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Mahoba-Logic-Engine

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mayank23raj commited on May 4

Commit

3eaad37

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1 Parent(s): 1c54346

Update app.py

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Files changed (1) hide show

app.py +35 -31

app.py CHANGED Viewed

@@ -1,18 +1,20 @@
 import os
 from fastapi import FastAPI, Request
 from fastapi.responses import HTMLResponse, StreamingResponse
-import json
 from huggingface_hub import hf_hub_download
 from llama_cpp import Llama
-import uvicorn
 # --- ENGINE SETUP ---
 repo_id = "unsloth/Llama-3.2-1B-Instruct-GGUF"
 filename = "Llama-3.2-1B-Instruct-Q4_K_M.gguf"
-print("🚀 Mahoba Logic: Initializing Engine...")
 model_path = hf_hub_download(repo_id=repo_id, filename=filename)
 llm = Llama(model_path=model_path, n_ctx=2048, n_threads=4, verbose=False)
 # --- THE FRONTEND ---
 HTML_CONTENT = """
@@ -27,18 +29,20 @@ HTML_CONTENT = """
     <style>
         :root { --bg-gradient: linear-gradient(135deg, #6366F1, #7C3AED); --chat-bg: #F9FAFB; --primary: #6D28D9; --secondary: #4F46E5; }
         body { margin: 0; font-family: 'Inter', sans-serif; background: var(--bg-gradient); height: 100vh; width: 100vw; display: flex; justify-content: center; align-items: center; overflow: hidden; }
-        .welcome-screen { position: fixed; inset: 0; background: linear-gradient(135deg, #4F46E5, #7C3AED); display: flex; justify-content: center; align-items: center; z-index: 9999; }
         .welcome-box { text-align: center; color: white; max-width: 420px; padding: 30px; }
-        .welcome-box button, .input-box button { background: white; color: var(--primary); border: none; padding: 14px 28px; border-radius: 12px; font-weight: 600; cursor: pointer; }
         .chat-container { width: 90%; max-width: 900px; height: 85vh; background: white; border-radius: 24px; display: flex; flex-direction: column; box-shadow: 0 25px 60px rgba(0,0,0,0.3); overflow: hidden; position: relative; }
         .header { padding: 20px; text-align: center; font-weight: 600; color: white; background: linear-gradient(135deg, #4F46E5, #7C3AED); }
         #progress-container { padding: 12px 24px; background: #fdfdfd; border-bottom: 1px solid #f0f0f0; }
-        .chat { flex: 1; background: var(--chat-bg); overflow-y: auto; padding: 25px; display: flex; flex-direction: column; gap: 16px; }
-        .msg { max-width: 75%; padding: 14px 20px; border-radius: 18px; font-size: 15px; }
-        .ai { background: white; align-self: flex-start; border: 1px solid #eef0f2; }
         .user { background: var(--primary); color: white; align-self: flex-end; }
         .input-box { display: flex; padding: 20px; gap: 12px; background: white; border-top: 1px solid #f0f0f0; }
-        .input-box textarea { flex: 1; padding: 14px; border-radius: 12px; border: 1px solid #e5e7eb; outline: none; resize: none; }
     </style>
 </head>
 <body>
@@ -69,7 +73,6 @@ HTML_CONTENT = """
         let step = 0;
         let userData = { name: "", interest: "", progress: 0 };
-        // FIX: Listen for 'Enter' key
         input.addEventListener("keypress", (e) => {
             if (e.key === "Enter" && !e.shiftKey) {
                 e.preventDefault();
@@ -83,6 +86,7 @@ HTML_CONTENT = """
             div.innerHTML = type === "ai" ? marked.parse(text) : text;
             chat.appendChild(div);
             chat.scrollTop = chat.scrollHeight;
         }
         function startApp() {
@@ -110,10 +114,7 @@ HTML_CONTENT = """
                 return;
             }
-            const aiMsgDiv = document.createElement("div");
-            aiMsgDiv.className = "msg ai";
-            aiMsgDiv.innerHTML = "<i>Analyzing...</i>";
-            chat.appendChild(aiMsgDiv);
             try {
                 const response = await fetch("/guide", {
@@ -121,29 +122,38 @@ HTML_CONTENT = """
                     headers: {"Content-Type": "application/json"},
                     body: JSON.stringify({ name: userData.name, interest: userData.interest, followup: text })
                 });
                 const reader = response.body.getReader();
                 const decoder = new TextDecoder();
                 let fullText = "";
                 let buffer = "";
                 while (true) {
                     const { done, value } = await reader.read();
                     if (done) break;
                     buffer += decoder.decode(value, { stream: true });
-                    const lines = buffer.split("\n");
                     buffer = lines.pop();
                     for (const line of lines) {
                         if (!line.trim()) continue;
-                        const data = JSON.parse(line);
-                        fullText += data.token;
-                        aiMsgDiv.innerHTML = marked.parse(fullText);
-                        chat.scrollTop = chat.scrollHeight;
                     }
                 }
-                userData.progress += 10;
                 document.getElementById("progress-bar").style.width = userData.progress + "%";
                 document.getElementById("progress-text").textContent = `Mission Progress: ${userData.progress}%`;
-            } catch (e) { aiMsgDiv.innerHTML = "Error connecting to engine."; }
         }
     </script>
 </body>
@@ -164,20 +174,17 @@ async def guide(request: Request):
     interest = data.get("interest", "Career")
     query = data.get("followup", "")
-    # Llama 3.2 1B is very picky about this format.
-    # We use the 'Instruct' tags to tell it to START talking.
-    prompt = f"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are the 1% Filter AI by Mayank. Help {user_name} with {interest}. Be short and brutal.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n{query}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
     def stream_generator():
-        # The 'stream=True' must be there
         stream = llm(
             prompt,
-            max_tokens=128,
             stream=True,
             stop=["<|eot_id|>", "User:"]
         )
         for output in stream:
-            # We look for the text inside the dictionary
             if "choices" in output:
                 token = output["choices"][0].get("text", "")
                 yield json.dumps({"token": token}) + "\n"
@@ -185,10 +192,7 @@ async def guide(request: Request):
     return StreamingResponse(
         stream_generator(),
         media_type="application/x-ndjson",
-        headers={
-            "Cache-Control": "no-cache",
-            "X-Content-Type-Options": "nosniff"
-        }
     )
 if __name__ == "__main__":

 import os
+import json
+import uvicorn
 from fastapi import FastAPI, Request
 from fastapi.responses import HTMLResponse, StreamingResponse
 from huggingface_hub import hf_hub_download
 from llama_cpp import Llama
 # --- ENGINE SETUP ---
+# Using the 1B model for maximum speed on Free Tier
 repo_id = "unsloth/Llama-3.2-1B-Instruct-GGUF"
 filename = "Llama-3.2-1B-Instruct-Q4_K_M.gguf"
+print("🚀 Mahoba Logic: Downloading/Loading Model...")
 model_path = hf_hub_download(repo_id=repo_id, filename=filename)
 llm = Llama(model_path=model_path, n_ctx=2048, n_threads=4, verbose=False)
+print("✅ Engine Ready!")
 # --- THE FRONTEND ---
 HTML_CONTENT = """
     <style>
         :root { --bg-gradient: linear-gradient(135deg, #6366F1, #7C3AED); --chat-bg: #F9FAFB; --primary: #6D28D9; --secondary: #4F46E5; }
         body { margin: 0; font-family: 'Inter', sans-serif; background: var(--bg-gradient); height: 100vh; width: 100vw; display: flex; justify-content: center; align-items: center; overflow: hidden; }
+        .welcome-screen { position: fixed; inset: 0; background: var(--bg-gradient); display: flex; justify-content: center; align-items: center; z-index: 9999; }
         .welcome-box { text-align: center; color: white; max-width: 420px; padding: 30px; }
+        .welcome-box h1 { font-weight: 800; font-size: 2.5rem; margin-bottom: 10px; }
+        .welcome-box button, .input-box button { background: white; color: var(--primary); border: none; padding: 14px 28px; border-radius: 12px; font-weight: 600; cursor: pointer; transition: 0.3s; }
+        .welcome-box button:hover { transform: scale(1.05); box-shadow: 0 10px 20px rgba(0,0,0,0.2); }
         .chat-container { width: 90%; max-width: 900px; height: 85vh; background: white; border-radius: 24px; display: flex; flex-direction: column; box-shadow: 0 25px 60px rgba(0,0,0,0.3); overflow: hidden; position: relative; }
         .header { padding: 20px; text-align: center; font-weight: 600; color: white; background: linear-gradient(135deg, #4F46E5, #7C3AED); }
         #progress-container { padding: 12px 24px; background: #fdfdfd; border-bottom: 1px solid #f0f0f0; }
+        .chat { flex: 1; background: var(--chat-bg); overflow-y: auto; padding: 25px; display: flex; flex-direction: column; gap: 16px; scroll-behavior: smooth; }
+        .msg { max-width: 75%; padding: 14px 20px; border-radius: 18px; font-size: 15px; line-height: 1.6; }
+        .ai { background: white; align-self: flex-start; border: 1px solid #eef0f2; box-shadow: 0 2px 5px rgba(0,0,0,0.05); }
         .user { background: var(--primary); color: white; align-self: flex-end; }
         .input-box { display: flex; padding: 20px; gap: 12px; background: white; border-top: 1px solid #f0f0f0; }
+        .input-box textarea { flex: 1; padding: 14px; border-radius: 12px; border: 1px solid #e5e7eb; outline: none; resize: none; font-family: inherit; }
     </style>
 </head>
 <body>
         let step = 0;
         let userData = { name: "", interest: "", progress: 0 };
         input.addEventListener("keypress", (e) => {
             if (e.key === "Enter" && !e.shiftKey) {
                 e.preventDefault();
             div.innerHTML = type === "ai" ? marked.parse(text) : text;
             chat.appendChild(div);
             chat.scrollTop = chat.scrollHeight;
+            return div;
         }
         function startApp() {
                 return;
             }
+            const aiMsgDiv = addMessage("<i>Thinking...</i>", "ai");
             try {
                 const response = await fetch("/guide", {
                     headers: {"Content-Type": "application/json"},
                     body: JSON.stringify({ name: userData.name, interest: userData.interest, followup: text })
                 });
                 const reader = response.body.getReader();
                 const decoder = new TextDecoder();
                 let fullText = "";
                 let buffer = "";
+                aiMsgDiv.innerHTML = ""; // Clear the "Thinking..."
                 while (true) {
                     const { done, value } = await reader.read();
                     if (done) break;
                     buffer += decoder.decode(value, { stream: true });
+                    const lines = buffer.split("\\n");
                     buffer = lines.pop();
                     for (const line of lines) {
                         if (!line.trim()) continue;
+                        try {
+                            const data = JSON.parse(line);
+                            fullText += data.token;
+                            aiMsgDiv.innerHTML = marked.parse(fullText);
+                            chat.scrollTop = chat.scrollHeight;
+                        } catch(err) { console.error("JSON split error"); }
                     }
                 }
+                userData.progress = Math.min(userData.progress + 15, 100);
                 document.getElementById("progress-bar").style.width = userData.progress + "%";
                 document.getElementById("progress-text").textContent = `Mission Progress: ${userData.progress}%`;
+            } catch (e) {
+                aiMsgDiv.innerHTML = "Error connecting to engine. Check logs.";
+            }
         }
     </script>
 </body>
     interest = data.get("interest", "Career")
     query = data.get("followup", "")
+    # Optimized prompt for Llama 3.2 1B
+    prompt = f"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are the 1% Filter AI by Mayank. Helping {user_name} with {interest}. Be short, brutal, and strategic. Focus on real skills, not degrees.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n{query}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
     def stream_generator():
         stream = llm(
             prompt,
+            max_tokens=150,
             stream=True,
             stop=["<|eot_id|>", "User:"]
         )
         for output in stream:
             if "choices" in output:
                 token = output["choices"][0].get("text", "")
                 yield json.dumps({"token": token}) + "\n"
     return StreamingResponse(
         stream_generator(),
         media_type="application/x-ndjson",
+        headers={"Cache-Control": "no-cache", "X-Content-Type-Options": "nosniff"}
     )
 if __name__ == "__main__":