronomist providing 2-3 key bullet points to improve crop yield. Farmer's crop: '{farmer_data.get('crop')}' in an area of {farmer_data.get('areaHectare')} ha. Our model predicts a yield of {prediction:.2f} quintals per hectare. Focus on practical steps."
+    try:
+        completion = client.chat.completions.create(model=AI_MODEL_NAME, messages=[{"role": "user", "content": prompt}], temperature=0.7, max_tokens=512)
+        return completion.choices[0].message.content.strip()
+    except Exception as e: return f"Could not generate yield advice: {e}"
+
+def get_intent_from_llm(client, query: str):
+    if not client: return {"intent": "UNKNOWN", "error": "LLM client not available."}
+    prompt = f"""Analyze the user's query and classify it into one of the intents: 'CROP_RECOMMENDATION', 'YIELD_PREDICTION', or 'GREETING'. Respond ONLY with a JSON object like {{"intent": "YOUR_CLASSIFICATION"}}. User query: "{query}" """
+    try:
+        completion = client.chat.completions.create(model=AI_MODEL_NAME, messages=[{"role": "user", "content": prompt}], temperature=0.1, max_tokens=50)
+        result_text = completion.choices[0].message.content.strip()
+        return json.loads(result_text)
+    except Exception as e:
+        print(f"LLM intent classification failed: {e}")
+        return {"intent": "UNKNOWN"}
+
+# -------------------------
+# FastAPI App & Pydantic Models
+# -------------------------
+app = FastAPI(title="Farmer AI Services API")
+
+class FarmerRequest(BaseModel):
+    farmerId: str
+
+class VoiceQueryRequest(BaseModel):
+    farmerId: str
+    query: str
+
+# -------------------------
+# API Endpoints
+# -------------------------
+
+@app.get("/")
+def read_root():
+    return {"status": "API is running", "timestamp": datetime.now().isoformat()}
+
+@app.post("/m1/crop-recommendation")
+def recommend_crop(req: FarmerRequest):
+    farmer_doc = coll.find_one({"farmerId": req.farmerId})
+    if not farmer_doc:
+        raise HTTPException(status_code=404, detail="Farmer not found")
+    try:
+        result = get_crop_recommendation_logic(farmer_doc)
+        return { "farmerId": req.farmerId, **result, "recommended_at": datetime.utcnow().isoformat() }
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"An unexpected error occurred: {e}")
+
+@app.post("/m1/yield")
+def predict_yield(req: FarmerRequest):
+    farmer_doc = coll.find_one({"farmerId": req.farmerId})
+    if not farmer_doc:
+        raise HTTPException(status_code=404, detail="Farmer not found")
+    try:
+        result = get_yield_prediction_logic(farmer_doc)
+        advice = generate_yield_advice(llm_client, farmer_doc, result["predicted_yield_quintal_per_hectare"])
+        return { "farmerId": req.farmerId, **result, "yield_advice": advice, "predicted_at": datetime.utcnow().isoformat() }
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Yield prediction failed: {e}")
+
+@app.post("/m1/voice-query")
+def handle_voice_query(req: VoiceQueryRequest):
+    farmer_doc = coll.find_one({"farmerId": req.farmerId})
+    if not farmer_doc:
+        raise HTTPException(status_code=404, detail="Farmer not found")
+    intent_data = get_intent_from_llm(llm_client, req.query)
+    intent = intent_data.get("intent")
+    response_text = "I'm sorry, I couldn't process that request."
+    if intent == "CROP_RECOMMENDATION":
+        try:
+            result = get_crop_recommendation_logic(farmer_doc)
+            top_crop = result['new_crop_recommendations'][0]['crop'] if result['new_crop_recommendations'] else 'a suitable crop'
+            response_text = f"Based on your farm's data, I recommend planting {top_crop}. {result['advice_for_top_new_crop']}"
+        except Exception as e:
+            response_text = f"I tried to get a crop recommendation, but an error occurred: {e}"
+    elif intent == "YIELD_PREDICTION":
+        try:
+            result = get_yield_prediction_logic(farmer_doc)
+            yield_val = result['predicted_yield_quintal_per_hectare']
+            advice = generate_yield_advice(llm_client, farmer_doc, yield_val)
+            response_text = f"The predicted yield for your {farmer_doc.get('crop', 'crop')} is {yield_val} quintals per hectare. Here is some advice to improve it: {advice}"
+        except Exception as e:
+            response_text = f"I tried to predict the yield, but an error occurred: {e}"
+    elif intent == "GREETING":
+        response_text = "Hello! How can I assist you with your farm today? You can ask for a crop recommendation or a yield prediction."
+    else: # UNKNOWN
+        response_text = "I'm sorry, I didn't understand that. Please ask for a crop recommendation or a yield prediction."
+    return {"response_text": response_text}
+
+# -------------------------
+# NEW: Plant Disease Endpoint
+# -------------------------
+@app.post("/m2/plant-disease")
+async def detect_plant_disease(file: UploadFile = File(...)):
+    """Endpoint for plant disease detection from an image."""
+    if not disease_model:
+        raise HTTPException(status_code=503, detail="Disease detection service is unavailable.")
+
+    # Validate file type
+    if not file.content_type.startswith("image/"):
+        raise HTTPException(status_code=400, detail="Invalid file type. Please upload an image.")
+
+    try:
+        # Read and process the image
+        image_bytes = await file.read()
+        processed_image = preprocess_image(image_bytes)
+
+        # Make prediction
+        predictions = disease_model.predict(processed_image)
+        
+        # Process result
+        predicted_index = np.argmax(predictions[0])
+        confidence = float(predictions[0][predicted_index])
+        predicted_class = DISEASE_CLASS_NAMES[predicted_index]
+
+        return {
+            "predicted_class": predicted_class,
+            "confidence": f"{confidence:.2%}", # Format as percentage string
+            "details": {
+                "raw_predictions": predictions[0].tolist(),
+                "class_names": DISEASE_CLASS_NAMES
+            }
+        }
+    except Exception as e:
+        print(f"Error during disease prediction: {e}")
+        raise HTTPException(status_code=500, detail="Failed to process the image.")
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)

requirements.txt

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+fastapi
+uvicorn[standard]
+pandas
+numpy
+joblib
+scikit-learn
+pymongo
+openai
+requests
+kaggle
+dotenv
+tensorflow 
+Pillow

space.yml

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+sdk: docker
+docker:
+  build:
+    dockerfile: Dockerfile