add files

Dockerfile

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+FROM python:3.12
+
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+WORKDIR /app
+
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+COPY --chown=user . /app
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+import pandas as pd
+from sentence_transformers import SentenceTransformer
+import chromadb
+from fastapi.middleware.cors import CORSMiddleware
+import uvicorn
+import requests
+# Define FastAPI app
+app = FastAPI()
+
+origins = [
+    "http://localhost:5173",
+    "localhost:5173"
+]
+
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"]
+)
+
+# Load the dataset and model at startup
+df = pd.read_csv("hf://datasets/QuyenAnhDE/Diseases_Symptoms/Diseases_Symptoms.csv")
+df['Symptoms'] = df['Symptoms'].str.split(',')
+df['Symptoms'] = df['Symptoms'].apply(lambda x: [s.strip() for s in x])
+
+model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+client = chromadb.PersistentClient(path='./chromadb')
+collection = client.get_or_create_collection(name="symptomsvector")
+
+class SymptomQuery(BaseModel):
+    symptom: str
+
+# Endpoint to handle symptom query and return matching symptoms
+@app.post("/find_matching_symptoms")
+def find_matching_symptoms(query: SymptomQuery):
+    # Generate embedding for the symptom query
+    symptoms = query.symptom.split(',')
+    all_results = []
+
+    for symptom in symptoms:
+        symptom = symptom.strip()
+        query_embedding = model.encode([symptom])
+
+        # Perform similarity search in ChromaDB
+        results = collection.query(
+            query_embeddings=query_embedding.tolist(),
+            n_results=3  # Return top 3 similar symptoms for each symptom
+        )
+        all_results.extend(results['documents'][0])
+
+    # Remove duplicates while preserving order
+    matching_symptoms = list(dict.fromkeys(all_results))
+
+    return {"matching_symptoms": matching_symptoms}
+
+# Endpoint to handle symptom query and return matching diseases
+@app.post("/find_matching_diseases")
+def find_matching_diseases(query: SymptomQuery):
+    # Generate embedding for the symptom query
+    query_embedding = model.encode([query.symptom])
+
+    # Perform similarity search in ChromaDB
+    results = collection.query(
+        query_embeddings=query_embedding.tolist(),
+        n_results=5  # Return top 5 similar symptoms
+    )
+
+    # Extract matching symptoms
+    matching_symptoms = results['documents'][0]
+
+    # Filter diseases that match the symptoms
+    matching_diseases = df[df['Symptoms'].apply(lambda x: any(s in matching_symptoms for s in x))]
+
+    return {"matching_diseases": matching_diseases['Name'].tolist()}
+
+# Endpoint to handle symptom query and return detailed disease list
+@app.post("/find_disease_list")
+def find_disease_list(query: SymptomQuery):
+    # Generate embedding for the symptom query
+    query_embedding = model.encode([query.symptom])
+
+    # Perform similarity search in ChromaDB
+    results = collection.query(
+        query_embeddings=query_embedding.tolist(),
+        n_results=5  # Return top 5 similar symptoms
+    )
+
+    # Extract matching symptoms
+    matching_symptoms = results['documents'][0]
+
+    # Filter diseases that match the symptoms
+    matching_diseases = df[df['Symptoms'].apply(lambda x: any(s in matching_symptoms for s in x))]
+
+    # Create a list of disease information
+    disease_list = []
+    symptoms_list = []
+    unique_symptoms_list = []
+    for _, row in matching_diseases.iterrows():
+        disease_info = {
+            'Disease': row['Name'],
+            'Symptoms': row['Symptoms'],
+            'Treatments': row['Treatments']
+        }
+        disease_list.append(disease_info)
+        symptoms_info = row['Symptoms']
+        symptoms_list.append(symptoms_info)
+    for i in range(len(symptoms_list)):
+        for j in range(len(symptoms_list[i])):
+            if symptoms_list[i][j] not in unique_symptoms_list:
+                unique_symptoms_list.append(symptoms_list[i][j])
+    return {"disease_list": disease_list, "unique_symptoms_list": unique_symptoms_list}
+
+class SelectedSymptomsQuery(BaseModel):
+    selected_symptoms: list
+
+@app.post("/find_disease")
+def find_disease(query: SelectedSymptomsQuery):
+    selected_symptoms = query.selected_symptoms
+    # Filter diseases that match at least one of the selected symptoms
+    matching_diseases = df[df['Symptoms'].apply(lambda x: any(s in x for s in selected_symptoms))]
+
+    # Sort diseases by the number of matching symptoms in descending order
+    matching_diseases['match_count'] = matching_diseases['Symptoms'].apply(lambda x: sum(s in selected_symptoms for s in x))
+    matching_diseases = matching_diseases.sort_values(by='match_count', ascending=False)
+
+    # Create a list of disease information
+    disease_list = []
+    max_match_count_disease = None
+    max_match_count = -1
+
+    for _, row in matching_diseases.iterrows():
+        disease_info = {
+            'Disease': row['Name'],
+            'Symptoms': row['Symptoms'],
+            'Treatments': row['Treatments'],
+            'MatchCount': row['match_count']
+        }
+        disease_list.append(disease_info)
+
+        # Check if this disease has the maximum match count
+        if row['match_count'] > max_match_count:
+            max_match_count = row['match_count']
+            max_match_count_disease = disease_info
+
+    return {"disease_list": disease_list, "max_match_count_disease": max_match_count_disease}
+class DiseaseListQuery(BaseModel):
+    disease_list: list
+
+class DiseaseDetail(BaseModel):
+    Disease: str
+    Symptoms: list
+    Treatments: str
+    MatchCount: int
+
+@app.post("/pass2llm")
+def pass2llm(query: DiseaseDetail):
+    # Prepare the data to be sent to the LLM API
+    disease_list_details = query
+
+    # Make the API request to the Ngrok endpoint to get the public URL
+    headers = {
+        "Authorization": "Bearer 2npJaJjnLBj1RGPcGf0QiyAAJHJ_5qqtw2divkpoAipqN9WLG",
+        "Ngrok-Version": "2"
+    }
+    response = requests.get("https://api.ngrok.com/endpoints", headers=headers)
+
+    # Check if the request was successful
+    if response.status_code == 200:
+        llm_api_response = response.json()
+        public_url = llm_api_response['endpoints'][0]['public_url']
+
+        # Prepare the prompt with the disease list details
+        prompt = f"Here is a list of diseases and their details: {disease_list_details}. Please generate a summary."
+
+        # Make the request to the LLM API
+        llm_headers = {
+            "Content-Type": "application/json"
+        }
+        llm_payload = {
+            "model": "llama3",
+            "prompt": prompt,
+            "stream": False
+        }
+        llm_response = requests.post(f"{public_url}/api/generate", headers=llm_headers, json=llm_payload)
+
+        # Check if the request to the LLM API was successful
+        if llm_response.status_code == 200:
+            llm_response_json = llm_response.json()
+            return {"message": "Successfully passed to LLM!", "llm_response": llm_response_json.get("response")}
+        else:
+            return {"message": "Failed to get response from LLM!", "error": llm_response.text}
+    else:
+        return {"message": "Failed to get public URL from Ngrok!", "error": response.text}
+# To run the FastAPI app with Uvicorn
+# if __name__ == "__main__":
+#     uvicorn.run(app, host="0.0.0.0", port=8000)