Update app.py

app.py

@@ -95,58 +95,37 @@ if submit:
 
         st.write(f"### 📊 Your BMI: {bmi} ({bmi_status})")
 
-        equipment_list = ", ".join(equipment)
-
         # -------------------------
-        # GENERATE 5-DAY PLAN (LOOP METHOD)
+        # GENERATE 5-DAY PLAN (SINGLE PROMPT METHOD)
         # -------------------------
-        with st.spinner("Generating your 5-day workout plan..."):
-
-            from prompt_builder import build_prompt
-
-with st.spinner("Generating your 5-day workout plan..."):
-
-    prompt, bmi, bmi_status = build_prompt(
-        name=name,
-        gender="Not specified",  # or add gender field in form
-        height=height,
-        weight=weight,
-        goal=goal,
-        fitness_level=level,
-        equipment=equipment
-    )
-
-    inputs = tokenizer(prompt, return_tensors="pt", truncation=True)
+        from prompt_builder import build_prompt
 
-    outputs = model.generate(
-        **inputs,
-        max_new_tokens=800,   # increase token limit
-        temperature=0.7,
-        do_sample=True,
-        repetition_penalty=1.1
-    )
-
-    full_plan = tokenizer.decode(
-        outputs[0],
-        skip_special_tokens=True
-    ).strip()
-
-                inputs = tokenizer(prompt, return_tensors="pt", truncation=True)
-
-                outputs = model.generate(
-                    **inputs,
-                    max_new_tokens=350,
-                    temperature=0.8,
-                    do_sample=True,
-                    repetition_penalty=1.1
-                )
-
-                result = tokenizer.decode(
-                    outputs[0], 
-                    skip_special_tokens=True
-                ).strip()
+        with st.spinner("Generating your 5-day workout plan..."):
 
-                full_plan += result + "\n\n"
+            prompt, bmi, bmi_status = build_prompt(
+                name=name,
+                gender="Not specified",  # You can add gender field later
+                height=height,
+                weight=weight,
+                goal=goal,
+                fitness_level=level,
+                equipment=equipment
+            )
+
+            inputs = tokenizer(prompt, return_tensors="pt", truncation=True)
+
+            outputs = model.generate(
+                **inputs,
+                max_new_tokens=800,
+                temperature=0.7,
+                do_sample=True,
+                repetition_penalty=1.1
+            )
+
+            full_plan = tokenizer.decode(
+                outputs[0],
+                skip_special_tokens=True
+            ).strip()
 
         # -------------------------
         # DISPLAY PLAN