Added master.py and Dockerfile

DockerFile

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+FROM python:3.11-slim
+
+WORKDIR /app
+
+COPY master.py /app/
+COPY requirements.txt /app/
+
+RUN pip install -r requirements.txt
+
+CMD ["uvicorn", "master:app", "--host", "0.0.0.0", "--port", "7860"]

master.py

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+# from fastapi import FastAPI
+# from fastapi.middleware.cors import CORSMiddleware
+# from pydantic import BaseModel
+# from sentence_transformers import SentenceTransformer, util
+
+# app = FastAPI()
+
+# app.add_middleware(
+#     CORSMiddleware,
+#     allow_origins=["http://localhost:5173"],
+#     allow_credentials=True,
+#     allow_methods=["*"],
+#     allow_headers=["*"],
+# )
+
+# model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
+
+# class Profile(BaseModel):
+#     name: str
+#     budget: float
+#     lifestyle: dict
+#     interests: list
+
+# class CompatibilityRequest(BaseModel):
+#     user_profile: Profile
+#     candidate_profiles: list[Profile]
+
+# @app.post("/compute_compatibility")
+# def compute_compatibility(data: CompatibilityRequest):
+#     scores = []
+#     user_text = f"Budget: {data.user_profile.budget}, Lifestyle: {data.user_profile.lifestyle}, Interests: {', '.join(data.user_profile.interests)}"
+#     user_embedding = model.encode(user_text, convert_to_tensor=True)
+
+#     for candidate in data.candidate_profiles:
+#         candidate_text = f"Budget: {candidate.budget}, Lifestyle: {candidate.lifestyle}, Interests: {', '.join(candidate.interests)}"
+#         candidate_embedding = model.encode(candidate_text, convert_to_tensor=True)
+
+#         similarity_score = util.pytorch_cos_sim(user_embedding, candidate_embedding).item()
+#         match_reasons = []
+
+#         if similarity_score > 0.7:
+#             match_reasons.append("Strong compatibility based on overall profile match")
+#         elif similarity_score > 0.4:
+#             match_reasons.append("Moderate compatibility with some common aspects")
+#         else:
+#             match_reasons.append("Low compatibility due to differing aspects")
+
+#         scores.append({
+#             "profile": candidate.name,
+#             "compatibility": round(similarity_score * 100),
+#             "matchReasons": match_reasons
+#         })
+
+#     return {"all_matches": scores}
+
+# if __name__ == "__main__":
+#     import uvicorn
+#     uvicorn.run("master:app", host="127.0.0.1", port=8000, reload=True)
+
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
+import torch
+
+app = FastAPI()
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["http://localhost:5173"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+model_name = "meta-llama/Meta-Llama-3-8B-Instruct"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
+generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+class Profile(BaseModel):
+    name: str
+    budget: float
+    lifestyle: dict
+    interests: list
+
+class CompatibilityRequest(BaseModel):
+    user_profile: Profile
+    candidate_profiles: list[Profile]
+
+@app.post("/compute_compatibility")
+def compute_compatibility(data: CompatibilityRequest):
+    scores = []
+    user_text = f"Budget: {data.user_profile.budget}, Lifestyle: {data.user_profile.lifestyle}, Interests: {', '.join(data.user_profile.interests)}"
+
+    for candidate in data.candidate_profiles:
+        candidate_text = f"Budget: {candidate.budget}, Lifestyle: {candidate.lifestyle}, Interests: {', '.join(candidate.interests)}"
+        
+        prompt = f"Compare the following profiles and rate their compatibility from 0 to 100:\nUser: {user_text}\nCandidate: {candidate_text}\nCompatibility Score:"
+        response = generator(prompt, max_length=50, do_sample=True)
+        compatibility_score = int(''.join(filter(str.isdigit, response[0]["generated_text"])))
+
+        scores.append({
+            "profile": candidate.name,
+            "compatibility": compatibility_score,
+            "matchReasons": f"Generated by Llama-3 based on textual profile similarities"
+        })
+
+    return {"all_matches": scores}
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run("master:app", host="127.0.0.1", port=8000, reload=True)

requirements.txt

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+fastapi
+uvicorn
+transformers
+torch