my commit

.gitattributes

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+*.pth filter=lfs diff=lfs merge=lfs -text

.gitignore

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+.venv

.idea/.gitignore

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+# Default ignored files
+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/

.idea/Pest_disease.iml

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+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$">
+      <excludeFolder url="file://$MODULE_DIR$/.venv" />
+    </content>
+    <orderEntry type="jdk" jdkName="Python 3.11 (Pest_disease)" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/inspectionProfiles/Project_Default.xml

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+<component name="InspectionProjectProfileManager">
+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
+    <inspection_tool class="PyPackageRequirementsInspection" enabled="true" level="WARNING" enabled_by_default="true">
+      <option name="ignoredPackages">
+        <list>
+          <option value="huggingface_hub" />
+        </list>
+      </option>
+    </inspection_tool>
+  </profile>
+</component>

.idea/inspectionProfiles/profiles_settings.xml

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+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Black">
+    <option name="sdkName" value="Python 3.11 (Pest_disease)" />
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.11 (Pest_disease)" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/Pest_disease.iml" filepath="$PROJECT_DIR$/.idea/Pest_disease.iml" />
+    </modules>
+  </component>
+</project>

.idea/vcs.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$" vcs="Git" />
+  </component>
+</project>

Dockerfile

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+# ✅ Base image with Python
+FROM python:3.10-slim
+
+# Set working directory
+WORKDIR /app
+
+# Copy files
+COPY requirements.txt .
+COPY main.py .
+
+# Install dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Expose the port
+EXPOSE 7860
+
+# Run FastAPI server
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

main.py

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+from fastapi import FastAPI, File, UploadFile
+from fastapi.responses import JSONResponse
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+from PIL import Image
+import torch
+import io
+
+# Initialize FastAPI app
+app = FastAPI(title="🌱 Plant Disease Detection API (MobileNetV2)")
+
+# Load model + processor from Hugging Face
+MODEL_NAME = "linkanjarad/mobilenet_v2_1.0_224-plant-disease-identification"
+processor = AutoImageProcessor.from_pretrained(MODEL_NAME)
+model = AutoModelForImageClassification.from_pretrained(MODEL_NAME)
+
+# Device setup
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+model.eval()
+
+@app.get("/")
+def home():
+    return {"message": "✅ Plant Disease Detection API is running!"}
+
+@app.post("/predict/")
+async def predict(file: UploadFile = File(...)):
+    try:
+        # Read image
+        image = Image.open(io.BytesIO(await file.read())).convert("RGB")
+
+        # Preprocess
+        inputs = processor(images=image, return_tensors="pt").to(device)
+
+        # Run inference
+        with torch.no_grad():
+            outputs = model(**inputs)
+            logits = outputs.logits
+            predicted_class_idx = logits.argmax(-1).item()
+
+        # Decode prediction
+        label = model.config.id2label[predicted_class_idx]
+        confidence = torch.nn.functional.softmax(logits, dim=-1)[0][predicted_class_idx].item()
+
+        # Response
+        result = {
+            "filename": file.filename,
+            "prediction": label,
+            "confidence": round(confidence, 3),
+        }
+
+        return JSONResponse(content=result)
+
+    except Exception as e:
+        return JSONResponse(content={"error": str(e)})
+from fastapi import FastAPI, File, UploadFile
+from fastapi.responses import JSONResponse
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+from PIL import Image
+import torch
+import io
+import uvicorn
+
+# 🌿 Initialize FastAPI app
+app = FastAPI(title="🌱 Plant Disease Detection API (MobileNetV2)")
+
+# 🧠 Load model + processor from your Hugging Face repo
+MODEL_NAME = "theabeerrai/mobilenet_plant_disease"
+processor = AutoImageProcessor.from_pretrained(MODEL_NAME)
+model = AutoModelForImageClassification.from_pretrained(MODEL_NAME)
+
+# ⚙️ Set device (GPU if available)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+model.eval()
+
+@app.get("/")
+def root():
+    return {
+        "status": "✅ API is live!",
+        "model": MODEL_NAME,
+        "device": str(device),
+        "usage": "POST an image to /predict/ to identify plant disease",
+    }
+
+@app.post("/predict/")
+async def predict(file: UploadFile = File(...)):
+    try:
+        # Read and convert uploaded image
+        image_bytes = await file.read()
+        image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+
+        # Preprocess image
+        inputs = processor(images=image, return_tensors="pt").to(device)
+
+        # Run inference
+        with torch.no_grad():
+            outputs = model(**inputs)
+            logits = outputs.logits
+            predicted_class_idx = logits.argmax(-1).item()
+
+        # Get prediction label and confidence
+        label = model.config.id2label[predicted_class_idx]
+        confidence = torch.nn.functional.softmax(logits, dim=-1)[0][predicted_class_idx].item()
+
+        # Return result
+        return JSONResponse(
+            content={
+                "filename": file.filename,
+                "prediction": label,
+                "confidence": round(confidence, 3),
+            }
+        )
+
+    except Exception as e:
+        return JSONResponse(status_code=500, content={"error": str(e)})
+
+# ✅ Local run (for testing Docker or dev mode)
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=7860)

requirements.txt

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+fastapi
+uvicorn
+torch
+transformers
+pillow