Update main.py

main.py

@@ -1,152 +1,179 @@
-# main.py - SLM Inference Server
-from fastapi import FastAPI, HTTPException
-from fastapi.middleware.cors import CORSMiddleware
-from pydantic import BaseModel
-import subprocess
-import tiktoken
-import os
-import time
-
-app = FastAPI()
-
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-class GenerateRequest(BaseModel):
-    prompt: str
-    max_tokens: int = 100
-    temperature: float = 0.8
-    top_k: int = 40
-
-# Tokenizer setup
-try:
-    enc = tiktoken.get_encoding("gpt2")
-    print("✅ Tokenizer loaded successfully.")
-except Exception as e:
-    print(f"❌ Warning: tiktoken not found. Error: {e}")
-    enc = None
-
-
-@app.get("/health")
-async def health_check():
-    current_dir = os.path.dirname(os.path.abspath(__file__))
-    exe_path    = os.path.join(current_dir, "inference.exe")
-    model_path  = os.path.join(current_dir, "model.bin")
-
-    return {
-        "status": "ok",
-        "inference_exe_found": os.path.exists(exe_path),
-        "model_bin_found":     os.path.exists(model_path),
-        "working_directory":   current_dir
-    }
-
-
-@app.post("/generate")
-async def generate_text(req: GenerateRequest):
-
-    # 0. Tokenizer check
-    if enc is None:
-        raise HTTPException(
-            status_code=500,
-            detail="Tokenizer not loaded. Run: pip install tiktoken"
-        )
-
-    # 1. Encode prompt
-    input_tokens = enc.encode(req.prompt)
-    token_str    = ",".join(map(str, input_tokens))
-
-    # 2. Path setup
-    current_dir = os.path.dirname(os.path.abspath(__file__))
-    exe_path    = os.path.join(current_dir, "inference.exe")
-    model_path  = os.path.join(current_dir, "model.bin")
-
-    print(f"DEBUG: exe   -> {exe_path}   exists={os.path.exists(exe_path)}")
-    print(f"DEBUG: model -> {model_path} exists={os.path.exists(model_path)}")
-
-    # 3. File existence checks
-    if not os.path.exists(exe_path):
-        raise HTTPException(
-            status_code=500,
-            detail=f"inference.exe nahi mili: {exe_path} — Pehle C++ compile karo!"
-        )
-
-    if not os.path.exists(model_path):
-        raise HTTPException(
-            status_code=500,
-            detail=f"model.bin nahi mili: {model_path} — Model file same folder mein rakhni hai!"
-        )
-
-    # 4. Run C++ engine
-    # FIX: temperature aur top_k ab subprocess ko pass ho rahe hain
-    try:
-        start_time = time.perf_counter()
-
-        process = subprocess.run(
-            [
-                exe_path,
-                token_str,
-                str(req.max_tokens),
-                str(req.temperature),   # <-- FIX: was missing before
-                str(req.top_k),         # <-- FIX: was missing before
-            ],
-            capture_output=True,
-            text=True,
-            cwd=current_dir
-        )
-
-        elapsed_ms = (time.perf_counter() - start_time) * 1000
-
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Execution failed: {str(e)}")
-
-    # 5. Error check
-    if process.returncode != 0 and not process.stdout.strip():
-        stdout_msg = process.stdout.strip() if process.stdout else ""
-        stderr_msg = process.stderr.strip() if process.stderr else ""
-
-        if "ERROR_MODEL_NOT_FOUND" in stdout_msg:
-            raise HTTPException(status_code=500, detail="model.bin nahi mili! Same folder mein rakho.")
-        elif "ERROR_ARGS" in stdout_msg:
-            raise HTTPException(status_code=500, detail="C++ engine ko arguments galat mile.")
-        else:
-            raise HTTPException(
-                status_code=500,
-                detail=f"C++ Error | stdout: '{stdout_msg}' | stderr: '{stderr_msg}'"
-            )
-
-    # 6. Decode output token IDs
-    try:
-        output_str = process.stdout.strip()
-
-        if not output_str:
-            generated_ids = []
-        else:
-            generated_ids = []
-            for x in output_str.split():
-                try:
-                    generated_ids.append(int(x))
-                except ValueError:
-                    print(f"DEBUG: skipping non-integer token: '{x}'")
-
-        generated_text = enc.decode(generated_ids) if generated_ids else ""
-
-        tokens_out     = len(generated_ids)
-        tokens_per_sec = round(tokens_out / (elapsed_ms / 1000), 2) if elapsed_ms > 0 else 0
-
-        print(f"✅ Generated {tokens_out} tokens in {elapsed_ms:.2f}ms ({tokens_per_sec} tok/s)")
-
-        return {
-            "prompt":         req.prompt,
-            "generated_text": generated_text,
-            "tokens_in":      len(input_tokens),
-            "tokens_out":     tokens_out,
-            "latency_ms":     round(elapsed_ms, 2),
-            "tokens_per_sec": tokens_per_sec
-        }
-
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Decoding error: {str(e)}")
+# main.py - SLM Inference Server
+from fastapi import FastAPI, HTTPException
+from fastapi.responses import FileResponse
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+import subprocess
+import tiktoken
+import os
+import time
+
+app = FastAPI()
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+class GenerateRequest(BaseModel):
+    prompt: str
+    max_tokens: int = 100
+    temperature: float = 0.8
+    top_k: int = 40
+
+# Tokenizer setup
+try:
+    enc = tiktoken.get_encoding("gpt2")
+    print("✅ Tokenizer loaded successfully.")
+except Exception as e:
+    print(f"❌ Warning: tiktoken not found. Error: {e}")
+    enc = None
+
+
+# ✅ FIX 1: index.html serve karo root pe
+@app.get("/")
+async def root():
+    current_dir = os.path.dirname(os.path.abspath(__file__))
+    return FileResponse(os.path.join(current_dir, "index.html"))
+
+
+@app.get("/health")
+async def health_check():
+    current_dir = os.path.dirname(os.path.abspath(__file__))
+    # ✅ FIX 2: .exe → no extension (Linux binary)
+    exe_path    = os.path.join(current_dir, "inference")
+    model_path  = os.path.join(current_dir, "model.bin")
+
+    return {
+        "status": "ok",
+        "inference_exe_found": os.path.exists(exe_path),
+        "model_bin_found":     os.path.exists(model_path),
+        "working_directory":   current_dir
+    }
+
+
+@app.post("/generate")
+async def generate_text(req: GenerateRequest):
+
+    # 0. Tokenizer check
+    if enc is None:
+        raise HTTPException(
+            status_code=500,
+            detail="Tokenizer not loaded. Run: pip install tiktoken"
+        )
+
+    # 1. Encode prompt
+    input_tokens = enc.encode(req.prompt)
+    token_str    = ",".join(map(str, input_tokens))
+
+    # 2. Path setup
+    current_dir = os.path.dirname(os.path.abspath(__file__))
+    # ✅ FIX 3: .exe → no extension (Linux binary)
+    exe_path    = os.path.join(current_dir, "inference")
+    model_path  = os.path.join(current_dir, "model.bin")
+
+    print(f"DEBUG: exe   -> {exe_path}   exists={os.path.exists(exe_path)}")
+    print(f"DEBUG: model -> {model_path} exists={os.path.exists(model_path)}")
+
+    # 3. File existence checks
+    if not os.path.exists(exe_path):
+        raise HTTPException(
+            status_code=500,
+            detail=f"inference binary nahi mili: {exe_path} — Dockerfile se compile honi chahiye!"
+        )
+
+    if not os.path.exists(model_path):
+        raise HTTPException(
+            status_code=500,
+            detail=f"model.bin nahi mili: {model_path} — Model file same folder mein rakhni hai!"
+        )
+
+    # 4. Run C++ engine
+    try:
+        start_time = time.perf_counter()
+
+        process = subprocess.run(
+            [
+                exe_path,
+                token_str,
+                str(req.max_tokens),
+                str(req.temperature),
+                str(req.top_k),
+            ],
+            capture_output=True,
+            text=True,
+            cwd=current_dir
+        )
+
+        elapsed_ms = (time.perf_counter() - start_time) * 1000
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Execution failed: {str(e)}")
+
+    # 5. Error check
+    if process.returncode != 0 and not process.stdout.strip():
+        stdout_msg = process.stdout.strip() if process.stdout else ""
+        stderr_msg = process.stderr.strip() if process.stderr else ""
+
+        if "ERROR_MODEL_NOT_FOUND" in stdout_msg:
+            raise HTTPException(status_code=500, detail="model.bin nahi mili! Same folder mein rakho.")
+        elif "ERROR_ARGS" in stdout_msg:
+            raise HTTPException(status_code=500, detail="C++ engine ko arguments galat mile.")
+        else:
+            raise HTTPException(
+                status_code=500,
+                detail=f"C++ Error | stdout: '{stdout_msg}' | stderr: '{stderr_msg}'"
+            )
+
+    # 6. Decode output token IDs
+    try:
+        output_str = process.stdout.strip()
+
+        if not output_str:
+            generated_ids = []
+        else:
+            generated_ids = []
+            for x in output_str.split():
+                try:
+                    generated_ids.append(int(x))
+                except ValueError:
+                    print(f"DEBUG: skipping non-integer token: '{x}'")
+
+        generated_text = enc.decode(generated_ids) if generated_ids else ""
+
+        tokens_out     = len(generated_ids)
+        tokens_per_sec = round(tokens_out / (elapsed_ms / 1000), 2) if elapsed_ms > 0 else 0
+
+        print(f"✅ Generated {tokens_out} tokens in {elapsed_ms:.2f}ms ({tokens_per_sec} tok/s)")
+
+        return {
+            "prompt":         req.prompt,
+            "generated_text": generated_text,
+            "tokens_in":      len(input_tokens),
+            "tokens_out":     tokens_out,
+            "latency_ms":     round(elapsed_ms, 2),
+            "tokens_per_sec": tokens_per_sec
+        }
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Decoding error: {str(e)}")
+```
+
+---
+
+## 🚀 Deployment Steps (Basic se Basic)
+
+**Step 1** — Hugging Face pe jaao → apna `NOT-OMEGA/Inference` Space kholo
+
+**Step 2** — `main.py` file pe click karo → Edit button dabao → Pura purana code delete karo → Upar wala naya code paste karo → **Commit changes**
+
+**Step 3** — Baaki files (Dockerfile, index.html, inference.cpp, requirements.txt) already sahi hain, unhe **mat chhuona**
+
+**Step 4** — Space automatically rebuild hoga (2-5 minutes lagenge)
+
+**Step 5** — **Logs** tab check karo — ye lines dikhni chahiye:
+```
+✅ Tokenizer loaded successfully.
+INFO: Uvicorn running on http://0.0.0.0:7860